Model building and intelligent control method of ozone contact tank before advanced oxidation of petroleum wastewater

Abstract

The application discloses an intelligent control method for a front ozone contact tank for treating petroleum wastewater, which comprises seven steps: first, analyzing the organic component of the water inlet of the front ozone contact tank to determine the composition of each component; second, using an experimental method to simulate the reaction process to determine the reaction kinetics equation and constant of each subdivided component; third, based on the water inlet component and the reaction process, a mechanism model of the front ozone contact tank is established; fourth, a fluid mechanics three-dimensional model conforming to the actual structure is established by using COMSOL software, the mechanism model is coupled with the fluid model, and process modeling is completed; fifth, parameter correction is carried out through steady-state simulation and sensitivity analysis; and sixth, self-learning and self-correction of the model are realized by using a neural network algorithm, the ozone dosage is dynamically optimized according to the water inlet change, and thus an optimal control strategy is output. The application realizes optimal ozone dosage control, improves the wastewater treatment efficiency, and reduces energy consumption and reagent use.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology for wastewater treatment, specifically relating to a method for building a model of an ozone contact tank before advanced oxidation of petroleum wastewater. Background Technology

[0002] In the wastewater treatment industry, with increasingly stringent environmental protection standards, the operational efficiency, effluent quality, and energy conservation and emission reduction capabilities of wastewater treatment plants have become core issues of great concern. Among these, the pre-ozone contact tank, as a crucial link in the treatment process, effectively decomposes stubborn organic matter, optimizes sludge settling performance, and significantly reduces the burden on subsequent treatment processes thanks to ozone's superior oxidation capabilities. However, current wastewater treatment facilities still face many challenges in operation management and control, particularly in the insufficient level of automation and intelligence. Traditional PID control strategies are no longer sufficient to meet the demands for high efficiency and precision, and their limitations are becoming increasingly apparent.

[0003] A Chinese patent, "A Pre-Ozone Dosing Control Method and Control System" (202011582288.X), provides a solution that uses real-time monitoring of multiple water quality parameters, model prediction of pre-ozone dosage, and correction based on effluent turbidity and COD values ​​to obtain a precise ozone dosage, which is then added to the pre-ozone contact tank accordingly. However, research has revealed the following shortcomings: First, the patent's intelligent control method adjusts operating conditions based on real-time monitoring of COD, influent turbidity, and influent flow rate parameters, a typical feedback control mechanism. However, this control method suffers from time delays, making it difficult to respond quickly to drastic changes in water quality and failing to mitigate the risks associated with such changes. Furthermore, this method leads to significant control fluctuations, affecting the stability of the biological environment of the wastewater treatment system and interfering with the normal operation of the biological treatment process. Second, the control accuracy is limited. While the patent's intelligent control method combines real-time monitoring technology and model prediction, using effluent turbidity and COD values ​​to correct ozone dosage for precise control, it still suffers from limitations. However, online monitoring technology is affected by various factors such as instrument accuracy and environmental interference, leading to errors in the monitoring data. These errors further affect the prediction and correction of ozone dosage, thus reducing the overall treatment effect. Simultaneously, the model itself has certain limitations in adaptability, failing to provide accurate control output for conditions with significant water quality fluctuations. Thirdly, in wastewater treatment, multiple water quality parameters, such as COD, BOD, ammonia nitrogen, and pH, interact and require coordinated control to ensure overall water quality meets standards. However, the patented intelligent control method primarily focuses on setting two or three parameters, resulting in imbalances in other parameters. Furthermore, the relationships between these parameters are often non-linear, and the patented control method is insufficient in handling these non-linear relationships, failing to provide accurate and comprehensive control output. Therefore, in practical applications, further improvements and optimizations to the control strategy are needed to better adapt to complex and variable wastewater treatment conditions.

[0004] To address this challenge, this invention proposes a model building and intelligent control method for an advanced oxidation pre-ozone contact tank for petroleum wastewater. This model not only optimizes process parameters and control signals but also supports simulation experiments, accurately diagnosing and resolving design and operational challenges, and proposing optimization strategies. Through integrated adaptive algorithms, this mathematical model control system can self-learn and self-optimize, flexibly adapting to various operating conditions and accurately calculating the optimal ozone dosage, achieving efficient resource utilization. Furthermore, based on detailed experimental data, we established kinetic equations and parametric models for each sub-component. These models accurately describe the behavior of each component, constructing a more realistic mechanism model. This model not only accurately simulates actual operating conditions but also provides scientific guidance for water plant operation. Simultaneously, combined with computational fluid dynamics (CFD) modeling technology, the technical solution described in this invention can deeply simulate and analyze the flow and reaction states of each phase within the structure, further improving simulation accuracy. By coupling the two models, a comprehensive process model is constructed, which can accurately capture the reaction mechanism and dynamic changes of each phase in the pre-ozone contact tank. Building upon this foundation, we introduced artificial neural network autonomous learning technology to achieve feedforward control, intelligent adjustment, and self-optimization of ozone dosage. This provided precise guidance for production operations and maximized the potential of existing water treatment plant processes and equipment. Ultimately, by precisely controlling the ozone dosage, we achieved optimal resource allocation and minimized flow while ensuring efficient removal of chemical oxygen demand (COD) and improving the biodegradability of effluent. This made a significant contribution to the sustainable development of the wastewater treatment industry. Summary of the Invention

[0005] (1) Purpose of the invention

[0006] To address the challenges posed by the aforementioned feedback control mechanisms, such as time delay, insufficient control fluctuations and stability in the biological environment, large monitoring accuracy and environmental interference errors, limited model adaptability, inadequate multi-parameter collaborative control, and insufficient ability to handle nonlinear relationships, this invention proposes a model building and intelligent control method for constructing an ozone contact tank before advanced oxidation of petroleum wastewater.

[0007] In summary, the core technical problem of this invention is to achieve precise control and optimal optimization of the ozone dosing process through refined influent analysis, accurate degradation process modeling, coupled simulation of fluid mechanics and chemical reactions, and adaptive optimization based on intelligent algorithms, thereby improving the efficiency and effectiveness of wastewater treatment.

[0008] The present invention describes a model building and intelligent control method for an ozone contact tank before advanced oxidation of petroleum wastewater. The core technical problems to be solved are mainly in four aspects: refined analysis and classification of influent components, experimental simulation and reaction mechanism of ozone degradation process, coupling of mechanism model and fluid dynamics model, and optimization and intelligent control of model.

[0009] Firstly, regarding the refined analysis and classification of influent components, the technical solution described in this invention mainly addresses how to accurately measure and subdivide various pollutant components in the influent, such as COD, nitrogen, phosphorus, and petroleum, and classify them into different types, such as particulate or dissolved, biodegradable or non-biodegradable, to provide detailed water quality data for subsequent treatment and control.

[0010] Secondly, regarding the experimental simulation and reaction mechanism of the ozone degradation process, the technical solution described in this invention mainly addresses how to obtain the concentration changes, chemical structure changes, and degradation pathways of each sub-component in the ozone contact tank through experimental simulation, and to provide the model with accurate reaction kinetic equations and parameters to ensure that the model can accurately describe the actual reaction process.

[0011] Thirdly, regarding the coupling of the mechanistic model and the fluid dynamics model, the technical solution described in this invention mainly addresses how to combine the reaction mechanism model with the fluid dynamics model (CFD model) to simulate the mass transfer process, reaction process, and flow regime of ozone bubbles during water treatment, ensuring a comprehensive reflection of the attenuation and mass transfer behavior of ozone in water.

[0012] Fourth, regarding model optimization and intelligent control, the technical solution described in this invention mainly addresses how to self-correct and update the coupled model through steady-state simulation, sensitivity analysis, and parameter optimization, and how to use adaptive neural networks for optimization to dynamically adapt to different water quality conditions, formulate the optimal ozone dosing control strategy, and achieve intelligent optimization and automatic adjustment of the system.

[0013] Furthermore, the model building and intelligent control method for an ozone contact tank before advanced oxidation of petroleum wastewater described in this invention aims to optimize the ozone dosing process by combining physical modeling, CFD simulation, and artificial intelligence technologies, achieving efficient, energy-saving, and intelligent wastewater treatment, and providing strong technical support for the optimization and sustainable development of wastewater treatment facilities. The technical solution described in this invention aims to provide a new wastewater treatment method for petroleum wastewater treatment, capable of establishing an accurate petroleum wastewater treatment model with adaptive algorithms and self-learning capabilities. Utilizing CFD modeling and reaction state analysis, coupled with model and neural network optimization, and employing kinetic equations and parameter models of different water quality components, it achieves precise ozone dosing control, thereby improving wastewater treatment efficiency and reducing energy consumption and resource utilization. (2) Summary of the Invention

[0015] The present invention discloses a method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater, comprising the following steps:

[0016] S1. Analysis of influent components: Analysis of the components of each pollutant in the influent of the ozone contact tank and their initial concentrations;

[0017] S1.1 Sampling and Detection: Collect raw petroleum wastewater samples from sampling points and use different experimental detection methods to determine different pollutants in the influent;

[0018] S1.11 Determine the sampling method: Collect uniformly mixed wastewater samples from representative sampling points, and determine an appropriate sampling volume according to the testing requirements to ensure that the samples can accurately reflect the influent water quality;

[0019] S1.12 Sample Preservation: By employing specialized preservation measures for different components, we ensure that the collected samples remain stable in composition during analysis, unaffected by microbial activity or chemical reactions.

[0020] S1.2 The influent components are divided into four categories: COD components, nitrogen components, phosphorus components and petroleum components;

[0021] S1.21 COD Component Analysis: By measuring total COD, particulate and dissolved COD, biodegradable and non-biodegradable COD, and types of organic matter, the organic pollutant load and degradation characteristics in wastewater are assessed.

[0022] S1.211 Total COD determination: The total COD concentration is determined using the potassium dichromate method or spectrophotometry to provide overall data on organic load;

[0023] S1.212 Separation of particulate and dissolved COD: Dissolved and particulate organic matter in the sample are separated using a 0.45μm filter membrane, and their COD is measured separately to distinguish components with different biodegradability.

[0024] S1.213 Differentiation between biodegradable and non-biodegradable COD: By comparing the COD values ​​of samples that have not been treated with aerobic microbial degradation, the proportion of easily degradable and difficult-to-degrade organic matter in wastewater is quantitatively analyzed.

[0025] S1.214 Organic matter type analysis: The main organic matter in the wastewater is separated, qualitatively and quantitatively analyzed by gas chromatography-mass spectrometry to determine the type and concentration of each component;

[0026] S1.22 Nitrogen Component Analysis: By measuring the concentrations of total nitrogen, ammonia nitrogen, nitrate nitrogen, and nitrite nitrogen in wastewater, the distribution of nitrogen speciation is analyzed to assess the nitrogen pollution level, nitrogen conversion process, and the need for subsequent denitrification treatment.

[0027] S1.221 Total Nitrogen Determination: The total nitrogen content in wastewater is determined by using a total nitrogen analyzer or ultraviolet spectrophotometry to assess the overall level of nitrogen pollution and its potential impact on eutrophication of water bodies.

[0028] S1.222 Ammonia nitrogen analysis: The concentration of ammonia nitrogen in wastewater was determined by Nessler's reagent colorimetric method or ion-selective electrode method;

[0029] S1.223 Nitrate and Nitrite Nitrogen Analysis: Quantitative determination of nitrate nitrogen and nitrite nitrogen in wastewater by ion chromatography or spectrophotometry;

[0030] S1.224 Nitrogen Component Distribution: By calculating the proportion of each nitrogen form in the total nitrogen and drawing a nitrogen distribution map, the source, transformation and removal difficulty of different nitrogen forms in wastewater are clarified;

[0031] S1.23 Phosphorus Component Analysis: The phosphorus pollution level in wastewater is assessed by measuring the concentrations of total phosphorus, orthophosphate, particulate and dissolved phosphorus, and organic phosphorus.

[0032] S1.231 Determination of total phosphorus (TP): The total phosphorus content is determined using the molybdenum blue method or a total phosphorus analyzer;

[0033] S1.232 Orthophosphate Analysis: Orthophosphate concentration was determined by ammonium molybdate method or ion chromatography;

[0034] S1.233 Separation of particulate and dissolved phosphorus: The sample was filtered using a 0.45 μm filter membrane, and the dissolved phosphorus in the filtrate and the particulate phosphorus in the filter residue were measured.

[0035] S1.234 Organic phosphorus analysis: Organic phosphorus is converted into inorganic phosphorus using acid hydrolysis or heating digestion methods, and then the concentration is determined;

[0036] S1.24 Petroleum Component Analysis: By measuring the concentration and separation of total petroleum substances, specific components and their ozone requirements are analyzed;

[0037] S1.241 Determination of total petroleum substances: The total concentration of petroleum substances in wastewater shall be determined by infrared photometry or ultraviolet spectrophotometry;

[0038] S1.242 Component Separation and Qualitative Analysis: Petroleum-based substances are enriched by liquid-liquid extraction and their specific components are analyzed by gas chromatography or GC-MS.

[0039] S1.243 Assessment of the ozone demand of petroleum products: Calculate the ozone demand of petroleum products based on their chemical structure and content.

[0040] S1.3 Microbial component test: Analysis of microbial communities such as autotrophic bacteria and aerobic bacteria;

[0041] S1.31 Total bacterial concentration determination: The total concentration of microorganisms in the sample is determined by microscopic counting or flow cytometry;

[0042] S1.32 Population composition analysis: Microbial population composition, including autotrophic and heterotrophic bacteria, was analyzed using 16S rRNA gene sequencing or fluorescence in situ hybridization (FISH) technology.

[0043] S1.33 Activity Assay: The bioactivity of microorganisms is assessed using an activity staining method.

[0044] S1.34 Bioload Assessment: Calculate the bioload level in wastewater based on microbial concentration and population structure;

[0045] S1.4 Determine the final influent component list: Based on the experimental measurement results, confirm the specific influent components and the initial concentration levels of various pollutants in the pre-ozone contact tank;

[0046] S1.41 Summary of test results: Compile the test results of COD, nitrogen, phosphorus, microorganisms and petroleum components, and construct a comprehensive spectrum of influent components;

[0047] S1.42 Component Correlation Analysis: This involves analyzing the relationships between different pollutants.

[0048] S1.43 Preliminary assessment of water quality characteristics: Based on the component data, assess the main pollution characteristics of the wastewater.

[0049] S2. Experimental simulation of the reaction process in the pre-ozone contact tank: The reaction process of each sub-substance in the pre-ozone contact tank is simulated by experimental methods to determine the concentration changes, chemical structure changes and corresponding reaction kinetic equations and constants of each sub-component;

[0050] S2.1 Construction of small-scale test device: Customize a small-scale pre-ozone contact pool test device according to CAD drawings, and then arrange ozone dosing in the small contact pool of the laboratory according to the ozone controller dosing method at the sampling location.

[0051] S2.2 Small-scale test water configuration: Based on the components and concentrations in the petroleum wastewater, water is allocated according to the actual concentration range to ensure that the composition of the influent to the small pre-ozone contact tank matches the actual working conditions as closely as possible.

[0052] S2.21 Preparation of influent sample: Based on the sampling results and actual wastewater characteristics, prepare simulated influent according to the proportion;

[0053] S2.22 Ozone generation system configuration: Install the ozone generator and calibrate its output concentration and flow rate, and determine the ozone dosing method and dosage range;

[0054] S2.23 Monitoring Equipment Preparation: Configure online monitoring instruments and laboratory equipment, including COD analyzer, ammonia nitrogen, nitrate nitrogen, total phosphorus analyzer, GC-MS gas chromatography-mass spectrometry system, pH meter and dissolved oxygen meter, for detecting changes in chemical structure and concentration changes of each component;

[0055] S2.3 Determine the reaction process of sub-substances in the pre-ozone contact tank: In a small-scale experimental device, measure the concentration changes and chemical structure changes of each sub-component under the action of ozone, analyze its degradation pathway, and determine the reaction kinetic equations and constants of each sub-substance;

[0056] S2.31 Experimental Operation: By setting initial conditions, starting the reaction, and taking samples at different time intervals, the ozone contact process under actual working conditions is simulated to monitor pollutant degradation and changes in related parameters;

[0057] S2.311 Initial conditions setting: Adjust the water volume, inlet flow rate, and ozone gas flow rate in the experimental setup;

[0058] S2.312 Reaction Start-up: Introduce the prepared simulated wastewater into the small ozone contact tank, start the circulation system, and then add ozone gas. Gradually increase the dosage according to the concentration and time designed in the experiment, and continuously record the operating parameters.

[0059] S2.313 Time-segmented sampling: Sampling is performed in the contact pool at predetermined time intervals, and changes in temperature, pH, and ozone concentration are recorded;

[0060] S2.32 Detailed Component Reaction Analysis: By measuring the changes in the concentration and chemical structure of each pollutant, and analyzing the effects of ozone concentration, gas-liquid mixing, temperature and pH on reaction efficiency, the degradation mechanism of each component is sought and the optimal operating conditions are determined.

[0061] S2.321 Pollutant Concentration Change Measurement: By sampling at regular intervals and using online and laboratory analysis methods, the particulate and dissolved COD in wastewater, as well as the concentration and chemical structure changes of various types of nitrogen, phosphorus, and petroleum substances before and after degradation, were detected.

[0062] S2.322 Chemical Structure Change Analysis: GC-MS was used to detect structural changes in petroleum compounds before and after degradation, and to identify intermediate products such as lower alkanes or small molecule aromatic compounds.

[0063] S2.323 Identify key influencing factors: Analyze the effects of ozone concentration, gas-liquid mixing effect, temperature, and pH on the degradation efficiency of each component, and then determine the optimal operating conditions for the reaction;

[0064] S2.33 Kinetic Parameter Extraction: The reaction kinetic model is fitted using experimental data to extract reaction kinetic parameters in order to quantitatively describe the reaction process of each pollutant component;

[0065] S2.331 Kinetic Equation Fitting: The kinetic models of each component are fitted using experimental data, and key parameters are extracted to quantitatively describe the reaction process of each pollutant component.

[0066] The first-order reaction kinetics are as follows:

[0067]

[0068] Where (C) or [C] represents the concentration of a component, such as resin monomer or reactant, and k is the reaction rate constant; the negative sign "-" indicates that the concentration of reactant decreases over time;

[0069] S2.332 Mass Transfer Parameter Calculation: Based on the two-film theory, the gas-liquid mass transfer rate constant and bubble specific surface area of ​​ozone between the gas and liquid phases are calculated to analyze the gas-liquid mass transfer limiting conditions.

[0070] Calculate the ozone mass transfer rate constant K. L And the specific surface area of ​​the bubbles α:

[0071] J = K L α·([O3] gas -[O3] liquid )

[0072] Where J is the mass transfer rate flux of ozone (O3); K L It is the mass transfer coefficient of the liquid phase, [O3] gas and [[O3]] liquid These represent the concentrations of ozone in the gas and liquid phases, respectively.

[0073] S2.333 Data Validation and Adjustment: Compare and correct the fitted kinetic equations and parameters with the experimental data to ensure that the model can accurately reflect the actual reaction process. If necessary, readjust the model parameters.

[0074] S2.34 Experimental Data Analysis and Optimization: By analyzing the degradation efficiency, ozone utilization rate and degradation mechanism of each pollutant at different time points, and adjusting the experimental parameters based on data feedback, the best removal effect can be obtained.

[0075] S2.341 Degradation Efficiency Analysis: The degradation efficiency of each pollutant at different time points was calculated. The degradation efficiency formula is as follows:

[0076]

[0077] Where Co represents the initial concentration of pollutants at the start of treatment, i.e., the initial concentration; Ct represents the concentration of pollutants at any time during the treatment process; by calculating the reduction in pollutant concentration (Co-Ct) and then comparing it with the initial concentration Co, the degradation efficiency is obtained and expressed as a percentage.

[0078] S2.342 Ozone Utilization Efficiency Analysis: Calculate the effect of unit ozone dosage on pollutant removal and evaluate ozone utilization rate;

[0079] S2.343 Degradation Mechanism Summary: By integrating the concentrations of various pollutants and the chemical structural changes of intermediate products, the degradation pathways of different components from partial oxidation to final mineralization are summarized.

[0080] S2.344 Optimize experimental conditions: Based on data feedback, adjust key parameters and conduct repeated experiments to find the optimal reaction conditions.

[0081] S3. Establish a mechanism model for the pre-ozone contact tank: Based on the mechanism models established in S1. and S2., a multi-component degradation reaction model for the pre-ozone contact tank is formed, taking the influent components and their reaction kinetics and the reaction process of the substances as inputs.

[0082] S3.1 Model Input Components: The various components and their initial concentrations determined in S1. and S2. are used as model input parameters;

[0083] S3.11 Collect experimental data: Under strictly controlled sampling conditions, collect the concentrations of organic matter, nutrients, and petroleum pollutants in the influent, as well as the corresponding reaction kinetic parameters;

[0084] S3.12 Determine the reaction type: Identify the main chemical reactions and physical processes in the pre-ozone contact tank, and construct a kinetic model to describe their coupling effects;

[0085] S3.13 Selecting Modeling Tools: Select a suitable modeling tool for writing and solving equations;

[0086] S3.2 Kinetic Equations and Constants: The reaction kinetic equations and reaction kinetic constants obtained in S2. are incorporated into the model, and degradation equations are established for each type of substance;

[0087] S3.21 Classification and Development of Rate Equations: Establish specific degradation equations for different types of pollutants;

[0088] S3.211 COD Degradation: Based on the first-order reaction kinetics of ozone oxidation, the COD degradation rate equation is established:

[0089]

[0090] Where [C] represents COD concentration; k COD [O3] represents the reaction rate constant between COD and ozone; [O3] represents the ozone concentration. It is a dynamic quantity, representing the rate of change of C (COD concentration) per unit time;

[0091] S3.212 Reaction of nitrogen components: The rate equation describing the reaction of ammonia nitrogen with ozone is used to calculate the oxidative degradation process of ammonia nitrogen;

[0092] First, the ammonia nitrogen oxidation equation:

[0093]

[0094] in, is the rate constant for the reaction of ammonia nitrogen with ozone;

[0095] Secondly, the dynamic change equations for nitrate and nitrite nitrogen: During the reaction, ammonia nitrogen is oxidized to nitrite. Further oxidation to nitrate The concentrations of nitrite and nitrate will exhibit dynamic changes over time, initially increasing and then decreasing, or continuing to increase.

[0096] First, the formation and consumption of nitrite are as follows:

[0097]

[0098] in, Describe the rate of the reaction from ammonia nitrogen to nitrite; the first positive term. The term represents the rate of nitrite formation—that is, the process of ammonia nitrogen oxidation to nitrite; the second negative term. This indicates the rate at which nitrite is further oxidized to nitrate by ozone;

[0099] Next, nitrate is formed as follows:

[0100]

[0101] in, Describe the rate of the reaction from nitrite to nitrate; there is only one positive term, representing the rate at which the nitrate concentration increases with time; under the following assumptions, It is no longer further oxidized or consumed;

[0102] S3.213 Changes in phosphorus composition: Considering the direct reaction between phosphate and ozone, the rate expression for the reaction equation between phosphate and ozone is:

[0103]

[0104] in, This is the rate constant for the reaction of phosphate with ozone; the negative sign "-" indicates that the phosphate is consumed during the oxidation process.

[0105] S3.214 Oxidation of petroleum components: Describes a kinetic model of the reaction between petroleum pollutants and ozone. This model is based on ozone oxidation, and the reaction rate is proportional to the product of the concentration of petroleum pollutants and the concentration of ozone.

[0106] The decomposition rate equations for alkanes, cycloalkanes, and aromatic hydrocarbons are established as follows:

[0107]

[0108] in, This indicates the concentration of petroleum pollutants [P]. oil The rate of change of [P] over time; oil [] represents the concentration of petroleum pollutants; [O3] represents the concentration of ozone; k R This represents the rate constant for the reaction between petroleum products and ozone.

[0109] S3.22 Mass transfer and reaction combined: The mass transfer process of ozone from the gas phase to the liquid phase is coupled with the chemical reaction rate in the liquid phase to reveal and predict how the overall reaction rate is affected by the concentration difference at the gas-liquid interface and the mass transfer coefficient.

[0110] The mass transfer rate equation is as follows:

[0111] J = K L α·A·([O3] gas -[O3] liquid )

[0112] Where J represents the mass transfer flux of ozone at the gas-liquid interface; K L α represents the liquid-phase mass transfer coefficient; α represents the gas solubility coefficient; A is the effective specific surface area within the reactor, i.e., the total area of ​​the gas-liquid interface; [O3] gas [O3] represents the concentration of ozone in the liquid phase at equilibrium. liquid This represents the actual ozone concentration in the liquid phase.

[0113] S3.3 Integrated Mechanism Model: The reaction equations of each component obtained from S3.1 and S3.2 are assembled and coupled to form a multi-component mechanism model of the pre-ozone contact tank as a whole;

[0114] S3.31 Determine the equation structure: Summarize all rate equations and establish a total rate equation system containing multiple components;

[0115] S3.32 Simultaneous Equations: The rate equation and mass transfer equation are integrated into a system of simultaneous equations using software. The system of equations is solved using finite element analysis or matrix calculation tools. At the same time, the coupling effect of mass transfer and reaction in the spatiotemporal distribution is described. The system of equations is solved using finite element analysis or matrix calculation tools.

[0116] S3.33 Set initial conditions: Input the initial concentration value obtained from the experiment and set the initial ozone concentration;

[0117] S3.41 Input experimental parameters: Input the kinetic constants, mass transfer coefficients, and reaction rate constants obtained in the experiment into the model one by one;

[0118] S3.42 Set boundary conditions: Input boundary conditions in the pool model;

[0119] S3.43 Setting the time step and simulation range: Based on the experimental reaction time range, set the simulation time step and total simulation time;

[0120] S3.4 Parameter Input: By importing the kinetic constants, mass transfer coefficients, initial concentrations, and setting boundary conditions, time steps, and total simulation time obtained from experiments, a parameter library that truly reflects the actual working conditions is constructed.

[0121] S3.41 Input experimental parameters: Input the kinetic constants, mass transfer coefficients, and reaction rate constants obtained in the experiment into the model one by one;

[0122] S3.42 Set boundary conditions: By inputting ozone gas concentration and influent parameters consistent with the experimental conditions into the pool model, the model can truly reflect the actual operation process and provide necessary initial and boundary data for subsequent solutions;

[0123] S3.43 Setting the time step and simulation range: By selecting an appropriate time step and total simulation time, the dynamic changes in the reaction process can be accurately captured;

[0124] S3.5 Solving the Mechanism Model: By selecting an appropriate solver, combining the mass transfer and reaction equations, running the simulation, and verifying the consistency between the model results and experimental data;

[0125] S3.51 Choosing a solution method: By selecting an appropriate time-dependent or steady-state solver, the mass transfer and reaction kinetic equations are combined to construct a complete coupled model;

[0126] S3.52 Simulation: Numerical integration calculations are performed by inputting parameters, initial conditions, and boundary conditions to observe the dynamic trends of the concentration of different components over time.

[0127] S3.53 Check the model results: Verify whether the model output meets expectations by comparing the simulation results with the experimental data;

[0128] S3.6 Result Analysis and Correction: By comparing the simulation results with the experimental data, the model parameters are adjusted to improve the simulation accuracy, and finally a reliable mechanism model is generated;

[0129] S3.61 Results Output and Visualization: Outputs pollutant concentration change curves, degradation rate charts, and visualizes the distribution of ozone in the pool;

[0130] S3.62 Correcting Model Parameters: When there is a deviation between the model results and experimental data, adjust the kinetic constants, mass transfer parameters or initial conditions, and rerun the model;

[0131] S3.63 Generate the final mechanism model: Based on the corrected parameters, save the final model.

[0132] S4. CAD drawing modeling and CFD three-dimensional modeling: Based on the CAD drawings, a three-dimensional geometric model of the pre-ozone contact tank is established, and a fluid dynamics model is constructed in CFD software, including flow field establishment, turbulence model selection, boundary condition setting, and numerical simulation of gas-liquid mass transfer and ozone decay reaction.

[0133] S4.1 Three-dimensional geometric model construction: Based on the CAD drawing of the pre-ozone contact tank, a complete three-dimensional geometric model and computational domain are established in COMSOL to accurately reflect the actual structure and key areas;

[0134] S4.11 Data Collection: Obtain CAD drawings of the pre-ozone contact tank and physicochemical parameters of the wastewater;

[0135] S4.12 Software Tool Selection: Select suitable modeling and fluid dynamics simulation software based on project requirements;

[0136] S4.2 Physical field selection: Based on the Reynolds number Re being greater than the critical value, the Standard k-ε turbulence model is selected to simulate jet diffusion, rotating flow, and separated flow in the flow field;

[0137] S4.21 Establishing a 3D geometric model: Based on CAD drawings, construct a 3D model in modeling software that contains only the key pool structure;

[0138] S4.22 Define the calculation domain: Define the calculation domain as the internal area of ​​the front ozone contact tank;

[0139] S4.3 Boundary Condition Setting: Set specific fluid boundary conditions for each key region of the front ozone contact tank in the numerical simulation, and define the convergence criteria;

[0140] S4.31 Pool Material Settings: Define the pool and set its physical properties;

[0141] S4.32 Liquid and Gas Parameter Settings: Set key physical parameters for wastewater and air-ozone mixtures respectively;

[0142] S4.4 Pre-ozone contact tank flow reaction simulation: A numerical model based on the two-film theory and first-order reaction kinetics is constructed to evaluate and optimize the wastewater treatment effect by simulating the mass transfer, decay and oxidation reaction process of ozone and organic matter in wastewater in the gas-liquid two phases.

[0143] S4.41 Selecting a turbulence model: Determine the flow regime based on the fluid Reynolds number and use the standard k-ε turbulence model to simulate complex turbulence phenomena;

[0144] S4.42 Define boundary conditions: Set various boundary conditions for the model, including inlet, free surface, no-slip wall and outlet;

[0145] S4.5 Gas-Liquid Two-Phase Flow Model Selection and Parameter Settings: By selecting a multiphase flow model, reasonably configuring the drag and lift models, and customizing the ozone mass transfer and decay functions;

[0146] S4.51 Selecting the gas-liquid model: By adopting a multiphase flow model and setting the mass transfer parameters of ozone, the mass exchange between bubbles and liquid in the reactor and the transfer process of ozone from the gas phase to the liquid phase are accurately simulated.

[0147] S4.52 Force Setting: By defining gas-liquid interaction forces, including drag force, virtual mass force, lift force and surface tension, the interaction forces between bubbles and liquids can be accurately simulated, thereby simulating bubble motion trajectory, size change and mass transfer process;

[0148] S4.53 Mass transfer and decay model: By employing the two-film theory and first-order reaction kinetics, the mass transfer process and decay reaction of ozone in water are accurately simulated;

[0149] S4.6 Mesh Generation and Refinement: By meshing the 3D geometric model and refining the mesh in key areas, a balance between computational accuracy and computational cost in the numerical simulation is ensured.

[0150] S4.61 Mesh Generation: The computational domain is meshed, and the mesh is refined in key areas;

[0151] S4.62 Solver Selection: Select the static segregated solver, set weak constraints, and input initial values;

[0152] S4.63 Convergence Check: By monitoring the stability of variable residuals, flow rate, and mass, determine whether the numerical simulation has converged and obtained accurate results;

[0153] S4.7 Solution and Flow Optimization: Solve the turbulence equations using a static separation solver, and optimize the structural dimensions or flow velocity based on the flow distribution analysis;

[0154] S4.71 Simulation Result Analysis: By extracting the flow pattern distribution, ozone diffusion path, and concentration distribution, we checked for dead zones, short-circuiting phenomena, and areas of uneven flow velocity.

[0155] S4.72 Geometric Adjustment: Solve dead zone or short-circuit phenomena by optimizing the system geometry;

[0156] S4.73 Optimize gas-liquid mixing: Optimize the mass transfer efficiency at the gas-liquid interface by adjusting the inlet flow rate, bubble size, and ozone component ratio;

[0157] S4.8 Subsequent Reaction Kinetics Optimization: After optimization through flow simulation, the reaction kinetic parameters are further optimized according to the process flow of the upstream ozone contact tank to ensure that the system meets the expected effluent water quality standards.

[0158] S4.81 Optimization based on reaction kinetics: By combining the simulation results of mass transfer and decay processes, the ozone dosage and bubble residence time are adjusted to optimize the reactor operating parameters;

[0159] S4.82 Generate design parameters: Determine the tank structure dimensions, equipment installation depth, inlet and outlet water flow velocities, and operating conditions;

[0160] S4.83 Generate Model Report: Summarizes the output 3D model, mesh generation scheme, flow distribution map, and key parameters to generate a model report.

[0161] S5. Coupling of Mechanism Model and Fluid Dynamics Model: The mechanism model described in S3. is coupled with the CFD fluid dynamics model described in S4. to form a comprehensive pre-ozone contact tank simulation model that simultaneously reflects the hydraulic flow state and chemical reaction mechanism;

[0162] S5.1 Dilute Substance Transport Module Setup: Set up dilute substance transport modules for each component in the CFD model, define the transport properties of each component, and set the reaction domain condition interface.

[0163] S5.11 Determine the content of the mechanism model: collect reaction kinetic data and confirm the degradation pathway of pollutants;

[0164] S5.12 Prepare the fluid dynamics (CFD) model: When the CFD model is completed, accurately calculate the flow distribution, gas-liquid mixing and mass transfer in the pool to ensure that the mesh is suitable for the reaction calculation;

[0165] S5.2 Coupled Reaction Rates: The mechanism model is coupled in CFD by defining and incorporating the reaction rate equations of each component into the local variables of the CFD model, and incorporating the reaction rate as a source term into the CFD solver.

[0166] S5.21 Defines mass transfer properties: By setting the liquid flow velocity field and direction in the CFD model, the convective transfer, molecular diffusion and turbulent diffusion behavior of pollutants in the liquid are defined, and the turbulent mixing coefficient is coupled through the k-ε turbulence model to further couple the convection term, diffusion term and turbulent mixing term to simulate the pollutant transport process;

[0167] S5.22 Initialize substance concentration: Set the initial concentration value of pollutant components based on experimental or monitoring data;

[0168] S5.3 Forming a complete process model: By combining hydraulic flow calculations and chemical reaction mechanisms, the reaction zone is defined, reaction kinetic equations are imported, and reaction rate parameters are set to establish a comprehensive pre-ozone contact tank simulation model;

[0169] S5.31 Define the reaction zone: Determine the extent of the liquid phase region within the tank and set the reaction zone, then define the reaction zone in the CFD model;

[0170] S5.32 Importing Reaction Kinetic Equations: Define reaction kinetic equations for COD, ammonia nitrogen, and petroleum components based on the reaction mechanism of pollutants, and input the reaction rate constants and related parameters;

[0171] S5.321 COD Degradation Reaction Kinetic Equation: Add a first-order reaction kinetic equation to define the reaction rate between ozone and organic matter;

[0172] The reaction rate equation for the COD oxidation reaction is defined as follows:

[0173] r COD =k COD ·[COD]

[0174] Where, k COD Let be the oxidation rate constant of ozone for [COD];

[0175] S5.322 Ammonia nitrogen (NH3) oxidation reaction kinetic equation: A reaction kinetic equation needs to be defined, and the oxidation of ammonia nitrogen by ozone is the key reaction;

[0176] The rate equation for the ammonia nitrogen oxidation reaction is:

[0177]

[0178] in, is the oxidation rate constant of ammonia nitrogen by ozone;

[0179] S5.323 Kinetic equation for the degradation of petroleum components: Based on experimental results, a kinetic equation for the degradation is defined. Assuming a second-order reaction occurs between ozone and the pH of petroleum hydrocarbons, meaning the reaction rate is affected by the concentrations of both, the following kinetic equation, fitted to experimental data, describes the degradation process of petroleum components:

[0180]

[0181] Where [PH] represents the concentration of petroleum hydrocarbons or petroleum components; [O3] represents the ozone concentration; k PH It is the reaction rate constant, and its value needs to be determined through experimental data;

[0182] If the ozone concentration remains relatively stable or in an excess state, the equation can be simplified to a pseudo-first-order kinetic model:

[0183]

[0184] Where, k obs =k PH [o3];

[0185] S5.33 Set reaction rate parameters: Input the oxidation rate constant of ozone for COD and ammonia nitrogen based on experimental data, and set the mass transfer coefficient according to the gas-liquid mass transfer characteristics of ozone, and adjust other mass transfer coefficients to adapt to the mass transfer behavior of different pollutants.

[0186] S5.4 Coupled Mass Transfer and Reaction Process: By applying the two-film theory to define the mass transfer rate equation, setting various mass transfer conditions, and coupling the mass transfer process with the reaction rate equation, the mass conservation equation is finally solved in the CFD model.

[0187] S5.41 Apply the two-film theory to define the mass transfer rate equation: determine the values ​​of the overall mass transfer coefficient K and the bubble specific surface area a, apply the mass transfer rate equation as the boundary condition to the CFD model, and calculate and update the ozone concentration in the gas and liquid phases in real time.

[0188] The mass transfer rate equation is defined using the two-film theory as follows:

[0189] J = K·a··(C gas -C liquid )

[0190] Where K represents the overall mass transfer coefficient; a represents the specific surface area of ​​the bubble; C gas and C liquid This indicates the ozone concentration in the gas and liquid phases;

[0191] S5.42 Set mass transfer conditions: Determine bubble size, gas flow rate and liquid phase turbulent mixing intensity, and set each mass transfer condition in the CFD model. At the same time, couple the mass transfer rate equation with the reaction rate equation for calculation.

[0192] S5.43 Coupled Mass Transport and Reaction: By constructing a mass conservation equation in a CFD model, the diffusion term, turbulent mixing term and reaction rate are integrated, the diffusion coefficient and turbulent mixing coefficient are adjusted, and the results are compared with experimental data;

[0193] S5.5 Solution Model: The coupled mass transfer and reaction processes are solved using numerical methods, and the changes in pollutant concentration and flow pattern distribution are dynamically calculated.

[0194] S5.51 Setting up the Coupled Solver: Select the multiphysics coupled solver and set the calculation accuracy;

[0195] S5.52 Initialization Calculation: Input all boundary conditions, then define the convergence criteria;

[0196] S5.53 Simulation: Start the solver to perform calculations, monitor the convergence of the physical field, adjust the time step or refine the mesh to improve accuracy, and analyze the changes in flow field and pollutant concentration through post-processing results. At the same time, simulate the flow distribution and pollutant reaction process, and dynamically calculate the changes in pollutant concentration.

[0197] S5.6 Results Analysis and Optimization: Visual analysis is performed on the output coupled simulation results to analyze the simulation results, pollutant degradation pathways and reaction efficiency, identify problem areas, and then adjust the pool structure and operating conditions based on the results, and optimize through multiple iterations;

[0198] S5.61 Result Analysis and Optimization: The simulation results are exported through the post-processing module, a visualization image is generated, and the pollutant degradation rate and ozone utilization rate are calculated, thereby obtaining data on pollutant concentration distribution, ozone diffusion path and reaction efficiency, while extracting relevant key parameters.

[0199] S5.62 Analyze pollutant degradation pathways: By dividing regions, analyzing degradation rate data, and comparing the degradation of each pollutant in different regions, identify problem areas with low degradation rates, and assess flow regime and mass transfer characteristics;

[0200] S5.63 Optimized Design and Operating Parameters: Based on simulation results and degradation pathway analysis, the tank structure and operating conditions were adjusted and iteratively optimized multiple times.

[0201] S6. Steady-state simulation, sensitivity analysis and parameter correction: Perform steady-state simulation, sensitivity analysis and parameter correction on the simulation model;

[0202] S6.1 Steady-state simulation: By setting input conditions and running the steady-state solver, continuous steady-state simulation is performed to simulate the steady state of the system under continuous operation, determine whether the concentration field and flow field are stable and compare them with actual detection data, and evaluate pollutant conversion and ozone utilization efficiency.

[0203] S6.11 Determine steady-state simulation conditions: Set the influent pollutant concentration, ozone dosage, and gas-liquid mixing parameters; select the reaction mechanism model and the CFD coupling model; and check the rationality of all initial and boundary conditions.

[0204] S6.12 Simulation Run: Start the steady-state solver, monitor the convergence of key variables, observe and record the changing trends of the concentrations of each component in the system by running the steady-state solver, adjust the time step or initial conditions until the system parameters no longer change significantly, and confirm whether the model has reached steady state.

[0205] S6.13 Output steady-state results: Export steady-state calculation results through post-processing tools, extract key data of the system under steady state and visualize the data, then compare the extracted data with the design standards to conduct preliminary analysis and evaluation of the reactor performance;

[0206] S6.14 Steady-state result verification: Collect actual operating data and compare it with simulation results, analyze the source of error, adjust model parameters or initial conditions, and reset and calculate if necessary until the model results match the actual data;

[0207] S6.2 Sensitivity Analysis: For changes in influent conditions or reaction kinetic parameters, each key parameter is adjusted step by step, the sensitivity coefficient is calculated, the sensitivity diagram is plotted, the impact of different parameters on the model output is evaluated, the key parameters with the greatest impact on the model output are identified, and optimization design and correction are carried out accordingly.

[0208] S6.21 Select Analysis Parameters: List all parameters that affect the model output, filter and identify the key parameters that have a significant impact on the model's operation;

[0209] S6.22 Stepwise parameter adjustment: By adjusting each key parameter in stages, observe and record the impact of each key parameter on the model output data before and after adjustment, analyze the impact of each parameter on pollutant removal rate and ozone depletion, and identify sensitive areas of system response;

[0210] S6.23 Calculate the sensitivity coefficient: Quantify the sensitivity of the model output to parameter changes through normalized index, and calculate the sensitivity coefficient of the model output for each parameter;

[0211] The specific formula for the sensitivity coefficient is as follows:

[0212]

[0213] Where S represents the sensitivity coefficient; ΔR represents the output change; ΔP represents the parameter change; P represents the parameter value; and R represents the output value.

[0214] S6.24 Drawing Sensitivity Plots: Using data analysis tools to draw visualizations to help identify the key parameters that have the greatest impact on model output;

[0215] S6.25 Determine sensitive parameters: Based on the sensitivity analysis results, identify the key parameters that have the greatest impact on the model output, i.e., the sensitive parameters;

[0216] S6.3 Parameter Correction: Based on actual operating data, fine-tune the key parameters, including kinetic and mass transfer parameters, until the model simulation results match the actual situation.

[0217] S6.31 Determine the parameters to be corrected: Prioritize correcting sensitive parameters that have a significant impact on the model output, and correct deviation parameters under actual operating conditions;

[0218] S6.32 Parameter Adjustment Method: Fine-tune the dynamic parameters and mass transfer parameters in the model using actual operating data to make the simulation results gradually approach the actual operating data;

[0219] S6.33 Re-simulation and verification: By adjusting the parameters and rerunning the model, compare the steady-state results with the actual data, analyze the deviations and continue to adjust until the simulation results are consistent with the expected requirements;

[0220] S6.4 Model Stability Verification: Through multi-condition testing and convergence analysis, the model is ensured to have good adaptability, prediction accuracy, and stable convergence under different conditions.

[0221] S6.41 Multi-condition simulation: Simulation is performed by setting multiple different operating conditions, comparing the simulation results with actual data under each condition, and analyzing the accuracy and stability of the model under different conditions;

[0222] S6.42 Convergence Analysis: Set convergence criteria, monitor residual changes and trends during the iteration process, and adjust solver parameters as necessary to ensure that the model converges stably to a stable solution within a reasonable time and computational step size.

[0223] S7. Model self-learning and self-calibration and output of optimal ozone control: Using the self-learning and self-calibration mechanism, the optimal ozone dosing control strategy of the front ozone contact tank is output.

[0224] S7.1RBF Neural Network Adaptive Self-Learning: An adaptive self-learning module is constructed using an RBF neural network to randomly transform the collected influent parameters and iteratively calculate them repeatedly, recording and saving the optimal ozone dosage or optimal operating parameters.

[0225] S7.11 Data Preparation: Collect actual running data, clean and remove outlier data, define model input and output parameters, organize historical running data to establish the mapping relationship between input and output, and further construct training and validation datasets;

[0226] S7.12 Self-learning Framework Establishment: Construct an RBF neural network, define the parameters of the input and output layers, train the model using a training dataset, optimize the weights and activation functions, and optimize the optimal ozone dosage and removal efficiency through nonlinear mapping;

[0227] S7.13 Initialize weights and parameters: Randomly initialize the weights, center and width parameters of the RBF neural network, set the learning rate and objective function, and use the backpropagation algorithm to optimize the network parameters to minimize the error and gradually approach the optimal solution;

[0228] S7.2 Self-correction: Randomly transform sensitive parameters within a given range, continuously iterate and calculate, and replace old values ​​with better-performing parameters to optimize and self-correct the parameter settings within the model;

[0229] S7.21 Input data perturbation: Randomly perturb the influent parameters to simulate fluctuations in the influent parameters and generate multiple sample points. Then run the model and record the output results.

[0230] S7.22 Multiple Iterative Optimization: Perform multiple iterative calculations and adjust model parameters for each set of perturbation data to find the optimal ozone dosage;

[0231] S7.23 Optimal value storage: The calculated optimal ozone dosage and corresponding influent conditions are organized and stored in the database, and an index and retrieval mechanism are established so that the stored optimal solution can be called first under similar influent conditions;

[0232] S7.3 Self-calibration process: By selecting sensitive parameters and subjecting them to random perturbations, the optimal parameter combination is repeatedly sought to optimize the model's prediction accuracy and performance;

[0233] S7.31 Sensitive Parameter Selection: Based on the sensitivity analysis results, select the key parameters that have the greatest impact on the model output;

[0234] S7.32 Randomly Varying Parameters: Determine the variation range of each sensitive parameter, use a random number generator to perturb the sensitive parameters, generate multiple perturbed parameter values, and synthesize multiple parameter combinations;

[0235] S7.33 Finding the optimal value: Run the model for each set of parameters, calculate and record the pollutant removal efficiency, compare the results of all combinations, and select the best-performing parameter combination as the basis for the next parameter update.

[0236] S7.34 Parameter Update: Updates the optimal parameter values ​​to the model, replacing the original parameter settings;

[0237] S7.4 Model Validation and Adjustment: By validating the accuracy and adaptability of the model's predictions, errors are identified and adjusted.

[0238] S7.41 Model Validation: Compare the model's predictions with the actual data to verify the model's accuracy and adaptability;

[0239] S7.5 Output Optimal Ozone Control: By adjusting the structure and optimization algorithm of the RBF neural network, the deviation between the model output and the actual data is reduced;

[0240] S7.51 Optimal Value Output: Based on the self-learning and self-correcting model, calculate and output the optimal ozone dosage and corresponding operation strategy under the current influent conditions;

[0241] S7.52 Control System Feedback: The optimal ozone dosage output by the model is sent to the controller, and the ozone dosage is adjusted according to the actual operating effect;

[0242] S7.53 Dynamic Optimization: By monitoring influent parameters in real time and calling the model for calculation, the ozone dosage is dynamically adjusted;

[0243] S7.6 Result Storage and Application: Stores the optimal value and related data for each run;

[0244] S7.61 Result Storage: By storing the optimal value and related data from each run into the database, a complete historical dataset is formed;

[0245] S7.62 Empirical Rule Update: Updates existing empirical rules based on model output and runtime data.

[0246] Furthermore, the method for building a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to the present invention also includes the following details:

[0247] Firstly, in the S2.22 ozone generation system configuration, the ozone addition method is either continuous addition or intermittent addition.

[0248] Secondly, in the input components of the S3.1 model, the input variables include COD sub-components, nutrients, and petroleum pollutants;

[0249] The input variables include COD sub-components, nutrients, and petroleum pollutants;

[0250] The COD sub-components include organic compounds of different molecular weights or structural types;

[0251] The nutrients include nitrogen and phosphorus;

[0252] The petroleum pollutants mentioned include various hydrocarbons and their derivatives.

[0253] Third, S3.12 determines the reaction type, including the main chemical reactions and physical processes in the pre-ozone contact tank, specifically:

[0254] The main chemical reactions include ozone oxidation and auxiliary reactions;

[0255] The ozone oxidation reaction is a direct reaction between ozone and organic matter, or indirect oxidation through the generation of hydroxyl radicals. Different organic matter may exhibit selective reaction or non-selective attack.

[0256] The auxiliary reactions include the self-decomposition of ozone in water and side reactions with ions or other intermediate products;

[0257] The main physical processes include mass transfer processes, mixing and diffusion, and degradation pathways;

[0258] The mass transfer process is the transfer of ozone from the gas phase to the liquid phase.

[0259] The mixing and diffusion mentioned above refer to the fact that uneven mixing or local concentration gradients within the reactor can also affect the reaction rate and conversion rate.

[0260] The degradation pathway involves multi-stage degradation processes for different components, forming intermediate products, which are then further oxidized into harmless substances.

[0261] Fourth, in step S3.13, the modeling tool can be selected from MATLAB, COMSOL Multiphysics, or Aspen Plus.

[0262] Fifth, S4.42 defines boundary conditions, including various boundary conditions such as inlet, free surface, non-slip wall, and outlet, specifically as follows:

[0263] The inlet is set with a velocity inlet, i.e., the inlet flow velocity. The flow velocity is calculated and input.

[0264] The free liquid surface is set to Degassing, which simulates gas detaching from the liquid surface and escaping through the liquid surface.

[0265] The non-slip wall is set as a non-slip boundary condition, friction is ignored, and the wall surface velocity is zero.

[0266] The outlet is set to free flow, and free flow boundary conditions are set for the outlet.

[0267] Fifth, the selection and parameter settings of the S4.5 gas-liquid two-phase flow model, including the Symmetric drag model, Tomiyama lift model, UDF, Degassing boundary, air intake, and ignoring other components in the air and the liquid phase, are as follows:

[0268] The mechanism for selecting and setting parameters of the gas-liquid two-phase flow model includes Symmetric drag model, Tomiyama lift model, UDF, Degassing boundary, air intake, and ignoring other components in the air and the liquid phase;

[0269] The Symmetric drag model describes the interaction force between the bubble and the liquid.

[0270] The Tomiyama lift model represents the simulated force on a bubble; the UDF represents a custom function characterizing ozone mass transfer and decay.

[0271] The Degassing boundary is used to define the free surface;

[0272] The air intake is configured as a mass inlet;

[0273] The other components in the air are ignored, and only two gaseous components, oxygen and ozone, are set.

[0274] The liquid phase needs to include water, liquid ozone, and liquid oxygen.

[0275] Sixth, the force setting in S4.52 includes gas-liquid interaction forces such as drag force, virtual mass force, lift force, and surface tension, specifically:

[0276] The drag force refers to the drag force exerted by the liquid on the bubble as the bubble moves in the liquid;

[0277] The virtual mass force refers to the additional force required by the liquid to respond to the acceleration of the bubble when the bubble accelerates or decelerates.

[0278] The lift force refers to the buoyancy effect of the bubble in the liquid, i.e., the rotation and movement path of the bubble;

[0279] The surface tension refers to the intermolecular forces at the gas-liquid interface.

[0280] (3) Invention-related mechanisms and principles

[0281] The main mechanism and principle of the present invention, which describes a model building method for an ozone contact tank before advanced oxidation of petroleum wastewater, lies in the organic coupling of complex wastewater component analysis, reaction kinetics, fluid mechanics and intelligent control, thereby realizing the precise simulation and optimized control of the ozone reaction process with pollutants in the pre-ozone contact tank.

[0282] This invention provides a detailed analysis and classification of influent components, categorizing petroleum wastewater into multiple components including organic matter, nitrogen, phosphorus, microorganisms, and petroleum derivatives. COD is further subdivided into particulate, dissolved, biodegradable, and non-biodegradable forms, while nitrogen and phosphorus are also subdivided into various forms. This refined classification method provides a foundation for subsequent reaction kinetic analysis, enabling quantitative studies of each sub-component during ozone oxidation and ensuring sufficient data support for establishing the reaction mechanism.

[0283] Based on experimental simulation and reaction kinetic studies, this invention employs a scaled-down experimental model to prepare the actual influent water. The concentration and chemical structure changes of each sub-component under ozone exposure are then monitored experimentally. Therefore, by experimentally determining the degradation pathways, transformation processes, and corresponding reaction rate constants and kinetic equations of each sub-component, a kinetic model accurately reflecting the reaction behavior of ozone with each component is established.

[0284] In terms of constructing the mechanistic model, this invention transforms the reaction kinetic data obtained from experiments into mathematical models. The basic rate equation and the overall reaction rate equation are used to describe the transformation process of each component in the influent under the action of ozone. By using these mathematical models, the reaction rate and final transformation effect of ozone with each component under certain conditions can be predicted, providing a theoretical basis for process design and control.

[0285] From a CFD (Computational Fluid Dynamics) modeling perspective, this invention constructs a three-dimensional geometric model of the pre-ozone contact tank based on CAD drawings, similar to the actual structure, and uses CFD software to simulate the flow regime within the tank. This invention employs the Standard k-ε turbulence model and two-film theory to model the gas-liquid two-phase mass transfer process, setting realistic boundary conditions such as velocity inlets, no-slip walls, and free liquid surfaces to ensure that the simulated flow, mixing, and mass transfer processes closely match actual conditions.

[0286] In terms of coupling the mechanistic model and the CFD model, this invention couples the reaction kinetic model with the CFD fluid model, enabling each local region in the three-dimensional flow field to reflect the actual reaction rate and mass transfer process. This coupling method achieves a unified consideration of the microscopic reaction mechanism and macroscopic fluid motion, ensuring sufficient contact between ozone and pollutants while promptly capturing local flow unevenness issues, thereby optimizing the entire reaction process.

[0287] From the perspective of intelligent control with self-learning and self-correction, this invention introduces the RBF neural network intelligent algorithm. Through random variation, iterative optimization, and sensitivity analysis, it adjusts the influent parameters in real time to optimize the ozone dosage. The system performs dynamic calculations under each influent condition, recording and updating the optimal control parameters, achieving self-learning and self-correction based on historical data and the current state. This ensures energy conservation and consumption reduction while maintaining effluent quality.

[0288] This invention discloses a modeling method for a pre-ozone contact tank in the advanced oxidation process of petroleum wastewater. Through detailed analysis of the influent components, experimental determination of reaction kinetics, construction of a mechanistic model and a CFD model, and coupling of these two models, a self-learning control mechanism is introduced. This achieves a closed-loop design encompassing the entire process, from microscopic reaction mechanisms to macroscopic fluid motion and intelligent optimization control. This modeling method for a pre-ozone contact tank in the advanced oxidation process of petroleum wastewater not only accurately describes the chemical reactions and mass transfer processes between ozone and various components within the pre-ozone contact tank, but also adaptively adjusts the ozone dosage according to actual operating conditions, thereby significantly improving process stability and treatment efficiency.

[0289] (4) Effects of the invention

[0290] This invention discloses a modeling method for an ozone contact tank before advanced oxidation of petroleum wastewater. This method successfully constructs an intelligent control system capable of optimizing and precisely controlling the reaction in the ozone contact tank during petroleum wastewater treatment. By combining mechanistic mathematical models, fluid dynamics models, and artificial neural network technology, this invention successfully constructs an intelligent and precise wastewater treatment system. Through precise control of ozone dosage, this system achieves resource optimization, pollutant removal, reduced energy and chemical consumption, and stable emission indicators during wastewater treatment. This innovation replaces traditional manual experience-based control methods with a highly efficient intelligent control system, driving the wastewater treatment industry towards a more intelligent, environmentally friendly, and sustainable development.

[0291] First, in terms of system overview and technological innovation, the system of this invention adopts a mechanistic mathematical model-based intelligent control system. By integrating multiple parameters, self-learning, and self-correction functions, it can learn and adjust itself according to changes in real-time operating conditions. As the system runs longer, the memory area of ​​the model continuously expands, and the accuracy of the mathematical model calculation gradually improves. The system can calculate the optimal values ​​of each control parameter in real time according to different operating conditions, thereby achieving automated and precise control of complex biological reaction processes, breaking through the traditional manual experience-based control methods.

[0292] Secondly, in terms of application effects, the system effectively reduces the amount of ozone added in the pre-ozone contact tank. Through optimization of the mathematical model and self-learning function, the system can more accurately control the amount of ozone used, avoiding over-dosing, thereby improving treatment efficiency and reducing costs. Precise control stabilizes and optimizes effluent indicators, ensuring that the system consistently meets emission standards under various operating conditions, optimizing effluent quality, reducing environmental pollution load, and positively impacting the improvement of the regional water environment. Furthermore, the system's automation and intelligence reduce the need for manual intervention, significantly alleviating the workload of maintenance personnel. Real-time automatic monitoring and adjustment ensure stable effluent quality compliance at the wastewater treatment plant. By optimizing ozone dosage and adjusting other control parameters, the system can reduce energy consumption, chemical consumption, and carbon emissions, thereby maximizing environmental and social benefits.

[0293] Third, in terms of technological innovation and methodology, this invention establishes kinetic equations for the subdivided components, accurately describing the behavior of each component, and constructs a more realistic mechanism model. This provides scientific guidance for the operation of water plants, especially in the use of ozone and the removal of pollutants in wastewater treatment, enabling more accurate prediction and optimization. The technical solution described in this invention, through the construction of a fluid dynamics (CFD) model, deeply simulates and analyzes the fluid flow and reaction state inside the reaction tank, significantly improving the accuracy of the simulation. This helps to more accurately capture the reaction mechanism and dynamic changes of the fluid in the pre-ozone contact tank, providing strong support for further process optimization. Furthermore, this invention couples the mechanism model with the fluid dynamics model to form a comprehensive process model capable of accurately simulating the entire treatment process. Simultaneously, this invention introduces artificial neural network technology to achieve feedforward control, intelligent adjustment, and self-optimization of ozone dosage, making the production process more intelligent and efficient.

[0294] Fourth, from the perspective of resource optimization and sustainable development, this invention not only improves wastewater treatment efficiency but also achieves optimal resource allocation and minimizes flow rate through precise control of ozone dosage. This control method plays a positive role in improving the removal efficiency of chemical oxygen demand (COD) and enhancing the biodegradability of effluent. This technical solution provides new ideas and methods for the sustainable development of the wastewater treatment industry, particularly in terms of energy conservation and emission reduction, improving treatment efficiency, and reducing operating costs, which has significant practical implications. Attached Figure Description

[0295] Figure 1 This is an overall flowchart of the model building method for an ozone contact tank before advanced oxidation of petroleum wastewater according to the present invention.

[0296] Figure 2This is a model diagram of the ozone contact tank before the advanced oxidation of petroleum wastewater, as described in the present invention.

[0297] Figure 3 This is a model diagram of the post-ozone contact tank in the model construction method of the pre-ozone contact tank for advanced oxidation of petroleum wastewater according to the present invention. Detailed Implementation

[0298] The present invention will be further described below with reference to the accompanying drawings, but this is not intended to limit the present invention in any way. Any modifications or substitutions made based on the teachings of the present invention shall fall within the protection scope of the present invention.

[0299] Figure 1 This is an overall flowchart of a method for building a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to the present invention. It describes a method for establishing a model of an ozone contact tank before treating petroleum wastewater using advanced oxidation. By combining mechanistic models, CFD simulations, stability analysis, and machine learning techniques, it is possible to accurately predict the ozone dosage and wastewater treatment effect, thereby achieving the goal of optimizing the treatment process and improving efficiency.

[0300] Specifically, Figure 1This paper presents the overall process of building a model for an advanced oxidation pre-ozone contact tank for petroleum wastewater, as follows: First, determining the influent components of the pre-ozone contact tank is the initial step in model building. It requires identifying the various components in the water sample entering the ozone contact tank to provide basic data for subsequent model building and parameter analysis. Second, establishing the mechanistic model of the pre-ozone contact tank is the core of the entire model building process. Its purpose is to construct a reasonable mechanistic model by understanding and describing the behavior of substances in the ozone contact tank to reflect the actual treatment process. Third, determining the tank structure based on CAD drawings and completing the 3D model using computational fluid dynamics (CFD) involves using CAD tools to determine the structure and dimensions of the contact tank and using CFD technology to simulate the fluid flow within the tank, thus completing the 3D model and accurately reflecting the key parameters such as the fluid flow path and velocity distribution within the tank. Fourth, combining the mechanistic model of the pre-ozone contact tank with the CFD model involves... The steps of the process are as follows: First, the mechanistic model and the CFD model can better simulate and analyze the mass transfer process within the ozone contact tank, especially the reaction process between ozone and pollutants in the water. Second, the stability model, after establishing the mechanistic and CFD models, requires stability analysis to ensure that the model can operate stably under various operating conditions and accurately reflect the actual situation. Third, the sensitivity analysis explores the impact of different parameters, such as water flow velocity and tank structure, on the model results, thereby understanding the impact of changes in key parameters on the treatment effect. Fourth, the parameter fitting and adjustment step involves fitting and adjusting the parameters in the model based on actual experimental data to improve the accuracy and reliability of the model. Fifth, the model self-learning and self-adjustment step combines machine learning technology to enable the model to learn and automatically adjust itself to better adapt to different treatment environments and parameter changes. Sixth, the ozone dosage control step ultimately controls the ozone dosage based on the model's predictions to optimize ozone utilization efficiency and achieve the best wastewater treatment effect.

[0301] Figure 2 This diagram illustrates the pre-ozone contact tank model of the advanced oxidation pre-ozone contact tank for petroleum wastewater, as described in this invention. It depicts the ozone addition, contact reaction, and post-reaction treatment process during wastewater treatment. Ozone, as a strong oxidant, reacts with pollutants in the wastewater. During this process, ozone is precisely controlled and introduced into the reaction tank. By combining different reaction units and storage modules, the entire system can achieve the degradation and removal of pollutants in the wastewater.

[0302] Specifically, Figure 2This paper demonstrates a method for constructing a pre-ozone contact tank model for advanced oxidation of petroleum wastewater. The model's specific components are as follows: First, the input terminal (in), located in the diagram, represents the inflow of wastewater or the liquid to be treated into the model. Its function is to represent the raw material or wastewater flow entering the pre-ozone contact tank. Second, the carbon combiner is a mixing device that mixes activated carbon or other carbon-based materials with the wastewater or treatment liquid. Its purpose is to enhance the wastewater treatment effect through adsorption, particularly for mixing certain substances, such as activated carbon or other adsorbents, with the wastewater. This enhances the adsorption and removal efficiency of pollutants, ensuring effective contact between the activated carbon or adsorbent and the wastewater, thus providing conditions for subsequent ozone contact. Third, a multiplexer (Mux) is a signal combining device that combines multiple input signals, such as ozone dosage and flow rate, into a single signal for processing or transmission. In this diagram, the Mux combines multiple pieces of information, including ozone dosage and wastewater flow rate, for use by subsequent treatment units. By combining multiple input signals into a single stream or signal, the multiplexer simplifies and integrates multiple signals in complex control systems, allowing the system to select the most suitable operating parameters based on different conditions. Combining ozone dosage and wastewater flow rate input data, it provides parameters for subsequent reactions, thereby controlling the entire reaction process. Fourth, the ozone contact reaction (Advoxdation) is the core component of the ozone contact reaction, representing the process of pollutants in wastewater reacting with ozone. In this process, ozone, as a strong oxidant, reacts with organic pollutants in the water, degrading or transforming them. The basic principle of the ozone reaction is that ozone molecules (O3) degrade organic matter or other pollutants in the water through oxidation. Ozone reacts with pollutants in the water to generate oxygen and water, removing harmful components from the water. This process is widely used in water treatment, wastewater treatment, and disinfection. Ozone contact reaction decomposes organic pollutants in the water through oxidation, achieving the purpose of wastewater purification. Fifth, ozone addition (Ozone1) refers to the ozone addition process. Controlling the amount of ozone ensures that the reaction takes place under optimal conditions, ensuring sufficient ozone participation in the ozone contact reaction stage, thereby improving water treatment efficiency. Controlling the ozone dosage is a key aspect of water treatment. By precisely controlling the ozone dosage, it can effectively react with pollutants without waste. The amount of ozone added is dynamically adjusted based on the concentration of pollutants in the water, treatment objectives, and reactor design factors. Therefore, effective ozone addition can ensure optimized treatment results.Sixth, Reactor Reac1 represents the reactor unit, which is the core area of ​​the ozone contact reaction in the entire wastewater treatment process. In this reactor, ozone reacts with pollutants in the wastewater to complete the task of degrading and removing harmful substances. Inside the reactor, wastewater and ozone are in full contact. Ozone, as a strong oxidant, reacts with organic pollutants in the water, oxidizing them into harmless substances or substances that are easier to remove. The mixing of water flow and ozone in the reactor, as well as the contact time, determine the reaction efficiency. The reactor is the area where the chemical reaction takes place, ensuring that wastewater and ozone are in full contact, thereby completing the task of degrading and removing organic matter. Seventh, Storage (Store car1, Store reac1, Store ozone1) represents the monitoring and recording of different variables (such as activated carbon concentration, reaction products, and ozone concentration). It is used to store and track various key data in the process, ensuring continuous monitoring and optimized control of the data. Storage (Store car1, Store reac1, Storeozone1) ensures the tracking and preservation of various data in the reaction process, facilitating further analysis and control of the treatment process. Among them, the carbon storage (Store car1) stores parameters or concentrations related to activated carbon or carbon-based materials, which can be used to track the adsorption capacity of activated carbon for pollutants and its usage in wastewater treatment; the reactor storage (Store reac1) stores relevant data in the reactor, such as the temperature, ozone concentration, and quality of the treated water, which helps to analyze the working status of the reactor and optimize the treatment process; and the ozone storage (Store ozone1) stores ozone dosage and concentration data, which helps to monitor and control the ozone dosage effect, and by monitoring the ozone dosage, it can ensure the efficiency of ozone use during the reaction process and avoid over- or under-dosing. Eighth, "out" represents the treated wastewater or liquid flowing out of the system. It is the final output of the entire treatment process. After ozone reaction and adsorption, along with other treatment processes, pollutants in the water are removed, and the quality of the final effluent should meet treatment targets, such as discharge standards or reuse requirements. "Out" indicates the final wastewater flowing to the outlet after reaction and treatment, signifying the effectiveness of the ozone contact reaction. The water treated by the reactor will flow out, and pollutants are degraded or removed during this process. The final effluent is wastewater treated with advanced oxidation, meeting relevant discharge standards or suitable for other uses.

[0303] Figure 3 This is a model diagram of the post-ozone contact tank in the advanced oxidation pre-ozone contact tank model construction method of petroleum wastewater according to the present invention. Similar to the pre-ozone contact tank, it describes the subsequent process of ozone reacting with pollutants in wastewater during wastewater treatment.

[0304] Specifically, Figure 3The diagram illustrates a post-ozone contact tank model, which includes: First, the input (in), representing the water flow into the post-ozone contact tank. Similar to the pre-ozone contact tank, wastewater undergoes preliminary treatment before flowing into the post-ozone contact tank, ready for further ozone oxidation. Second, the Carboncombiner, similar to the Carboncombiner in the pre-ozone contact tank, mixes activated carbon or other adsorbent materials with the wastewater, enhancing pollutant adsorption, especially for organics that are difficult for ozone to directly degrade. The Carboncombiner enhances pollutant removal efficiency by adding activated carbon or other carbon-based materials, while optimizing subsequent reaction processes. Third, the Mux, in this model, combines multiple input signals, such as ozone dosage and wastewater flow rate, into a single signal for use in the subsequent ozone contact reaction process. The Mux integrates the ozone dosage and wastewater flow rate signals, ensuring the system can adjust the ozone dosage and flow rate as needed to optimize subsequent reaction conditions. Fourth, the ozone contact reaction (Advoxdation) refers to the process in which ozone reacts with pollutants in the wastewater in the post-ozone contact tank. In this reaction unit, ozone oxidizes organic matter and other pollutants in the water, removing harmful substances. Ozone acts as a strong oxidant in this process, oxidizing pollutants in the wastewater into harmless or removable substances. Fifth, the function of Ozone2 (ozone dosing) is to control the amount of ozone added, ensuring sufficient ozone in the post-ozone contact tank for pollutant oxidation. The amount of ozone added is precisely controlled based on the concentration of pollutants in the wastewater and the treatment objectives. Therefore, ozone dosing control is a key step in the treatment process, ensuring maximum reaction efficiency and preventing excessive or insufficient ozone addition. Sixth, reactor Reac2 is the reaction unit of the post-ozone contact tank, where wastewater and ozone undergo a complete reaction. This reactor ensures that ozone and pollutants in the water come into full contact and effectively remove pollutants through oxidation. Inside reactor Reac2, wastewater reacts with ozone, and the strong oxidizing properties of ozone remove harmful substances, thereby further purifying the water.Seventh, the storage (Store car2, Store reac2, Storeozone2) is used to store and monitor key data during the process for subsequent analysis and optimization. Specifically, Storecar2 stores the concentration and usage of activated carbon or other adsorbent materials, helping to track their performance during treatment. Store reac2 stores key data from the reactor, such as ozone concentration, treatment effect, and reaction conditions, for analyzing reactor operation and optimizing the reaction process. Store ozone2 stores the ozone dosage and concentration parameters, ensuring effective monitoring of ozone usage during the reaction process. Eighth, the output (out) represents the final treated wastewater flow. After the ozone contact reaction and adsorption steps, pollutants in the wastewater are effectively removed, and the final effluent should meet discharge standards or be reused. The water then undergoes further treatment in the post-ozone contact tank before being discharged or subjected to further treatment.

[0305] Example 1: Specific Implementation of the Model Construction Method for an Ozone Contact Tank Before Advanced Oxidation of Petroleum Wastewater According to the Present Invention

[0306] The technical solution described in this invention achieves intelligent optimization from influent component analysis to the optimal ozone dosing control strategy through seven main steps; specifically as follows:

[0307] S1. Collection and Analysis of Influent Components: First, the influent water quality is precisely measured, analyzing various chemical components such as COD, nitrogen, phosphorus, and petroleum hydrocarbon concentrations. This step provides detailed data support for subsequent model building and control strategy design.

[0308] S2. Small-scale experimental setup to determine reaction kinetics: In the laboratory, the reaction process is simulated using a small-scale experimental setup to study the kinetic behavior of the reaction between ozone and pollutants in water. By acquiring experimental data, the degradation rate constant of pollutants and other reaction parameters are determined, providing the necessary reaction kinetic basis for subsequent mechanistic modeling.

[0309] S3. Establish a mechanistic model: Based on experimental data of reaction kinetics, establish a mechanistic model describing the ozone oxidation process. This model can simulate the specific process of ozone reacting with pollutants under different water quality conditions, revealing the reaction mechanism and material transformation pathway;

[0310] S4. CFD Flow Pattern and Gas-Liquid Mass Transfer Modeling: Computational Fluid Dynamics (CFD) is used to simulate the flow pattern and gas-liquid mass transfer process in the ozone contact tank. By establishing a flow field model, the distribution, transfer, and reaction processes of ozone in the contact tank, as well as the diffusion of bubbles and the mass transfer characteristics of the gas-liquid interface, are accurately described.

[0311] S5. Coupled Mechanism Model and CFD: The aforementioned mechanism model is coupled with the CFD fluid dynamics model to form a comprehensive coupled model. This coupled model can simultaneously consider chemical reaction processes and fluid dynamic processes, thereby more accurately simulating the behavior of ozone in actual operation and comprehensively improving the model's predictive ability.

[0312] S6. Validate the coupled model and perform parameter sensitivity analysis: Validate the coupled model to ensure its consistency with actual experimental or field data. Furthermore, perform parameter sensitivity analysis to identify key parameters affecting the ozone dosing control effect, ensuring the model has good adaptability and reliability.

[0313] S7. Optimal Ozone Dosing Control Through Neural Network Self-Learning and Self-Correction: Utilizing a neural network for self-learning and self-correction, the model's output is continuously optimized based on historical and real-time data input. The neural network continuously adjusts its parameters through a feedback mechanism, automatically generating the optimal ozone dosage and control strategy. This process allows for real-time adjustment of the ozone dosage under varying water quality fluctuations, ensuring optimal pollutant degradation and ozone utilization.

[0314] The present invention discloses a method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater, comprising the following steps:

[0315] S1. Analysis of influent components: Analysis of the components of each pollutant in the influent of the ozone contact tank and their initial concentrations;

[0316] S1.1 Sampling and Detection: Collect raw petroleum wastewater samples from sampling points and use different experimental detection methods to determine different pollutants in the influent;

[0317] S1.11 Determine the sampling method: Collect uniformly mixed wastewater samples from representative sampling points, and determine an appropriate sampling volume according to the testing requirements to ensure that the samples can accurately reflect the influent water quality;

[0318] S1.12 Sample Preservation: By employing specialized preservation measures for different components, we ensure that the collected samples remain stable in composition during analysis, unaffected by microbial activity or chemical reactions.

[0319] S1.2 The influent components are divided into four categories: COD components, nitrogen components, phosphorus components, and petroleum components;

[0320] S1.3 Microbial component test: Analysis of microbial communities such as autotrophic bacteria and aerobic bacteria;

[0321] S1.4 Determine the final influent component list: Based on the experimental measurement results, confirm the specific influent components and the initial concentration levels of various pollutants in the pre-ozone contact tank;

[0322] S2. Experimental simulation of the reaction process in the pre-ozone contact tank: The reaction process of each sub-substance in the pre-ozone contact tank is simulated by experimental methods to determine the concentration changes, chemical structure changes and corresponding reaction kinetic equations and constants of each sub-component;

[0323] S2.1 Construction of small-scale test device: Customize a small-scale pre-ozone contact pool test device according to CAD drawings, and then arrange ozone dosing in the small contact pool of the laboratory according to the ozone controller dosing method at the sampling location.

[0324] S2.2 Small-scale test water configuration: Based on the components and concentrations in the petroleum wastewater, water is allocated according to the actual concentration range to ensure that the composition of the influent to the small pre-ozone contact tank matches the actual working conditions as closely as possible.

[0325] S2.21 Preparation of influent sample: Based on the sampling results and actual wastewater characteristics, prepare simulated influent according to the proportion;

[0326] S2.22 Ozone generation system configuration: Install the ozone generator and calibrate its output concentration and flow rate, and determine the ozone dosing method and dosage range;

[0327] S2.23 Monitoring Equipment Preparation: Configure online monitoring instruments and laboratory equipment, including COD analyzer, ammonia nitrogen, nitrate nitrogen, total phosphorus analyzer, GC-MS gas chromatography-mass spectrometry system, pH meter and dissolved oxygen meter, for detecting changes in chemical structure and concentration changes of each component;

[0328] S2.3 Determine the reaction process of sub-substances in the pre-ozone contact tank: In a small-scale experimental device, measure the concentration changes and chemical structure changes of each sub-component under the action of ozone, analyze its degradation pathway, and determine the reaction kinetic equations and constants of each sub-substance;

[0329] S2.31 Experimental Operation: By setting initial conditions, starting the reaction, and taking samples at different time intervals, the ozone contact process under actual working conditions is simulated to monitor pollutant degradation and changes in related parameters;

[0330] S2.32 Detailed Component Reaction Analysis: By measuring the changes in the concentration and chemical structure of each pollutant, and analyzing the effects of ozone concentration, gas-liquid mixing, temperature and pH on reaction efficiency, the degradation mechanism of each component is sought and the optimal operating conditions are determined.

[0331] S2.33 Kinetic Parameter Extraction: The reaction kinetic model is fitted using experimental data to extract reaction kinetic parameters in order to quantitatively describe the reaction process of each pollutant component;

[0332] S2.34 Experimental Data Analysis and Optimization: By analyzing the degradation efficiency, ozone utilization rate and degradation mechanism of each pollutant at different time points, and adjusting the experimental parameters based on data feedback, the best removal effect can be obtained.

[0333] S3. Establish a mechanism model for the pre-ozone contact tank: Based on the mechanism models established in S1. and S2., a multi-component degradation reaction model for the pre-ozone contact tank is formed, taking the influent components and their reaction kinetics and the reaction process of the substances as inputs.

[0334] S3.1 Model Input Components: The various components and their initial concentrations determined in S1. and S2. are used as model input parameters;

[0335] S3.11 Collect experimental data: Under strictly controlled sampling conditions, collect the concentrations of organic matter, nutrients, and petroleum pollutants in the influent, as well as the corresponding reaction kinetic parameters;

[0336] S3.12 Determine the reaction type: Identify the main chemical reactions and physical processes in the pre-ozone contact tank, and construct a kinetic model to describe their coupling effects;

[0337] S3.13 Selecting Modeling Tools: Select a suitable modeling tool for writing and solving equations;

[0338] S3.2 Kinetic Equations and Constants: The reaction kinetic equations and reaction kinetic constants obtained in S2. are incorporated into the model, and degradation equations are established for each type of substance;

[0339] S3.21 Classification and Development of Rate Equations: Establish specific degradation equations for different types of pollutants;

[0340] S3.22 Mass transfer and reaction combined: The mass transfer process of ozone from the gas phase to the liquid phase is coupled with the chemical reaction rate in the liquid phase to reveal and predict how the overall reaction rate is affected by the concentration difference at the gas-liquid interface and the mass transfer coefficient.

[0341] The mass transfer rate equation is as follows:

[0342] J = K L α·A·([O3] gas -[O3] liquid )

[0343] Where J represents the mass transfer flux of ozone at the gas-liquid interface; K L α represents the liquid-phase mass transfer coefficient; α represents the gas solubility coefficient; A is the effective specific surface area within the reactor, i.e., the total area of ​​the gas-liquid interface; [O3] gas [O3] represents the concentration of ozone in the liquid phase at equilibrium. liquid This represents the actual ozone concentration in the liquid phase.

[0344] S3.3 Integrated Mechanism Model: The reaction equations of each component obtained from S3.1 and S3.2 are assembled and coupled to form a multi-component mechanism model of the pre-ozone contact tank as a whole;

[0345] S3.31 Determine the equation structure: Summarize all rate equations and establish a total rate equation system containing multiple components;

[0346] S3.32 Simultaneous Equations: The rate equation and mass transfer equation are integrated into a system of simultaneous equations using software. The system of equations is solved using finite element analysis or matrix calculation tools. At the same time, the coupling effect of mass transfer and reaction in the spatiotemporal distribution is described. The system of equations is solved using finite element analysis or matrix calculation tools.

[0347] S3.33 Set initial conditions: Input the initial concentration value obtained from the experiment and set the initial ozone concentration;

[0348] S3.41 Input experimental parameters: Input the kinetic constants, mass transfer coefficients, and reaction rate constants obtained in the experiment into the model one by one;

[0349] S3.42 Set boundary conditions: Input boundary conditions in the pool model;

[0350] S3.43 Setting the time step and simulation range: Based on the experimental reaction time range, set the simulation time step and total simulation time;

[0351] S3.4 Parameter Input: By importing the kinetic constants, mass transfer coefficients, initial concentrations, and setting boundary conditions, time steps, and total simulation time obtained from experiments, a parameter library that truly reflects the actual working conditions is constructed.

[0352] S3.41 Input experimental parameters: Input the kinetic constants, mass transfer coefficients, and reaction rate constants obtained in the experiment into the model one by one;

[0353] S3.42 Set boundary conditions: By inputting ozone gas concentration and influent parameters consistent with the experimental conditions into the pool model, the model can truly reflect the actual operation process and provide necessary initial and boundary data for subsequent solutions;

[0354] S3.43 Setting the time step and simulation range: By selecting an appropriate time step and total simulation time, the dynamic changes in the reaction process can be accurately captured;

[0355] S3.5 Solving the Mechanism Model: By selecting an appropriate solver, combining the mass transfer and reaction equations, running the simulation, and verifying the consistency between the model results and experimental data;

[0356] S3.51 Choosing a solution method: By selecting an appropriate time-dependent or steady-state solver, the mass transfer and reaction kinetic equations are combined to construct a complete coupled model;

[0357] S3.52 Simulation: Numerical integration calculations are performed by inputting parameters, initial conditions, and boundary conditions to observe the dynamic trends of the concentration of different components over time.

[0358] S3.53 Check the model results: Verify whether the model output meets expectations by comparing the simulation results with the experimental data;

[0359] S3.6 Result Analysis and Correction: By comparing the simulation results with the experimental data, the model parameters are adjusted to improve the simulation accuracy, and finally a reliable mechanism model is generated;

[0360] S3.61 Results Output and Visualization: Outputs pollutant concentration change curves, degradation rate charts, and visualizes the distribution of ozone in the pool;

[0361] S3.62 Correcting Model Parameters: When there is a deviation between the model results and experimental data, adjust the kinetic constants, mass transfer parameters or initial conditions, and rerun the model;

[0362] S3.63 Generate the final mechanism model: Based on the corrected parameters, save the final model;

[0363] S4. CAD drawing modeling and CFD three-dimensional modeling: Based on the CAD drawings, a three-dimensional geometric model of the pre-ozone contact tank is established, and a fluid dynamics model is constructed in CFD software, including flow field establishment, turbulence model selection, boundary condition setting, and numerical simulation of gas-liquid mass transfer and ozone decay reaction.

[0364] S4.1 Three-dimensional geometric model construction: Based on the CAD drawing of the pre-ozone contact tank, a complete three-dimensional geometric model and computational domain are established in COMSOL to accurately reflect the actual structure and key areas;

[0365] S4.11 Data Collection: Obtain CAD drawings of the pre-ozone contact tank and physicochemical parameters of the wastewater;

[0366] S4.12 Software Tool Selection: Select suitable modeling and fluid dynamics simulation software based on project requirements;

[0367] S4.2 Physical field selection: Based on the Reynolds number Re being greater than the critical value, the Standard k-ε turbulence model is selected to simulate jet diffusion, rotating flow, and separated flow in the flow field;

[0368] S4.21 Establishing a 3D geometric model: Based on CAD drawings, construct a 3D model in modeling software that contains only the key pool structure;

[0369] S4.22 Define the calculation domain: Define the calculation domain as the internal area of ​​the front ozone contact tank;

[0370] S4.3 Boundary Condition Setting: Set specific fluid boundary conditions for each key region of the front ozone contact tank in the numerical simulation, and define the convergence criteria;

[0371] S4.31 Pool Material Settings: Define the pool and set its physical properties;

[0372] S4.32 Liquid and Gas Parameter Settings: Set key physical parameters for wastewater and air-ozone mixtures respectively;

[0373] S4.4 Pre-ozone contact tank flow reaction simulation: A numerical model based on the two-film theory and first-order reaction kinetics is constructed to evaluate and optimize the wastewater treatment effect by simulating the mass transfer, decay and oxidation reaction process of ozone and organic matter in wastewater in the gas-liquid two phases.

[0374] S4.41 Selecting a turbulence model: Determine the flow regime based on the fluid Reynolds number and use the standard k-ε turbulence model to simulate complex turbulence phenomena;

[0375] S4.42 Define boundary conditions: Set various boundary conditions for the model, including inlet, free surface, no-slip wall and outlet;

[0376] S4.5 Gas-Liquid Two-Phase Flow Model Selection and Parameter Settings: By selecting a multiphase flow model, reasonably configuring the drag and lift models, and customizing the ozone mass transfer and decay functions;

[0377] S4.51 Selecting the gas-liquid model: By adopting a multiphase flow model and setting the mass transfer parameters of ozone, the mass exchange between bubbles and liquid in the reactor and the transfer process of ozone from the gas phase to the liquid phase are accurately simulated.

[0378] S4.52 Force Setting: By defining gas-liquid interaction forces, including drag force, virtual mass force, lift force and surface tension, the interaction forces between bubbles and liquids can be accurately simulated, thereby simulating bubble motion trajectory, size change and mass transfer process;

[0379] S4.53 Mass transfer and decay model: By employing the two-film theory and first-order reaction kinetics, the mass transfer process and decay reaction of ozone in water are accurately simulated;

[0380] S4.6 Mesh Generation and Refinement: By meshing the 3D geometric model and refining the mesh in key areas, a balance between computational accuracy and computational cost in the numerical simulation is ensured.

[0381] S4.61 Mesh Generation: The computational domain is meshed, and the mesh is refined in key areas;

[0382] S4.62 Solver Selection: Select the static segregated solver, set weak constraints, and input initial values;

[0383] S4.63 Convergence Check: By monitoring the stability of variable residuals, flow rate, and mass, determine whether the numerical simulation has converged and obtained accurate results;

[0384] S4.7 Solution and Flow Optimization: Solve the turbulence equations using a static separation solver, and optimize the structural dimensions or flow velocity based on the flow distribution analysis;

[0385] S4.71 Simulation Result Analysis: By extracting the flow pattern distribution, ozone diffusion path, and concentration distribution, we checked for dead zones, short-circuiting phenomena, and areas of uneven flow velocity.

[0386] S4.72 Geometric Adjustment: Solve dead zone or short-circuit phenomena by optimizing the system geometry;

[0387] S4.73 Optimize gas-liquid mixing: Optimize the mass transfer efficiency at the gas-liquid interface by adjusting the inlet flow rate, bubble size, and ozone component ratio;

[0388] S4.8 Subsequent Reaction Kinetics Optimization: After optimization through flow simulation, the reaction kinetic parameters are further optimized according to the process flow of the upstream ozone contact tank to ensure that the system meets the expected effluent water quality standards.

[0389] S4.81 Optimization based on reaction kinetics: By combining the simulation results of mass transfer and decay processes, the ozone dosage and bubble residence time are adjusted to optimize the reactor operating parameters;

[0390] S4.82 Generate design parameters: Determine the tank structure dimensions, equipment installation depth, inlet and outlet water flow velocities, and operating conditions;

[0391] S4.83 Generate Model Report: Summarizes the output 3D model, mesh generation scheme, flow distribution diagram and key parameters, and generates a model report;

[0392] S5. Coupling of Mechanism Model and Fluid Dynamics Model: The mechanism model described in S3. is coupled with the CFD fluid dynamics model described in S4. to form a comprehensive pre-ozone contact tank simulation model that simultaneously reflects the hydraulic flow state and chemical reaction mechanism;

[0393] S5.1 Dilute Substance Transport Module Setup: Set up dilute substance transport modules for each component in the CFD model, define the transport properties of each component, and set the reaction domain condition interface.

[0394] S5.11 Determine the content of the mechanism model: collect reaction kinetic data and confirm the degradation pathway of pollutants;

[0395] S5.12 Prepare the fluid dynamics (CFD) model: When the CFD model is completed, accurately calculate the flow distribution, gas-liquid mixing and mass transfer in the pool to ensure that the mesh is suitable for the reaction calculation;

[0396] S5.2 Coupled Reaction Rates: The mechanism model is coupled in CFD by defining and incorporating the reaction rate equations of each component into the local variables of the CFD model, and incorporating the reaction rate as a source term into the CFD solver.

[0397] S5.21 Defines mass transfer properties: By setting the liquid flow velocity field and direction in the CFD model, the convective transfer, molecular diffusion and turbulent diffusion behavior of pollutants in the liquid are defined, and the turbulent mixing coefficient is coupled through the k-ε turbulence model to further couple the convection term, diffusion term and turbulent mixing term to simulate the pollutant transport process;

[0398] S5.22 Initialize substance concentration: Set the initial concentration value of pollutant components based on experimental or monitoring data;

[0399] S5.3 Forming a complete process model: By combining hydraulic flow calculations and chemical reaction mechanisms, the reaction zone is defined, reaction kinetic equations are imported, and reaction rate parameters are set to establish a comprehensive pre-ozone contact tank simulation model;

[0400] S5.31 Define the reaction zone: Determine the extent of the liquid phase region within the tank and set the reaction zone, then define the reaction zone in the CFD model;

[0401] S5.32 Importing Reaction Kinetic Equations: Define reaction kinetic equations for COD, ammonia nitrogen, and petroleum components based on the reaction mechanism of pollutants, and input the reaction rate constants and related parameters;

[0402] S5.33 Set reaction rate parameters: Input the oxidation rate constant of ozone for COD and ammonia nitrogen based on experimental data, and set the mass transfer coefficient according to the gas-liquid mass transfer characteristics of ozone, and adjust other mass transfer coefficients to adapt to the mass transfer behavior of different pollutants.

[0403] S5.4 Coupled Mass Transfer and Reaction Process: By applying the two-film theory to define the mass transfer rate equation, setting various mass transfer conditions, and coupling the mass transfer process with the reaction rate equation, the mass conservation equation is finally solved in the CFD model.

[0404] S5.41 Apply the two-film theory to define the mass transfer rate equation: determine the values ​​of the overall mass transfer coefficient K and the bubble specific surface area a, apply the mass transfer rate equation as the boundary condition to the CFD model, and calculate and update the ozone concentration in the gas and liquid phases in real time.

[0405] The mass transfer rate equation is defined using the two-film theory as follows:

[0406] J=K··a··(C gas -C liquid )

[0407] Where K represents the overall mass transfer coefficient; a represents the specific surface area of ​​the bubble; C gas and C liquid This indicates the ozone concentration in the gas and liquid phases;

[0408] S5.42 Set mass transfer conditions: Determine bubble size, gas flow rate and liquid phase turbulent mixing intensity, and set each mass transfer condition in the CFD model. At the same time, couple the mass transfer rate equation with the reaction rate equation for calculation.

[0409] S5.43 Coupled Mass Transport and Reaction: By constructing a mass conservation equation in a CFD model, the diffusion term, turbulent mixing term and reaction rate are integrated, the diffusion coefficient and turbulent mixing coefficient are adjusted, and the results are compared with experimental data;

[0410] S5.5 Solution Model: The coupled mass transfer and reaction processes are solved using numerical methods, and the changes in pollutant concentration and flow pattern distribution are dynamically calculated.

[0411] S5.51 Setting up the Coupled Solver: Select the multiphysics coupled solver and set the calculation accuracy;

[0412] S5.52 Initialization Calculation: Input all boundary conditions, then define the convergence criteria;

[0413] S5.53 Simulation: Start the solver to perform calculations, monitor the convergence of the physical field, adjust the time step or refine the mesh to improve accuracy, and analyze the changes in flow field and pollutant concentration through post-processing results. At the same time, simulate the flow distribution and pollutant reaction process, and dynamically calculate the changes in pollutant concentration.

[0414] S5.6 Results Analysis and Optimization: Visual analysis is performed on the output coupled simulation results to analyze the simulation results, pollutant degradation pathways and reaction efficiency, identify problem areas, and then adjust the pool structure and operating conditions based on the results, and optimize through multiple iterations;

[0415] S5.61 Result Analysis and Optimization: The simulation results are exported through the post-processing module, a visualization image is generated, and the pollutant degradation rate and ozone utilization rate are calculated, thereby obtaining data on pollutant concentration distribution, ozone diffusion path and reaction efficiency, while extracting relevant key parameters.

[0416] S5.62 Analyze pollutant degradation pathways: By dividing regions, analyzing degradation rate data, and comparing the degradation of each pollutant in different regions, identify problem areas with low degradation rates, and assess flow regime and mass transfer characteristics;

[0417] S5.63 Optimized Design and Operating Parameters: Based on simulation results and degradation pathway analysis, the tank structure and operating conditions were adjusted and iteratively optimized multiple times;

[0418] S6. Steady-state simulation, sensitivity analysis and parameter correction: Perform steady-state simulation, sensitivity analysis and parameter correction on the simulation model;

[0419] S6.1 Steady-state simulation: By setting input conditions and running the steady-state solver, continuous steady-state simulation is performed to simulate the steady state of the system under continuous operation, determine whether the concentration field and flow field are stable and compare them with actual detection data, and evaluate pollutant conversion and ozone utilization efficiency.

[0420] S6.11 Determine steady-state simulation conditions: Set the influent pollutant concentration, ozone dosage, and gas-liquid mixing parameters; select the reaction mechanism model and the CFD coupling model; and check the rationality of all initial and boundary conditions.

[0421] S6.12 Simulation Run: Start the steady-state solver, monitor the convergence of key variables, observe and record the changing trends of the concentrations of each component in the system by running the steady-state solver, adjust the time step or initial conditions until the system parameters no longer change significantly, and confirm whether the model has reached steady state.

[0422] S6.13 Output steady-state results: Export steady-state calculation results through post-processing tools, extract key data of the system under steady state and visualize the data, then compare the extracted data with the design standards to conduct preliminary analysis and evaluation of the reactor performance;

[0423] S6.14 Steady-state result verification: Collect actual operating data and compare it with simulation results, analyze the source of error, adjust model parameters or initial conditions, and reset and calculate if necessary until the model results match the actual data;

[0424] S6.2 Sensitivity Analysis: For changes in influent conditions or reaction kinetic parameters, each key parameter is adjusted step by step, the sensitivity coefficient is calculated, the sensitivity diagram is plotted, the impact of different parameters on the model output is evaluated, the key parameters with the greatest impact on the model output are identified, and optimization design and correction are carried out accordingly.

[0425] S6.21 Select Analysis Parameters: List all parameters that affect the model output, filter and identify the key parameters that have a significant impact on the model's operation;

[0426] S6.22 Stepwise parameter adjustment: By adjusting each key parameter in stages, observe and record the impact of each key parameter on the model output data before and after adjustment, analyze the impact of each parameter on pollutant removal rate and ozone depletion, and identify sensitive areas of system response;

[0427] S6.23 Calculate the sensitivity coefficient: Quantify the sensitivity of the model output to parameter changes through normalized index, and calculate the sensitivity coefficient of the model output for each parameter;

[0428] The specific formula for the sensitivity coefficient is as follows:

[0429]

[0430] Where S represents the sensitivity coefficient; ΔR represents the output change; ΔP represents the parameter change; P represents the parameter value; and R represents the output value.

[0431] S6.24 Drawing Sensitivity Plots: Using data analysis tools to draw visualizations to help identify the key parameters that have the greatest impact on model output;

[0432] S6.25 Determine sensitive parameters: Based on the sensitivity analysis results, identify the key parameters that have the greatest impact on the model output, i.e., the sensitive parameters;

[0433] S6.3 Parameter Correction: Based on actual operating data, fine-tune the key parameters, including kinetic and mass transfer parameters, until the model simulation results match the actual situation.

[0434] S6.31 Determine the parameters to be corrected: Prioritize correcting sensitive parameters that have a significant impact on the model output, and correct deviation parameters under actual operating conditions;

[0435] S6.32 Parameter Adjustment Method: Fine-tune the dynamic parameters and mass transfer parameters in the model using actual operating data to make the simulation results gradually approach the actual operating data;

[0436] S6.33 Re-simulation and verification: By adjusting the parameters and rerunning the model, compare the steady-state results with the actual data, analyze the deviations and continue to adjust until the simulation results are consistent with the expected requirements;

[0437] S6.4 Model Stability Verification: Through multi-condition testing and convergence analysis, the model is ensured to have good adaptability, prediction accuracy, and stable convergence under different conditions.

[0438] S6.41 Multi-condition simulation: Simulation is performed by setting multiple different operating conditions, comparing the simulation results with actual data under each condition, and analyzing the accuracy and stability of the model under different conditions;

[0439] S6.42 Convergence Analysis: Set convergence criteria, monitor residual changes and trends during iteration, and adjust solver parameters as necessary to ensure that the model converges stably to a stable solution within a reasonable time and computational step size;

[0440] S7. Model self-learning and self-calibration and output of optimal ozone control: Using the self-learning and self-calibration mechanism, the optimal ozone dosing control strategy of the front ozone contact tank is output.

[0441] S7.1RBF Neural Network Adaptive Self-Learning: An adaptive self-learning module is constructed using an RBF neural network to randomly transform the collected influent parameters and iteratively calculate them repeatedly, recording and saving the optimal ozone dosage or optimal operating parameters.

[0442] S7.11 Data Preparation: Collect actual running data, clean and remove outlier data, define model input and output parameters, organize historical running data to establish the mapping relationship between input and output, and further construct training and validation datasets;

[0443] S7.12 Self-learning Framework Establishment: Construct an RBF neural network, define the parameters of the input and output layers, train the model using a training dataset, optimize the weights and activation functions, and optimize the optimal ozone dosage and removal efficiency through nonlinear mapping;

[0444] S7.13 Initialize weights and parameters: Randomly initialize the weights, center and width parameters of the RBF neural network, set the learning rate and objective function, and use the backpropagation algorithm to optimize the network parameters to minimize the error and gradually approach the optimal solution;

[0445] S7.2 Self-correction: Randomly transform sensitive parameters within a given range, continuously iterate and calculate, and replace old values ​​with better-performing parameters to optimize and self-correct the parameter settings within the model;

[0446] S7.21 Input data perturbation: Randomly perturb the influent parameters to simulate fluctuations in the influent parameters and generate multiple sample points. Then run the model and record the output results.

[0447] S7.22 Multiple Iterative Optimization: Perform multiple iterative calculations and adjust model parameters for each set of perturbation data to find the optimal ozone dosage;

[0448] S7.23 Optimal value storage: The calculated optimal ozone dosage and corresponding influent conditions are organized and stored in the database, and an index and retrieval mechanism are established so that the stored optimal solution can be called first under similar influent conditions;

[0449] S7.3 Self-calibration process: By selecting sensitive parameters and subjecting them to random perturbations, the optimal parameter combination is repeatedly sought to optimize the model's prediction accuracy and performance;

[0450] S7.31 Sensitive Parameter Selection: Based on the sensitivity analysis results, select the key parameters that have the greatest impact on the model output;

[0451] S7.32 Randomly Varying Parameters: Determine the variation range of each sensitive parameter, use a random number generator to perturb the sensitive parameters, generate multiple perturbed parameter values, and synthesize multiple parameter combinations;

[0452] S7.33 Finding the optimal value: Run the model for each set of parameters, calculate and record the pollutant removal efficiency, compare the results of all combinations, and select the best-performing parameter combination as the basis for the next parameter update.

[0453] S7.34 Parameter Update: Updates the optimal parameter values ​​to the model, replacing the original parameter settings;

[0454] S7.4 Model Validation and Adjustment: By validating the accuracy and adaptability of the model's predictions, errors are identified and adjusted.

[0455] S7.41 Model Validation: Compare the model's predictions with the actual data to verify the model's accuracy and adaptability;

[0456] S7.5 Output Optimal Ozone Control: By adjusting the structure and optimization algorithm of the RBF neural network, the deviation between the model output and the actual data is reduced;

[0457] S7.51 Optimal Value Output: Based on the self-learning and self-correcting model, calculate and output the optimal ozone dosage and corresponding operation strategy under the current influent conditions;

[0458] S7.52 Control System Feedback: The optimal ozone dosage output by the model is sent to the controller, and the ozone dosage is adjusted according to the actual operating effect;

[0459] S7.53 Dynamic Optimization: By monitoring influent parameters in real time and calling the model for calculation, the ozone dosage is dynamically adjusted;

[0460] S7.6 Result Storage and Application: Stores the optimal value and related data for each run;

[0461] S7.61 Result Storage: By storing the optimal value and related data from each run into the database, a complete historical dataset is formed;

[0462] S7.62 Empirical Rule Update: Updates existing empirical rules based on model output and runtime data.

[0463] The present invention describes a model building method for an ozone contact tank before advanced oxidation of petroleum wastewater. Through the linkage of multiple steps, the entire system starts with detailed analysis of influent components and reaction kinetics, combines flow simulation and chemical reaction coupling, and finally achieves self-learning and self-correction with the help of intelligent algorithms. This enables dynamic optimization of ozone dosing control strategies to ensure maximum treatment efficiency and resource utilization.

[0464] Example 2: Specific Implementation of "S1. Analyzing Influent Components" in the Technical Solution of the Present Invention

[0465] This embodiment provides a detailed explanation of step S1, analyzing the influent components, in the model building method for an ozone contact tank before advanced oxidation of petroleum wastewater according to the present invention. "S1. Analyzing the influent components" serves as a data reserve for accurately establishing the ozone oxidation and flow field coupling model; the final product is a complete and detailed quantitative description of the composition and concentration of pollutants in the raw water. It is precisely the grouping and measurement results obtained in this step that enable subsequent model construction, parameter sensitivity analysis, and self-learning optimization.

[0466] The core function of the pre-ozone contact tank in this technical solution is to utilize ozone to partially oxidize or degrade complex organic matter and other pollutants in wastewater, thereby improving the removal efficiency of subsequent treatment units, such as the biological treatment tank. Because petroleum-based wastewater contains a wide variety of pollutants with significantly different properties, their respective contents, forms, and molecular structures must be determined before they enter the pre-ozone contact tank. Only through detailed classification and quantitative analysis of the pollutants in the influent can an accurate ozone oxidation mechanism model be established, providing a scientific basis for "how to add ozone, how much to add, and under what reaction conditions."

[0467] Furthermore, the specific implementation method of "S1. Analyzing the influent components" in the model construction method of the advanced oxidation pre-ozone contact tank for petroleum wastewater described in this invention is as follows:

[0468] S1.1 Sampling and Detection: Collect raw petroleum wastewater samples from sampling points, and then use different experimental detection methods to determine different pollutants in the influent;

[0469] S1.11 Determine the sampling method: Collect uniformly mixed wastewater samples from representative sampling points, and determine an appropriate sampling volume according to the testing requirements to ensure that the samples can accurately reflect the influent water quality;

[0470] The determination of the sampling method requires the selection of representative sampling points, such as collecting wastewater from the inlet pipe or inlet of the ozone contact tank. During sampling, obvious stratification or deviation in the sample should be avoided to ensure thorough mixing. The sampling volume is determined according to the test items. A 5L sample is suitable for comprehensive analysis.

[0471] The sampling and testing involved taking samples at the oil wastewater collection point of the sewage treatment plant or the corresponding industrial park, bringing the collected water samples back to the laboratory, and using various analytical methods, such as COD determination, total nitrogen or various forms of nitrogen determination, total phosphorus or phosphate determination, infrared spectroscopy or chromatography to identify petroleum, and detecting the concentration and properties of each pollutant.

[0472] The sampling method involves selecting representative sampling points and collecting wastewater samples from the inlet pipe or inlet of the ozone contact tank. During sampling, uniform mixing is ensured to avoid local concentration deviations, and the sampling volume is determined according to the detection requirements, such as 5L of sample for comprehensive detection.

[0473] Specifically, the sampling point selection refers to choosing representative sampling points at the inlet pipe or inlet of the ozone contact tank to ensure that the samples can accurately reflect the overall influent water quality; the uniform mixing refers to ensuring that the water samples are thoroughly mixed during sampling to prevent data deviation due to local concentration differences; the sampling volume determination is based on the needs of subsequent tests, such as collecting approximately 5L of sample for comprehensive testing, and thus determining an appropriate sampling volume.

[0474] The representativeness and uniformity of the sampling are the foundation for ensuring the accuracy of subsequent data. Only by obtaining uniform samples from the correct sampling points can the actual concentration of each component in the wastewater be accurately reflected, providing a reliable basis for subsequent chemical and biological testing. To ensure that the samples can accurately represent the water quality status of the entire pool, the selection of sampling points must be able to cover various fluctuations in the influent, while mixing ensures the uniformity of the collected water samples and avoids the influence of local pollutant concentrations on the results.

[0475] S1.12 Sample Preservation: By employing specific preservation measures for different components, such as refrigeration, use of fixatives, and sealed preservation, we ensure that the collected samples remain stable in composition during analysis, unaffected by microbial activity or chemical reactions.

[0476] The sample preservation is divided into COD, nitrogen, phosphorus components, microbial components, and petroleum components;

[0477] The COD, nitrogen, and phosphorus components should be stored at 4°C to reduce microbial activity and prevent degradation of COD, nitrogen, and phosphorus during storage. The microbial components should be analyzed as soon as possible after sampling, or preserved with a fixative (such as formaldehyde) to prevent microbial growth or death, which could affect the analytical results. The petroleum components should be sealed in uncontaminated glass bottles to avoid the effects of sunlight and air, thus preventing volatilization and degradation.

[0478] The purpose of sample preservation is to prevent changes in the collected samples due to microbial activity or chemical reactions, and to ensure that the samples remain stable during analysis; refrigeration can slow down the microbial degradation process and reduce the rate of chemical reactions, ensuring that the components in the sample remain stable; fixatives are used to maintain the state of the microorganisms and avoid the influence of the external environment on them.

[0479] S1.2 The influent components are divided into four categories: COD components, nitrogen components, phosphorus components, and petroleum components;

[0480] The COD components include particulate biodegradable components, particulate non-biodegradable components, dissolved biodegradable components, and dissolved non-biodegradable components; the nitrogen components include nitrate nitrogen, nitrite nitrogen, ammonia nitrogen, and Kjeldahl nitrogen; the phosphorus components include dissolved total phosphorus and phosphate; and the petroleum components are mainly weakly polar alkanes, cycloalkanes, and aromatic hydrocarbons.

[0481] S1.21 COD Component Analysis: By measuring total COD, particulate and dissolved COD, biodegradable and non-biodegradable COD, and types of organic matter, the organic pollutant load and degradation characteristics in wastewater are assessed, providing data support for subsequent treatment processes.

[0482] COD is a key indicator for assessing the degree of organic pollution in wastewater. Measuring the total chemical oxygen demand (COD) in a water sample reflects the total amount of organic matter in the wastewater and is used to assess the self-purification capacity of the water body.

[0483] S1.211 Total COD determination: The total COD concentration was determined using the potassium dichromate method or spectrophotometry. Both methods can provide overall data on organic load, laying the foundation for judging the difficulty of subsequent treatment.

[0484] The procedure for determining total COD is either the potassium dichromate method or the spectrophotometric method.

[0485] The potassium dichromate method involves reacting potassium dichromate, a strong oxidant, with organic matter and measuring the amount of oxidation consumed to reflect the content of organic matter in the water. The potassium dichromate method utilizes a strong oxidant to oxidize organic matter in the water under high temperature conditions, consuming a certain amount of oxidant, and the amount of consumption is directly proportional to the content of organic matter.

[0486] The spectrophotometric method is based on the relationship between absorbance and concentration, determining COD colorimetrically; the spectrophotometric method is based on the absorbance characteristics of organic matter, quantifying it colorimetrically. Both methods can provide overall data on organic load, laying the foundation for assessing the difficulty of subsequent treatment.

[0487] S1.212 Separation of particulate and dissolved COD: Dissolved and particulate organic matter in the sample are separated using a 0.45μm filter membrane, and their COD is measured separately to distinguish components with different biodegradability.

[0488] The separation of particulate and dissolved COD requires filtering the sample using a 0.45μm filter membrane. The filtrate then contains dissolved organic matter, i.e., dissolved COD, and its concentration is measured. The filter residue represents particulate organic matter, which is then properly dissolved before its COD is measured.

[0489] The distinction between the particulate biodegradable and non-biodegradable components determines whether the particulate matter can be digested or precipitated and removed by microorganisms during subsequent treatment. The particulate organic matter is relatively difficult to remove through biodegradation, while the dissolved organic matter is relatively easy to degrade. Distinguishing between these two parts can help determine which part needs to be pre-treated by ozone oxidation, thereby optimizing the subsequent process design.

[0490] S1.213 Differentiation between biodegradable and non-biodegradable COD: By comparing the COD values ​​of samples that have not been treated with aerobic microbial degradation, the proportion of easily degradable and difficult-to-degrade organic matter in wastewater is quantitatively analyzed, providing a basis for the selection of pretreatment schemes (such as ozone oxidation);

[0491] To distinguish between biodegradable and non-biodegradable COD, the samples need to be divided into two groups: one group is directly measured for COD, and the other group is measured for COD after being treated with aerobic microbial degradation. The difference between the two groups is the biodegradable COD.

[0492] The presence of dissolved biodegradable and dissolved non-biodegradable components determines that ozone is more likely to react with dissolved components or be removed by microorganisms in subsequent biochemical treatment. Aerobic microorganisms can degrade usable organic matter. Through this comparative method, the ratio of easily degradable to difficult-to-degrade organic matter in wastewater can be quantitatively analyzed, providing a basis for selecting appropriate pretreatment schemes, such as ozone oxidation.

[0493] S1.214 Organic matter type analysis: The main organic matter in the wastewater is separated, qualitatively and quantitatively analyzed by gas chromatography-mass spectrometry, thereby revealing the type and concentration of each component and providing data support for optimizing pretreatment conditions (such as ozone oxidation);

[0494] The analysis of organic compounds involves using gas chromatography-mass spectrometry (GC-MS) to analyze the types and concentrations of major organic compounds, and to perform separation, qualitative and quantitative analysis of information such as fatty acids, phenols, and alcohols.

[0495] The GC-MS technology can accurately separate organic components in complex mixtures and provide molecular structure information. Through detailed analysis of the types of organic matter, it is possible to further determine which components have a greater impact on the processing technology, thereby optimizing the pretreatment conditions. Further subdividing these components helps to determine which parts most need to be pre-oxidized by ozone to destroy the structure of macromolecules or recalcitrant organic matter and improve their biochemical properties.

[0496] S1.22: Nitrogen component analysis: By measuring the concentrations of total nitrogen, ammonia nitrogen, nitrate nitrogen, and nitrite nitrogen in wastewater, the distribution of nitrogen speciation is analyzed to assess the nitrogen pollution level, nitrogen conversion process, and the need for subsequent denitrification treatment;

[0497] S1.221 Total Nitrogen Determination: The total nitrogen content in wastewater is determined by using a total nitrogen analyzer or ultraviolet spectrophotometry to assess the overall level of nitrogen pollution and its potential impact on eutrophication of water bodies.

[0498] The determination of total nitrogen requires the use of a total nitrogen analyzer or ultraviolet spectrophotometry to determine the total nitrogen content in the influent.

[0499] The total nitrogen determination is performed using a total nitrogen analyzer or ultraviolet spectrophotometry to measure the total nitrogen in wastewater. The total nitrogen value reflects the overall level of nitrogen pollution in wastewater and is an important indicator for assessing nitrogen load and its impact on eutrophication of water bodies.

[0500] S1.222 Ammonia Nitrogen Analysis: The concentration of ammonia nitrogen in wastewater is determined by Nessler's reagent colorimetric method or ion-selective electrode method to reflect the toxicity level of ammonia nitrogen concentration and the requirements for subsequent biochemical treatment.

[0501] The ammonia nitrogen analysis is performed by using Nessler's reagent colorimetric method or ion-selective electrode method to determine the concentration of ammonia nitrogen. Ammonia nitrogen is a common and highly toxic form of nitrogen in wastewater, and its determination helps to determine the direct toxicity of wastewater and the needs of subsequent biochemical treatment.

[0502] S1.223 Nitrate and Nitrite Nitrogen Analysis: Nitrate nitrogen and nitrite nitrogen in wastewater are quantitatively determined by ion chromatography or spectrophotometry to reveal the oxidation state and transformation process of nitrogen and provide a basis for denitrification treatment;

[0503] The analysis of nitrate nitrogen and nitrite nitrogen uses ion chromatography or spectrophotometry to quantitatively determine nitrate nitrogen and nitrite nitrogen. Nitrate nitrogen and nitrite nitrogen represent different nitrogen oxidation states, and their distribution helps to analyze the nitrogen transformation process in wastewater and guide subsequent denitrification treatment.

[0504] S1.224 Nitrogen Component Distribution: By calculating the proportion of each nitrogen form in the total nitrogen and drawing a nitrogen distribution map, the source, transformation and removal difficulty of different nitrogen forms in wastewater are clarified, thereby supporting the design of subsequent denitrification processes;

[0505] The nitrogen component distribution is calculated based on the above data to determine the proportion of nitrogen components in total nitrogen and to clarify the distribution of nitrogen forms.

[0506] The nitrogen component distribution is calculated based on the detection results in 1.31-1.33. The proportion of each nitrogen form in the total nitrogen is calculated, a nitrogen distribution map is drawn, and the analysis of the proportion of different nitrogen forms can help determine the source, transformation and removal difficulty of nitrogen in wastewater, and provide data support for the design of subsequent processes, such as biological denitrification.

[0507] The nitrogen content is an important indicator for measuring wastewater eutrophication and subsequent nitrogen and phosphorus removal. Different forms of nitrogen, such as ammonia nitrogen, nitrate nitrogen, nitrite nitrogen, and Kjeldahl nitrogen, behave differently in ozone oxidation or subsequent biochemical treatment, and it is necessary to understand their distribution and concentration beforehand.

[0508] S1.23 Phosphorus Component Analysis: By measuring the concentrations of total phosphorus, orthophosphate, particulate and dissolved phosphorus, and organic phosphorus, the phosphorus pollution level in wastewater is assessed, providing data support for the design of subsequent phosphorus removal processes;

[0509] S1.231 Determination of total phosphorus (TP): The total phosphorus content is determined using the molybdenum blue method or a total phosphorus analyzer;

[0510] The phosphorus content is an important indicator for measuring wastewater eutrophication and subsequent nitrogen and phosphorus removal.

[0511] The total phosphorus (TP) content in the wastewater is determined by the molybdenum blue method or by a dedicated total phosphorus analyzer. The total phosphorus content is an important indicator for measuring the eutrophication potential of wastewater and is directly related to the ecological safety of the water body and the design of subsequent phosphorus removal processes.

[0512] S1.232 Orthophosphate Analysis: Orthophosphate concentration was determined by ammonium molybdate method or ion chromatography;

[0513] The orthophosphate analysis utilizes the ammonium molybdate method or ion chromatography to determine the concentration of orthophosphate. Orthophosphate is the most readily available and removable form of phosphorus in wastewater, and its accurate determination helps to assess the feasibility of phosphorus removal processes.

[0514] S1.233 Separation of particulate and dissolved phosphorus: The sample was filtered using a 0.45 μm filter membrane, and the dissolved phosphorus in the filtrate and the particulate phosphorus in the filter residue were measured.

[0515] The separation of particulate and dissolved phosphorus is achieved by filtering the sample through a 0.45 μm filter membrane, collecting the dissolved and particulate phosphorus separately, and measuring them separately. At this time, particulate phosphorus is often bound to solid particles and is difficult to remove directly by biological or chemical methods; while dissolved phosphorus is more easily absorbed or precipitated. Separating these two types of phosphorus helps to design targeted removal processes.

[0516] S1.234 Organic phosphorus analysis: Organic phosphorus is converted into inorganic phosphorus using acid hydrolysis or heating digestion methods, and then the concentration is determined;

[0517] The organic phosphorus analysis employs acid hydrolysis or thermal digestion to convert organic phosphorus into inorganic phosphorus before measurement. After conversion into inorganic phosphorus, organic phosphorus can be detected using conventional methods, thus providing a complete evaluation of phosphorus pollution in wastewater and avoiding the omission of some phosphorus components.

[0518] S1.24 Petroleum Component Analysis: By determining the concentration of total petroleum substances, separating and analyzing specific components (such as alkanes, cycloalkanes, and aromatics) and their ozone requirements, data support is provided for the design of ozone oxidation pretreatment and subsequent process optimization.

[0519] S1.241 Determination of total petroleum substances: The total concentration of petroleum substances in wastewater shall be determined by infrared photometry or ultraviolet spectrophotometry;

[0520] The determination of total petroleum substances involves using infrared spectrophotometry or ultraviolet spectrophotometry to determine the total amount of petroleum substances in the sample. The total petroleum substance content is an important parameter for evaluating recalcitrant organic pollutants in wastewater and directly affects the design of ozone oxidation pretreatment.

[0521] S1.242 Component Separation and Qualitative Analysis: Petroleum-based substances are enriched by liquid-liquid extraction and their specific components are analyzed by gas chromatography or GC-MS to reveal their composition and content distribution, providing a basis for determining ozone demand.

[0522] The separation and qualitative analysis of the components require the use of liquid-liquid extraction to enrich petroleum substances in the sample into the organic phase; gas chromatography (GC) or gas chromatography-mass spectrometry (GC-MS) is used to analyze the specific components of the petroleum substances, such as alkanes, cycloalkanes and aromatics.

[0523] The component separation and qualitative analysis uses liquid-liquid extraction to enrich petroleum substances from the aqueous phase to the organic phase. Subsequently, gas chromatography (GC) or GC-MS is used to perform qualitative and quantitative analysis on alkanes, cycloalkanes and aromatics in the extract. This can reveal the specific composition and content distribution of petroleum substances and provide a basis for determining which types of organic compounds have a higher ozone demand.

[0524] S1.243 Assessment of ozone demand from petroleum products: Based on the chemical structure and content of petroleum products, calculate the ozone demand from petroleum products to provide a basis for the design of subsequent ozone dosage.

[0525] The assessment of the ozone requirement of petroleum compounds, based on the content and chemical structure of each component obtained in 1.62, calculates the amount of ozone required for these substances in the ozone oxidation reaction. The assessment results help determine the ozone dosage and reaction conditions, ensuring that the pretreatment stage can effectively destroy recalcitrant petroleum organic matter, thereby improving the overall efficiency of subsequent processes.

[0526] The petrochemical wastewater contains weakly polar organic compounds such as alkanes, cycloalkanes, and aromatic hydrocarbons. These components are often difficult to degrade directly by ordinary biochemical processes, but under the strong oxidizing effect of ozone, chain cleavage and aromatic ring opening oxidation processes can occur, thereby improving biodegradability.

[0527] S1.3 Microbial component test: Analysis of microbial communities such as autotrophic bacteria and aerobic bacteria;

[0528] S1.31 Total bacterial concentration determination: The total concentration of microorganisms in the sample is determined by microscopic counting or flow cytometry;

[0529] The total bacterial concentration determination is a direct measurement of the total concentration of microorganisms, such as bacteria and fungi, in a sample using microscopic counting or flow cytometry. The total bacterial concentration reflects the biomass of microorganisms in wastewater and helps to understand the background microbial ecological status of the system and its potential contribution to the degradation of organic matter.

[0530] S1.32 Population composition analysis: Microbial population composition, including autotrophic and heterotrophic bacteria, was analyzed using 16S rRNA gene sequencing or fluorescence in situ hybridization (FISH) technology.

[0531] The population composition analysis uses 16S rRNA gene sequencing or fluorescence in situ hybridization (FISH) technology to analyze the microbial population composition in the sample, including the ratio of autotrophic and heterotrophic bacteria. The microbial community structure determines the degradation capacity and stability of the biological treatment stage. Analyzing the population composition helps to determine whether the treatment process needs to be adjusted or specific bacterial species need to be inoculated.

[0532] S1.33 Activity assay: Use activity staining methods, such as FDA staining, to assess the bioactivity of microorganisms;

[0533] The activity assay uses an activity staining method, such as FDA staining, to detect the metabolic activity of microorganisms in the sample and assess whether the cells are in a healthy state. The activity assay not only reflects the quantity but also reveals whether the microorganisms have sufficient biological activity, thereby playing a positive role in the degradation of organic matter.

[0534] S1.34 Bioload Assessment: Calculate the bioload level in wastewater based on microbial concentration and population structure;

[0535] The bioburden assessment calculates the bioburden level in wastewater based on the concentration and population structure of microorganisms, assesses whether there is excessive biological growth or imbalance, and the bioburden information can help determine the self-cleaning capacity of the existing system, while providing a basis for subsequent design to prevent bioclogging or excessive deposition.

[0536] If there is a certain microbial community in the pre-ozone contact tank or in the influent itself, autotrophic bacteria and aerobic bacteria will also participate in part of the biodegradation process. Although the pre-ozone contact tank is mainly chemically oxidized, a preliminary understanding of the microbial components will help in the subsequent connection with the biochemical section.

[0537] S1.4 Determine the final influent component list: Based on the experimental measurement results, confirm the specific influent components and the initial concentration levels of various pollutants in the pre-ozone contact tank;

[0538] S1.41 Summary of test results: Compile the test results of COD, nitrogen, phosphorus, microorganisms and petroleum components, and construct a comprehensive spectrum of influent components;

[0539] The summarized test results, including the test data of COD, nitrogen, phosphorus, microorganisms, and petroleum, are organized to construct a complete influent component map. By summarizing the data, the concentration and proportion of each major pollutant in the wastewater can be intuitively understood, providing a basis for overall water quality assessment.

[0540] S1.42 Component Correlation Analysis: By analyzing the interrelationships between different pollutants (such as COD, nitrogen, phosphorus, petroleum substances, etc.), the intrinsic connections are revealed, providing a basis for subsequent experimental simulations and numerical model construction;

[0541] The component correlation analysis requires combining detection data to analyze the relationship between COD and nitrogen, phosphorus and petroleum substances; such as the ratio of non-biodegradable COD to petroleum substances, and the correlation between nitrogen components and microbial populations.

[0542] The component correlation analysis analyzes the correlation between different detection data, such as the ratio of non-biodegradable COD to petroleum substances, and the relationship between nitrogen components and microbial population structure. Then, statistical analysis methods, such as correlation analysis and regression analysis, are used to reveal the intrinsic connections between each component. Component correlation analysis helps to clarify the mutual influence and potential transformation pathways between pollutants, and thus provides guidance for subsequent experimental simulation and numerical model construction.

[0543] S1.43 Preliminary assessment of water quality characteristics: Based on the component data, assess the main pollution characteristics of the wastewater to provide guidance for subsequent experimental simulation and model construction;

[0544] The preliminary assessment of water quality characteristics, based on the summarized data, evaluates the main pollution characteristics of wastewater, such as high COD load, nitrogen and phosphorus ratio, and petroleum content, and proposes preliminary improvement suggestions for subsequent treatment processes, such as ozone pretreatment and biological treatment. The preliminary assessment results provide direction for the design of pretreatment processes, enabling subsequent experimental simulations, mechanism modeling, and numerical calculations to be carried out more effectively.

[0545] The data processing and classification involves organizing the sampled test data and classifying them according to the nature of the pollutants: COD components, such as particulate or dissolved, degradable or non-degradable, nitrogen components, phosphorus components, and petroleum components. If subsequent tests indicate the presence of microorganisms in the water, such as autotrophic bacteria and aerobic bacteria, the concentration and types of microorganisms can be further statistically analyzed as needed.

[0546] Through detailed operation and analysis of each of the above sub-steps, we can gain a comprehensive understanding of the composition of the influent to the pre-ozone contact tank, from sampling, separation, detection to data comprehensive evaluation. This not only provides accurate parameter input for subsequent process simulation and optimization, but also helps engineers better understand the characteristics of various pollutants in the wastewater, ensuring that a scientific and reasonable ozone addition and treatment plan can be formulated in the end.

[0547] Ultimately, the results of "S1. Analysis of Influent Components" are as follows: First, a list of influent components and their concentration ranges are generated, clearly recording the concentrations of components such as "particulate biodegradable COD," "dissolved aromatic hydrocarbons," "ammonia nitrogen," and "phosphate." Second, a preliminary property assessment is conducted if necessary. For example, if the concentration of certain aromatic hydrocarbons is too high, it indicates that the ozone dosage in the pre-ozone contact tank needs to be increased. If the ammonia nitrogen content is too high, it is necessary to consider combining it with the denitrification process. These detection and analysis results will become the input data for subsequent steps, such as experimental simulation, mechanism modeling, and CFD coupling.

[0548] Furthermore, the significant milestone of "S1. Analysis of Influent Components" is that by systematically detecting and classifying the "influent components," a set of accurate and quantifiable multi-component influent basic data is generated. This provides the initial information on the substances to be treated for subsequent ozone oxidation models, laying the foundation for control strategies. Specifically, it determines which substances are most susceptible to ozone attack, which are difficult to degrade, and whether auxiliary chemical or biological treatment is needed. Thus, the model's clear influent water quality characteristics can help determine the appropriate dosing strategy for the pre-ozone contact tank and its impact on subsequent processes. Mechanistically, "S1. Analysis of Influent Components" provides data reserves for accurately establishing a coupled ozone oxidation and flow field model; the final product is a complete and detailed quantitative description of the composition and concentration of pollutants in the raw water. It is precisely the grouping and measurement results obtained from "S1. Analysis of Influent Components" that enable the subsequent model construction, parameter sensitivity analysis, and self-learning optimization to be further realized.

[0549] Example 3: Specific Implementation of "S2. Experimental Method for Simulating the Reaction Process of the Pre-Ozone Contact Tank" in the Technical Solution of the Present Invention

[0550] This embodiment provides a detailed explanation of S2. Experimental simulation of the reaction process in the pre-ozone contact tank of an advanced oxidation process for petroleum wastewater, as described in this invention. "S2. Experimental simulation of the reaction process in the pre-ozone contact tank" involves simulating the reaction process in the pre-ozone contact tank through experiments. The purpose is to understand the reaction processes of various components in the pre-ozone contact tank and the degradation effect of ozone on pollutants.

[0551] The core of the "S2. Experimental Simulation of the Reaction Process of the Pre-Ozone Contact Tank" in the technical solution of this invention lies in revealing the specific reaction mechanism of ozone in treating petroleum wastewater through simulation experiments, analyzing the degradation pathways and products of pollutants, and determining the corresponding reaction kinetic parameters, thus providing theoretical support for optimizing the design and operation of the pre-ozone contact tank. Specifically, the entire experimental process involves building a model similar in structure to the actual pre-ozone contact tank and preparing an influent sample matching the petroleum wastewater. Using an ozone generation system and online monitoring equipment, the system simulates the contact reaction between ozone and various components in the wastewater. Through time-segmented sampling, concentration measurement, and GC-MS structural analysis, the degradation pathways of each pollutant and the formation rules of intermediate products are clarified. Based on the collected data, a reaction kinetic model is established, and rate constants and mass transfer parameters are extracted, thereby comprehensively revealing the oxidation mechanism of ozone in the wastewater treatment process and providing a basis for optimizing process parameters. The entire process focuses on both the rigorous implementation of operational methods and the in-depth analysis of reaction mechanisms, providing scientific support for practical engineering applications.

[0552] Furthermore, the specific implementation method of the model construction method for the pre-ozone contact tank of advanced oxidation of petroleum wastewater described in this invention, "S2. Experimental simulation of the reaction process of the pre-ozone contact tank", is as follows:

[0553] S2.1 Construction of small-scale test device: Based on the size of the pool in the CAD drawings, a small-scale pre-ozone contact pool is customized to scale. Then, ozone dosing is arranged in the small contact pool of the laboratory according to the dosing method of the ozone controller at the sampling location.

[0554] The experimental model is designed and constructed according to the actual ozone contact pool size ratio (e.g., 1:10 or 1:20), using transparent or actual materials such as glass, plastic, and concrete to build a small experimental device; it includes an inlet, an outlet, and an ozone gas dosing port to ensure that the structure is consistent with the actual pool; and it is equipped with a gas-liquid mixing system, such as a microporous diffuser or a mechanical stirrer, to simulate the mixing conditions in the actual process.

[0555] The construction of the small-scale experimental device is achieved by building a small pre-ozone contact tank according to CAD drawings. The purpose is to reproduce the reaction environment under actual working conditions. That is, by precisely controlling the size of the tank, the shape of the reactor and the ozone addition method, the reaction conditions in the actual treatment process can be simulated. At this time, the ozone addition method needs to be matched with the addition method in the actual sewage treatment process to ensure the comparability of experimental conditions.

[0556] The structural design of the aforementioned small-scale experimental device includes inlet and outlet ports to ensure that the water flow paths into and out of the experimental pool are consistent with actual working conditions; the ozone gas dosing port is designed with the same dosing method as the on-site ozone controller to ensure uniform ozone distribution; the gas-liquid mixing system can use a microporous diffuser or a mechanical stirrer to ensure full contact between ozone and wastewater, simulating the actual mixing effect.

[0557] S2.2 Small-scale test water configuration: Based on the components and concentrations in the petroleum wastewater, water is allocated according to the actual concentration range to ensure that the composition of the influent to the small pre-ozone contact tank matches the actual working conditions as closely as possible.

[0558] The influent configuration for the small-scale experiment is designed to ensure the representativeness of the experimental data. The composition of the influent to the small-scale pre-ozone contact tank needs to be as consistent as possible with the composition of the actual wastewater. In actual operation, the components of petroleum wastewater include various organics, oils, suspended solids, and heavy metals. Furthermore, the preparation of the experimental water needs to be based on the chemical composition and concentration of the actual water sample to ensure that the simulated influent composition matches the actual working conditions as closely as possible, thus helping to reveal the specific role of ozone in actual wastewater treatment.

[0559] The experimental model is designed according to the actual size ratio of the front ozone contact tank, such as 1:10 or 1:20, using transparent or actual engineering materials, such as glass, plastic or concrete, to build a small experimental device. The device should have the same structure as the actual equipment, including the inlet, outlet and ozone gas dosing port.

[0560] The gas-liquid mixing system is equipped with a microporous diffuser or a mechanical stirrer to ensure that ozone gas is fully dispersed in water, simulating the mixing conditions in actual processes;

[0561] The operation method is as follows: First, a model is customized according to the actual tank size and design requirements to ensure that the model's geometry, flow field, and interfaces are consistent with the actual contact tank; then, materials suitable for the chemical properties and corrosion resistance requirements of ozonation are selected to build a transparent or semi-transparent model for observing the reaction process; finally, a microporous diffuser or stirrer is fixed in the device, and the mixing effect is adjusted through pre-experimentation to ensure uniform bubble distribution.

[0562] The structure and mass transfer coupling in the influent configuration of the small-scale test is a model that allows ozone to be evenly distributed in the water, forming tiny bubbles. The large specific surface area of ​​the bubbles facilitates the transfer of ozone from the gas phase to the liquid phase, thereby causing an oxidation reaction with pollutants in the water.

[0563] The gas-liquid contact efficiency in the influent configuration of the pilot test is achieved by simulating the mixing conditions of actual working conditions to improve the contact efficiency between ozone and components in the water, thereby enhancing the subsequent oxidation reaction rate.

[0564] S2.21 Preparation of influent sample: Based on the sampling results and actual wastewater characteristics, prepare simulated influent according to the proportion;

[0565] The formulation includes COD components, nitrogen compounds, phosphorus compounds, and petroleum compounds; the COD components are specifically for particulate and dissolved organic matter; the nitrogen compounds are mainly ammonia nitrogen, nitrate nitrogen, and nitrite nitrogen; the phosphorus compounds are mainly total phosphorus and orthophosphate; and the petroleum compounds include alkanes, cycloalkanes, and aromatic hydrocarbons.

[0566] If microbial action is involved, microorganisms can be extracted from actual water samples and added to experimental samples to ensure that biological and chemical reactions proceed in synergy.

[0567] S2.22 Ozone Generation System Configuration: Install the ozone generator and calibrate its output concentration and flow rate; determine the ozone dosing method, such as continuous dosing, intermittent dosing, and dosing range;

[0568] The ozone generation system is configured by installing an ozone generator, calibrating the output concentration and flow rate, ensuring that the addition method (continuous or intermittent addition) is consistent with the actual working conditions, and then determining the range of ozone addition amount so as to adjust it at different experimental stages and study its effect on reaction rate and degradation efficiency.

[0569] S2.23 Monitoring Equipment Preparation: Configure online monitoring instruments or laboratory equipment, including COD analyzer, ammonia nitrogen, nitrate nitrogen, total phosphorus analyzer, GC-MS gas chromatography-mass spectrometry instrument for detecting chemical structure changes, pH meter, dissolved oxygen meter for detecting chemical structure changes and changes in the concentration of each component;

[0570] The preparation of the monitoring equipment involves configuring online monitoring instruments and laboratory analytical equipment, including water quality analysis instruments, chemical structure detection instruments, and auxiliary instruments. The water quality analysis instruments include COD analyzers, ammonia nitrogen analyzers, nitrate nitrogen analyzers, and total phosphorus analyzers. Chemical structure detection uses GC-MS (gas chromatography-mass spectrometry) to observe changes in the chemical structure of petroleum components. Auxiliary instruments include pH meters and dissolved oxygen meters for monitoring reaction conditions such as temperature, pH value, and dissolved oxygen content.

[0571] The preparation method for the influent sample is as follows: First, prepare the influent sample by mixing various pollutants according to the actual concentration range based on the sampling data to prepare a representative simulated wastewater. Then, adjust the concentration of each component to reflect the on-site conditions as closely as possible. If necessary, add microbial communities isolated from the actual water sample to ensure that the biochemical reactions proceed simultaneously. Next, calibrate the ozone system by installing the ozone generator, verifying the ozone concentration using a standard solution, and determining the output flow rate. Then, adjust the dosing method, such as starting with a low concentration and gradually increasing it to match the experimental design requirements. Finally, configure and debug the instruments by installing and debugging the online monitoring equipment to ensure the accuracy and real-time nature of data acquisition. In the preliminary test phase, pre-run the equipment to calibrate the instrument response.

[0572] The reaction mechanism of the prepared influent sample is as follows: First, in terms of pollutant oxidation, it simulates the oxidation reaction of organic matter, nitrogen, phosphorus, and petroleum substances in wastewater under the action of ozone, forming intermediate oxidation products or ultimately being mineralized into CO2 and water; Second, ozone can not only react directly with organic matter, but its decomposition also produces hydroxyl radicals (·OH), which have stronger oxidizing power and play an important role in the degradation of complex organic matter; Third, in terms of reaction selectivity, different components have different reaction rates to ozone and hydroxyl radicals (·OH), and the prepared influent sample provides a basis for subsequent kinetic analysis;

[0573] S2.3 Determine the reaction process of sub-components in the pre-ozone contact tank: Simulate the actual ozone contact process in a small pre-ozone contact tank, measure the concentration changes and chemical structure changes of each sub-component during the ozone reaction process, analyze the degradation pathways of the sub-components and their changes over time, and then determine the reaction kinetic equation and corresponding reaction kinetic constants of each sub-component based on the degradation process and the concentration change curves of each component.

[0574] The determination of the reaction process of sub-components in the pre-ozone contact tank is achieved by simulating the ozone contact process in a small pre-ozone contact tank, systematically measuring the concentration and chemical structure changes of each sub-component, such as COD, nitrogen, phosphorus, and petroleum, and obtaining the reaction rate constant and mass transfer parameters based on the data fitting reaction kinetic model.

[0575] Specifically, the mechanism of action of ozone includes ozone oxidation reaction, ozone reaction with organic matter and generation of hydroxyl radicals (·OH);

[0576] The ozone oxidation reaction involves ozone (O3) directly oxidizing organic pollutants in water, generating oxidation products through a given reaction mechanism, including substances with stronger oxidizing power, such as peroxides and hydroxyl radicals (·OH). These oxidation products further degrade the pollutants.

[0577] The ozone reacts with organic matter in the water to form oxidation products with higher redox potentials. The specific reaction products vary depending on the specific organic matter composition in the water. Common reaction products are carbon dioxide (CO2), aldehydes, and ketones.

[0578] The generation of hydroxyl radicals (·OH) occurs during the decomposition of ozone. The hydroxyl radicals (·OH) generated have a strong oxidizing ability and can undergo secondary reactions with organic pollutants, promoting the further degradation of pollutants.

[0579] The degradation pathways and reaction kinetics analysis, in actual experiments, can be used to monitor the curves of the concentration changes of each component over time to determine the degradation pathways in the ozone treatment process. Common degradation pathways include direct ozone degradation and degradation reactions involving free radicals. Direct ozone degradation refers to the direct reaction between ozone and pollutants. Degradation reactions involving free radicals, such as the generation of hydroxyl radicals or other free radicals, promote the degradation of pollutants.

[0580] The determination of the reaction process of the sub-components in the pre-ozone contact tank can be achieved by fitting the change curves of the concentration of each component. The reaction kinetic equations of each sub-component can be obtained. These kinetic equations describe the change of pollutant concentration over time, which is expressed as a kind of exponential decay or other reaction rate model. At the same time, the kinetic constants of each reaction can also be obtained. These constants provide a quantitative basis for assessing the ozone reaction rate.

[0581] S2.31 Experimental Operation: By setting initial conditions, starting the reaction, and taking samples at different time intervals, the ozone contact process under actual working conditions is simulated to monitor pollutant degradation and changes in related parameters;

[0582] S2.311 Initial conditions setting: Adjust the water volume, inlet flow rate and ozone gas flow rate in the experimental device to ensure that they match the actual working conditions, and ensure that the gas-liquid mixing system operates well and the bubbles are evenly distributed;

[0583] The initial conditions need to be set by adjusting the water volume in the pool, the inlet flow rate, and the ozone gas flow rate to ensure that they match the actual working conditions. Then, check the operation of the gas-liquid mixing system to ensure that the ozone bubbles are evenly distributed.

[0584] S2.312 Reaction Start-up: Introduce the prepared simulated wastewater into the small ozone contact tank, start the circulation system, and then add ozone gas. Gradually increase the dosage according to the concentration and time designed in the experiment, and continuously record the operating parameters.

[0585] The reaction start-up (S2.312) involves introducing the prepared simulated wastewater into the contact tank, starting the circulation system, and then gradually increasing the ozone dosage according to the experimental design, gradually increasing the ozone concentration according to the predetermined concentration and time scheme, while recording key operating parameters such as ozone concentration and flow rate.

[0586] S2.313 Time-segmented sampling: Sampling is performed in the contact tank at predetermined time intervals, and changes in temperature, pH and ozone concentration are recorded to reflect the degradation of pollutants at different reaction stages;

[0587] The time-segmented sampling involves taking samples from the contact tank at different time points, such as 0, 2, 5, and 10 minutes, to detect the degradation of pollutants; at the same time, changes in system temperature, pH value, and ozone concentration are recorded.

[0588] The time-segmented sampling involves taking samples from the contact tank at different time points of 0, 2, 5, and 10 minutes to ensure that the samples represent different reaction stages. At the same time, the system temperature, pH value, and ozone concentration changes are recorded to provide basic data for subsequent data analysis and ensure the integrity of the data.

[0589] S2.32 Detailed Component Reaction Analysis: By measuring the changes in the concentration and chemical structure of each pollutant, and analyzing the effects of ozone concentration, gas-liquid mixing, temperature and pH on reaction efficiency, the degradation mechanism of each component is sought and the optimal operating conditions are determined.

[0590] S2.321 Pollutant Concentration Change Measurement: By sampling at regular intervals and using online and laboratory analysis methods, the concentration and chemical structure changes of COD (particulate and dissolved), various types of nitrogen, phosphorus and petroleum substances in wastewater before and after degradation are detected to accurately assess its degradation dynamics and treatment effect.

[0591] The pollutant concentration changes were measured as follows: COD was determined by measuring the concentration changes of particulate and dissolved COD; nitrogen was determined by measuring the concentration changes of ammonia nitrogen, nitrate nitrogen, and nitrite nitrogen respectively; phosphorus was determined by measuring the concentration changes of total phosphorus and orthophosphate; and petroleum hydrocarbons were analyzed by GC-MS to examine the chemical structural changes of petroleum hydrocarbon components before and after degradation.

[0592] The pollutant concentration change measurement involves measuring the changes in particulate and dissolved COD, as well as the concentration changes of various types of nitrogen and phosphorus at different time points. Data are then obtained using online analytical instruments and laboratory methods to ensure data accuracy.

[0593] The petroleum-based substances were analyzed using GC-MS to detect changes in their chemical structure before and after degradation, focusing on the main components and their intermediate products.

[0594] S2.322 Chemical Structure Change Analysis: GC-MS was used to detect the structural changes of petroleum compounds before and after degradation, focusing on identifying intermediate products such as lower alkanes or small molecule aromatic compounds, thereby revealing the transformation process of complex organic matter from partial oxidation to final mineralization into CO2 and H2O;

[0595] The chemical structure change analysis focuses on analyzing the degradation pathways of petroleum compounds, identifying intermediate products such as lower alkanes or small molecule aromatic compounds, and thus determining the process by which complex organic matter is degraded into simpler compounds or ultimately mineralized into carbon dioxide and water.

[0596] The chemical structure change analysis involves using GC-MS to detect the chemical structure of petroleum compounds before and after degradation, focusing on identifying intermediate products such as lower alkanes or small molecule aromatic compounds to determine the degradation pathway. Then, the analysis is conducted on the formation of lower alkanes or small molecule aromatic compounds in the early stage of degradation, and further analysis is conducted on whether the degradation tends to generate simpler compounds or further mineralizes into CO2 and H2O. The analysis results reveal the transformation process of complex organic matter from partial oxidation to final mineralization.

[0597] S2.323 Identify key influencing factors: Analyze the effects of ozone concentration, gas-liquid mixing effect, temperature, and pH on the degradation efficiency of each component, and then determine the optimal operating conditions for the reaction;

[0598] The determination of the key influencing factors involves analyzing the effects of ozone concentration, gas-liquid mixing effect, temperature and pH on the degradation efficiency of each component, identifying the key factors limiting the reaction, and then finding the optimal operating conditions through controlled variable experiments.

[0599] S2.33 Dynamic Parameter Extraction

[0600] S2.331 Kinetic Equation Fitting: The kinetic models of each component are fitted using experimental data, and key parameters are extracted to quantitatively describe the reaction process of each pollutant component.

[0601] The kinetic equation fitting involves using data collected at different time points to establish reaction kinetic models for each pollutant component, such as first-order reaction kinetics or coupled models considering mass transfer limitations. Then, the reaction rate constant and initial concentration parameters are extracted through data fitting to quantitatively describe the reaction process.

[0602] First-order reaction kinetics or a kinetic model based on a combination of mass transfer and reaction is used.

[0603] The first-order reaction kinetics are as follows:

[0604]

[0605] Here, (C) or [C] represents the concentration of a component, such as resin monomer or reactant, and k is the reaction rate constant; the negative sign "-" indicates that the concentration of reactant decreases over time.

[0606] The first-order reaction kinetic model is common in many simple chemical reactions and is suitable for situations where the reaction rate is linearly dependent on the reactant concentration. It extracts the reaction rate constant and other parameters, such as the initial concentration C. O ;

[0607] The core of the kinetic equation fitting is to use experimental data to fit (or estimate) the kinetic model related to the reaction or synthesis process and to extract kinetic parameters. The goal is to extract and determine various parameters related to reaction kinetics from experimental data, such as reaction rate constant, reaction order, and concentration parameters.

[0608] The method for fitting the kinetic equations is to use experimental data to fit the kinetic model of the synthesis part. That is, to use experimentally measured data, such as concentration, time, and conversion rate k, to fit one or more kinetic models, thereby obtaining parameters that conform to the actual reaction conditions.

[0609] The kinetic equation fitting process involves using sampled data to fit a kinetic model to each pollutant component. Commonly used models include first-order reaction kinetics or kinetic models based on mass transfer and reaction coupling. During the fitting process, the reaction rate constants and other parameters, such as initial concentration and reaction order, are extracted.

[0610] The aforementioned kinetic model based on mass transfer and reaction coupling has limitations for some reaction systems, such as diffusion, insufficient stirring efficiency, and interphase transfer, in addition to the chemical reaction itself. This means that the actual reaction rate is not determined solely by chemical kinetics. In such cases, a more complex model is needed to combine the mass transfer process and the reaction process for description.

[0611] The kinetic model based on the coupling of mass transfer and reaction will have the following form:

[0612] Rate = k × f(mass transfer factor, concentration, ...)

[0613] Alternatively, add diffusion or convection terms to the equation;

[0614] The kinetic model based on mass transfer and reaction coupling needs to extract parameters such as the reaction rate constant k and other parameters, like resin concentration.

[0615] The reaction rate constant k is one of the most critical kinetic parameters, used to characterize the speed of the reaction.

[0616] Other parameters include the reaction order. If it is not necessarily a first-order reaction, the reaction order needs to be determined by fitting different mechanisms or experimental data. If it is a mass transfer coupling model, the mass transfer coefficient or other parameters reflecting the mass transfer rate also need to be determined.

[0617] The actual operational process for extracting the kinetic parameters is as follows: First, experimental sampling and measurement are conducted. During the reaction, samples are taken periodically or in stages to measure the concentration or conversion rate of key components, such as monomers and resins, and to record temperature and time information. Next, the form of the kinetic model is determined. Based on the understanding of the system, a suitable model is initially assumed, such as a first-order reaction model or a more complex mass transfer-reaction coupling model. Then, the experimental data is fitted. The collected concentration-time data are input into the model, and the optimal kinetic parameters, such as k, reaction order, and mass transfer coefficient, are estimated through regression analysis or numerical fitting, such as least squares or nonlinear regression. Finally, verification and correction are performed. The accuracy of the model is evaluated by comparing the differences between the model's predicted values ​​and the actual measured values. If the deviation is too large, the model or parameters need to be corrected, or more influencing factors, such as side reactions and heat transfer, need to be considered.

[0618] S2.332 Mass Transfer Parameter Calculation: Based on the two-film theory, the mass transfer rate constant of ozone between the gas and liquid phases and the specific surface area of ​​the bubbles are calculated to analyze the limiting conditions of gas-liquid mass transfer, thereby providing a theoretical basis for process optimization.

[0619] The mass transfer parameter calculation is based on the two-film theory. It calculates the mass transfer rate constant and bubble specific surface area of ​​ozone between the gas and liquid phases, and then analyzes the mass transfer limiting conditions in the reaction of each component to provide a theoretical basis for process optimization.

[0620] Based on the two-film theory, the ozone mass transfer rate constant K was calculated. L And the specific surface area of ​​the bubbles α:

[0621] J = K L α·([O3] gas -[O3] liquid )

[0622] Where J is the mass transfer rate flux of ozone (O3); K L It is the mass transfer coefficient of the liquid phase, [O3] gas and [[O3]] liquid These represent the concentrations of ozone in the gas and liquid phases, respectively.

[0623] The calculation of the mass transfer coefficient focuses on the mass transfer rate between the gas phase and the liquid phase, extracting the mass transfer constraints of each component, the mass transfer of gas (such as ozone) between the gas and liquid phases, and is used to evaluate the rate at which a gas like ozone is transferred from the gas phase to the liquid phase. The concentration in both phases is taken into account in the calculation, and the two-film theory is used to estimate the mass transfer rate.

[0624] The mass transfer parameters are calculated based on the two-film theory. The mass transfer limiting conditions of each component are extracted to explain the resistance to mass transfer in the gas and liquid phases. The mass transfer rate constant of ozone in water and the specific surface area of ​​bubbles are calculated. Then, the mass transfer limiting factors are identified to provide a basis for further optimization of reaction conditions.

[0625] The calculation of the mass transfer parameters can be extended by considering the transfer of gas to water (liquid phase), and an expression for the concentration difference at the gas-liquid interface is given.

[0626] S2.333 Data Validation and Adjustment: Compare and correct the fitted kinetic equations and parameters with the experimental data to ensure that the model can accurately reflect the actual reaction process. If necessary, readjust the model parameters.

[0627] The data verification and adjustment process involves correcting the fitted kinetic equations and parameters to ensure consistency with experimental data; if the fitting error is large, the experiment is redesigned or the model is adjusted.

[0628] The data verification and adjustment involves comparing the fitted kinetic equations with experimental data to verify the accuracy of the model. If the error is large, the experimental scheme needs to be redesigned or the model parameters adjusted to ensure that the reaction kinetic equations can truly reflect the actual reaction process.

[0629] S2.34 Experimental Data Analysis and Optimization

[0630] S2.341 Degradation Efficiency Analysis: Calculation of the degradation efficiency of each pollutant at different time points:

[0631]

[0632] Where Co is the initial concentration of pollutants at the start of treatment, i.e., the initial concentration; Ct is the concentration of pollutants at any time during the treatment process; the degradation efficiency analysis formula is obtained by calculating the reduction in pollutant concentration (Co-Ct) and then comparing it with the initial concentration Co, and the degradation efficiency is expressed as a percentage.

[0633] The analysis of degradation efficiency, especially the degradation efficiency of each pollutant measured at different time points, mainly involves calculating the degradation effect of pollutants in a specific treatment process, especially when applied to waste gas treatment processes.

[0634] In the formula for analyzing degradation efficiency, degradation efficiency is a standard method for calculating the degree of pollutant degradation at different time periods. It is based on the trend of pollutant concentration changing over time. That is, by calculating the pollutant concentration at different time points, the effectiveness of the treatment process can be evaluated, and the removal rate of pollutants can be determined. Degradation efficiency also helps to evaluate the removal effect of different pollutants under specific treatment processes, and helps to optimize the treatment process and improve the efficiency of treatment equipment.

[0635] The degradation efficiency is calculated based on the degradation efficiency of pollutants at different time periods. The degradation efficiency is calculated according to the changes in the concentration of each component at different time points and expressed as a percentage to evaluate the removal effect of different pollutants under ozone treatment.

[0636] The degradation efficiency reflects the degree to which pollutants are removed during a certain treatment process. A high degradation efficiency indicates that the pollutants are effectively removed and the treatment effect is good. This formula is applied in the fields of waste gas treatment and wastewater treatment to evaluate the removal effect of pollutants under different treatment conditions.

[0637] S2.342 Ozone Utilization Efficiency Analysis: Calculate the effect of unit ozone dosage on pollutant removal and evaluate ozone utilization rate;

[0638] The ozone utilization efficiency is calculated based on the amount of pollutants removed per unit of ozone dosage, and evaluates the utilization rate and economy of ozone.

[0639] S2.343 Degradation Mechanism Summary: By integrating the concentrations of various pollutants and the chemical structure changes of intermediate products, the degradation pathways of different components (such as petroleum compounds) from partial oxidation to final mineralization are summarized, and the synergistic effect of direct ozone oxidation and secondary reactions involving hydroxyl radicals is revealed.

[0640] The degradation mechanism summary requires identifying the degradation pathways of different components, such as the oxidation of petroleum compounds, the formation of intermediate products, and mineralization.

[0641] The degradation mechanism is summarized by combining the concentration changes of each pollutant and the chemical structure changes of intermediate products, and the degradation pathways of each component are summarized. For example, petroleum substances undergo initial partial oxidation, that is, the formation of lower alkanes and small molecule aromatic compounds, and then gradually mineralize into carbon dioxide and water. The direct oxidation effect of ozone and the synergistic effect of the secondary reaction involving hydroxyl radicals (·OH) generated by ozone decomposition are further analyzed.

[0642] S2.344 Optimize experimental conditions: Based on data feedback, by adjusting key parameters such as ozone dosage, gas-liquid mixing intensity, influent flow rate, temperature and pH value, we repeatedly experimented to find the best reaction conditions in order to improve degradation efficiency and ozone utilization and ensure stable system operation.

[0643] The experimental conditions were optimized by adjusting the ozone dosage, gas-liquid mixing intensity, or operating parameters to seek the best degradation effect.

[0644] The optimization of experimental conditions involves adjusting the ozone dosage, gas-liquid mixing intensity, influent flow rate, temperature, and pH parameters based on data feedback. Subsequently, repeated experiments are conducted to find the optimal operating conditions, improve degradation efficiency and ozone utilization, and ensure the stability of system operation.

[0645] The reaction mechanism of the pre-ozone contact tank reaction process simulated by the experimental method is as follows: First, from the perspective of direct oxidation and free radical-mediated reaction, ozone can directly react with organic pollutants in water, breaking chemical bonds. At the same time, the hydroxyl radicals (·OH) generated by its decomposition have higher oxidation capacity, and can carry out secondary oxidation of complex organic matter. Second, from the perspective of intermediate product generation and transformation, a variety of intermediate oxidation products are generated during the reaction process. These intermediates are further oxidized and also exist briefly, ultimately achieving mineralization through continuous oxidation. Third, from the perspective of mass transfer control, the gas-liquid mass transfer process determines the distribution and concentration of ozone in the liquid phase. The mass transfer rate directly affects the reaction rate. The mass transfer parameters calculated using the two-film theory can reveal whether the reaction is limited by the transfer of ozone from the gas phase to the liquid phase.

[0646] Ultimately, the results of "S2. Experimental Method Simulation of the Reaction Process in the Pre-Ozone Contact Tank" showed that various oxidation products are generated during the ozone reaction. The specific products depend on the original pollutant composition in the water; the products include: firstly, carbon dioxide (CO2): especially when organic matter is completely mineralized, the final product is often carbon dioxide; secondly, water-soluble organic matter, i.e., ozone converts large organic molecules into smaller water-soluble substances, such as acids, aldehydes, and ketones; and thirdly, gaseous products, ozone also produces a small amount of gaseous products when reacting with organic matter, such as oxygen (O2) and ozone (O3) decomposition products.

[0647] Furthermore, the significant achievement of the "S2. Experimental Method for Simulating the Reaction Process of the Pre-Ozone Contact Tank" is that analysis of these products allows for a deeper understanding of the oxidation reaction mechanism and pollutant removal efficiency during ozone treatment. This experimental procedure precisely constructs a small-scale pre-ozone contact tank to simulate actual water quality and reaction conditions, and monitors the concentration and chemical structure changes of various pollutants in detail. By comparing data from different time periods and fitting kinetic models, reaction rate constants and mass transfer parameters are extracted, thereby revealing the degradation mechanism and reaction pathways of pollutants under ozone action. Ultimately, the experimental results not only provide a theoretical basis for the design and operation of pre-ozone contact tanks but also offer experimental data support and operational strategies for optimizing ozone treatment processes.

[0648] Example 4: Specific Implementation of "S3. Establishing a Mechanism Model of the Pre-Ozone Contact Tank" in the Technical Solution of the Present Invention

[0649] This embodiment provides a detailed explanation of S3, establishing the mechanistic model of the pre-ozone contact tank in the model building method for advanced oxidation of petroleum wastewater described in this invention. "S3. Establishing the mechanistic model of the pre-ozone contact tank" comprehensively considers experimental data, reaction kinetics, mass transfer processes, and initial and boundary conditions, ultimately constructing a mathematical model that accurately reflects the multi-component reaction process within the pre-ozone contact tank. "S3. Establishing the mechanistic model of the pre-ozone contact tank" primarily addresses how to accurately simulate and predict the oxidative degradation and mass transfer efficiency of ozone on various pollutants. Its sub-steps, from data collection, equation establishment, parameter input, numerical solution, and result correction, specifically address each key aspect of this complex problem.

[0650] The core function of "S3. Establishing the Mechanism Model of the Pre-Ozone Contact Tank" in the technical solution of this invention lies in achieving fine coupling of multiple components, enabling dynamic correction based on experimental data, and preparing for coupling between numerical simulation and CFD. Firstly, fine coupling of multiple components: "S3. Establishing the Mechanism Model of the Pre-Ozone Contact Tank" establishes separate reaction rate equations for each component in the influent (COD, nitrogen, phosphorus, and petroleum), and then combines this with the gas-liquid two-phase mass transfer process, achieving a refined description and simultaneous equations for the degradation mechanisms of multiple pollutants. Secondly, dynamic correction based on experimental data: "S3. Establishing the Mechanism Model of the Pre-Ozone Contact Tank" rigorously corrects the mechanism model using experimentally measured initial concentrations, kinetic constants, and mass transfer coefficient parameters, making the model closer to actual operating conditions and possessing high engineering applicability. Thirdly, preparation for coupling between numerical simulation and CFD: "S3. Establishing the Mechanism Model of the Pre-Ozone Contact Tank" considers subsequent coupling with computational fluid dynamics (CFD) models during the establishment process, providing a solid foundation for simulating chemical and physical processes within complex reactors. "S3. Establishing a Mechanism Model for the Pre-Ozone Contact Tank" innovatively couples experimental data, detailed reaction kinetics, and mass transfer processes together by establishing an integrated multi-component mechanism model, forming a dynamic, accurate, and scalable simulation platform; in particular, it can adopt specific kinetic descriptions for different pollutants, while introducing a mass transfer-reaction combined mechanism, and preparing for subsequent coupling with fluid dynamics.

[0651] Furthermore, the specific implementation of the method for building a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to the present invention, specifically "S3. Establishing a mechanism model of the pre-ozone contact tank", is as follows:

[0652] S3.1 Determination of Model Input Components: The various components determined in S1. and S2., such as COD breakdown, nitrogen and phosphorus, and petroleum components, are used as input parameters for the mechanistic model, based on the experimentally measured initial concentration or concentration range.

[0653] The model input components are determined based on various components identified in S1 and S2, such as COD sub-components, nitrogen, phosphorus, and petroleum substances, as input parameters. These input parameters are set based on experimentally determined initial concentrations or concentration ranges to ensure the model reflects real-world conditions. Input variables include COD sub-components, nutrients, and petroleum pollutants. COD sub-components include organic compounds of different molecular weights or structural types, with varying reactivity and oxidation susceptibility. Nutrients include nitrogen compounds such as ammonia nitrogen and nitrate nitrogen, and phosphorus compounds such as orthophosphorus and organically bound phosphorus. Petroleum pollutants include various hydrocarbons and their derivatives.

[0654] The mechanism for determining the input components of the model requires that the concentration, reaction mechanism, and key physicochemical parameters (such as solubility and partition coefficient) of each component be clearly specified in the input parameters to ensure that the model can accurately reflect the actual reaction process.

[0655] The mechanism for determining the input components of the model must include the concentration data, reaction mechanism, and corresponding physicochemical parameters of each component, such as solubility and partition coefficient, all of which should be clearly stated in the input parameters.

[0656] S3.11 Collect experimental data: Under strictly controlled sampling conditions, collect the concentrations of organic matter, nutrients, and petroleum pollutants in the influent, as well as the corresponding reaction kinetic parameters (such as reaction rate constant and mass transfer coefficient), to ensure that reliable and representative numerical data are provided for the model.

[0657] The collection of experimental data requires collecting the concentration data of organic components, nitrogen, phosphorus and petroleum pollutants in the influent determined in the experiment; confirming the reaction kinetic equations and kinetic constants of each component obtained in the experiment, such as the reaction rate constant k and the mass transfer coefficient K;

[0658] The data collection includes the concentrations of organic components in the influent, the concentrations of nutrients and petroleum pollutants, and kinetic parameters. The concentrations of organic components in the influent are the concentrations of COD and its sub-components obtained from laboratory analysis or on-site monitoring. The concentrations of nutrients and petroleum pollutants are the actual concentration data of nitrogen, phosphorus, and various petroleum pollutants. The kinetic parameters are the reaction rate constant (k), mass transfer coefficient (K), and half-life of each component in the ozone oxidation process.

[0659] The experimental requirements for collecting experimental data need to ensure that the data is representative and repeatable. The data collection time, conditions, and instrument calibration need to be strictly controlled in order to provide reliable numerical basis for the model.

[0660] S3.12 Determine the reaction type: Identify the main chemical reactions and physical processes in the pre-ozone contact tank in order to construct a kinetic model describing their coupling effects;

[0661] Determining the reaction type requires clarifying the main chemical reactions and physical processes occurring in the pre-ozone contact tank, including ozone oxidation reaction, mass transfer process, and degradation pathway;

[0662] The main chemical reactions include ozone oxidation and auxiliary reactions. The ozone oxidation reaction is a direct reaction between ozone and organic matter, or indirect oxidation through the generation of hydroxyl radicals. Different organic matter may exhibit selective reaction or non-selective attack. The auxiliary reactions include the self-decomposition of ozone in water and side reactions with ions or other intermediate products. These all affect the overall oxidation efficiency.

[0663] The main physical processes include mass transfer, mixing and diffusion, and degradation pathways. The mass transfer process is the transfer of ozone from the gas phase to the liquid phase, which is an important factor affecting the reaction rate. The mixing and diffusion process is that uneven mixing or local concentration gradients within the reactor can also affect the reaction rate and conversion rate. The degradation pathway involves multi-stage degradation processes for different components, forming intermediate products, which are then further oxidized into harmless substances.

[0664] The mechanism modeling requirement is that the above chemical reactions and physical processes should be described in the model using appropriate kinetic equations or mass transfer models. It is necessary to establish a set of differential equations, such as ordinary differential equations or partial differential equations, to reflect the coupling effect between reaction rate and mass transfer.

[0665] S3.13 Select Modeling Tools: Select a suitable modeling tool, such as MATLAB, COMSOL Multiphysics, or Aspen Plus, for writing and solving equations;

[0666] The MATLAB software mentioned above can flexibly perform numerical calculations and has a rich set of toolboxes (such as the ODE solver and optimization toolbox). It is suitable for writing and solving reaction kinetic differential equations and is applicable to scenarios such as model parameter optimization, sensitivity analysis, dynamic simulation and control strategy design.

[0667] COMSOL Multiphysics has powerful finite element analysis capabilities, making it easy to handle mass transfer and reaction processes involving spatial distribution. It can take into account the heterogeneity inside the reactor and is suitable for simulating gas-liquid two-phase mass transfer, the influence of local concentration gradients and boundary conditions on the reaction. It is particularly suitable for establishing multiphysics coupling models.

[0668] Aspen Plus, a terminology engineering process simulation software, has a large number of built-in chemical reaction and mass balance models, suitable for overall simulation of industrial processes, applicable scenarios such as process scale-up, system optimization, and economic evaluation. For detailed reaction mechanism modeling, its built-in models require further customization.

[0669] The choice of modeling tool should be based on the research focus and requirements. If the focus is on reaction kinetics and parameter sensitivity analysis, MATLAB is a more flexible choice. If the spatial distribution of wastewater and local mass transfer phenomena need to be considered, COMSOL's multiphysics simulation capabilities are more suitable. If the research direction is inclined towards the overall optimization of wastewater industrial processes, Aspen Plus can provide better support in process simulation and economic evaluation.

[0670] The selection of modeling tools and the establishment of a mechanistic model of the pre-ozone contact tank is a multi-step process. From identifying the input components, collecting experimental data, determining the reaction mechanism of each component, to selecting appropriate numerical simulation tools, each step requires systematic construction based on experimental data and theoretical analysis. By comprehensively utilizing tools such as MATLAB, COMSOL Multiphysics, or Aspen Plus, dynamic simulation and optimization of the ozone oxidation process can be achieved, thereby providing a scientific basis for the design and operation of the pre-ozone contact tank.

[0671] S3.2 Kinetic Equations and Constants: The reaction kinetic equations and reaction kinetic constants obtained in S2. are incorporated into the model, and corresponding degradation equations are established for each type of sub-substance;

[0672] The arrangement of the kinetic equations and constants is the next step after the preliminary experimental data collection and reaction type determination, which is to incorporate the reaction kinetic equations and reaction rate constants of each component into the mechanistic model.

[0673] The key work in organizing the dynamic equations and constants includes organizing experimental data, defining and determining the constants and parameters of each variable in the model;

[0674] To organize the experimental data, we first need to establish a numerical database by referring to the initial concentrations, reaction rate constants (k), and mass transfer coefficients (KL) of each component collected in S3.11. Then, we need to set the initial concentration ranges and kinetic parameters for COD, nitrogen, phosphorus, and petroleum pollutants. Finally, for the reaction constants k of each component, considering that these data come from experimental measurements or reports, we must ensure the accuracy and representativeness of the data.

[0675] The model defines the concentration of each reactant or intermediate product; [COD] represents the concentration of organic matter (COD) in the solution; [O3] represents the concentration of dissolved ozone in the solution; [NH3], [NO2]... - [NO3] - [PO4] represents the concentrations of ammonia nitrogen, nitrite nitrogen, and nitrate nitrogen, respectively; 3- ] represents the phosphate concentration;

[0676] The determined constants and parameters include the reaction rate constant k, the liquid phase mass transfer coefficient K_L, and the specific surface area A parameter. These parameters directly affect the reaction rate and the mass transfer process at the gas-liquid interface.

[0677] S3.21 Classification and Development of Rate Equations: Establish specific degradation equations for different types of pollutants;

[0678] S3.211 COD Degradation: Based on the first-order reaction kinetics of ozone oxidation, the COD degradation rate equation is established:

[0679]

[0680] Where [C] represents COD concentration, which is an indicator that comprehensively measures the total amount of organic matter in water, and indicates the COD concentration (mg / L) in the water at the current moment; k COD [O3] represents the reaction rate constant between COD and ozone, which is the second-order rate constant (L / mg·s or similar dimension) of the reaction between COD and ozone. It is a parameter obtained by experiment or data fitting and reflects the intrinsic rate of ozone oxidation of organic matter; [O3] represents the ozone concentration, which is affected by mass transfer efficiency, inlet gas concentration and temperature factors in actual engineering. It is a dynamic quantity representing the rate of change of C (COD concentration) per unit time. It represents the rate of change of COD concentration per unit time (mg / L·s or mg / L·min). Since COD is consumed in the reaction process, its concentration decreases with time. Therefore, there is a negative sign in the rate equation, indicating that the concentration decreases with time.

[0681] The COD degradation rate equation expresses the consumption pattern of COD during ozone oxidation and is the most fundamental and crucial part of the mechanistic model. This is achieved by obtaining a suitable k... COD By combining the ozone concentration distribution under actual working conditions, the dynamic removal process of COD can be described and predicted more accurately.

[0682] The COD degradation rate equation, based on the assumption that the reaction between COD and ozone follows second-order kinetics (each exhibiting first-order kinetics with respect to its respective concentration), effectively describes the ozone oxidation degradation process under most conditions. Furthermore, by incorporating this rate equation into a holistic mechanistic model, it can be solved simultaneously with other pollutants (such as nitrogen, phosphorus, and petroleum hydrocarbons) and mass transfer processes, thus providing a scientific basis for engineering design and operational optimization. Even once k is determined... COD By understanding the variation pattern of [O3], we can quickly predict the degradation trend of COD in the ozone contact tank, providing a reference for subsequent process debugging and performance evaluation.

[0683] In the COD degradation rate equation, typical values ​​and influencing factors include temperature, pH, interfering substances, and mixing and residence time. Regarding temperature, the decomposition rate and solubility of ozone in water decrease with increasing temperature, thus affecting the actual value of [O3] and the reaction rate constant. Regarding pH, under alkaline conditions, ozone is more easily decomposed to generate hydroxyl radicals (·OH), thereby altering the reaction mechanism and rate constant. Regarding interfering substances, the presence of high concentrations of carbonates, bromide ions, or other components that can react with ozone or free radicals in the water can interfere with the COD removal rate, requiring the addition of more reaction pathways to the model. Regarding mixing and residence time, reactor design, such as gas-liquid contact methods, stirring conditions, and residence time, affects mass transfer efficiency and reaction rate, and must be considered during modeling.

[0684] The experiment determined k COD In practical engineering or laboratory settings, the rate constant k can be fitted by measuring the COD reduction at different ozone concentrations and reaction times. COD ;

[0685] The aforementioned combined mass transfer process, in a more complete model (such as the mechanistic model of the pre-ozone contact tank), also needs to consider the influence of gas-liquid mass transfer. Ozone is not supplied indefinitely, but continuously dissolves from the gas phase to the liquid phase. At this time, the [O3] varies with time and spatial distribution, requiring the simultaneous establishment of mass transfer equations:

[0686] J = K L ·A·(C*-[O3])

[0687] Among them, K L is the mass transfer coefficient, A is the gas-liquid interface area, and C* is the saturated dissolved ozone concentration, used to update the value of [O3] in real time;

[0688] In numerical simulation, Together with the degradation equations of other pollutants, they form a set of coupled differential equations, which can be used to calculate the change of [C][O3] over time using the ODE solver of MATLAB or COMSOL software;

[0689] The engineering significance of the modeling and application is that the equation can quantitatively predict the removal efficiency and rate of COD in the ozone oxidation process, select the appropriate ozone dosage and contact time to achieve the expected treatment effect, and, in conjunction with the calibration of mass transfer models and kinetic parameters, can be used for process optimization and cost assessment.

[0690] The aforementioned reaction mechanism and assumptions consider the first-order or second-order kinetics of ozone oxidation. In many water treatment processes, chemical oxygen demand (COD) represents the total amount of oxidizable organic matter in water. Ozone [O3] is a strong oxidant that can directly or indirectly (generating hydroxyl radicals) react with organic matter. Through extensive experimental and theoretical studies, it has been found that the reaction of many organic substances with ozone can be approximated as a first-order reaction with respect to both organic matter and ozone, i.e., the overall reaction is a second-order reaction. Therefore, the rate equation for the reaction between COD and ozone can be expressed as:

[0691] Rate = k COD ·[C] 1 ·[O3] 1

[0692] The applicable conditions for the COD degradation rate equation are that the rate equation is generally applicable when the ozone concentration is within a certain range and there are no significant interfering reactions in the solution.

[0693] If the COD degradation rate equation is applicable under actual working conditions, such as ozone concentration being much higher than COD, or other physicochemical factors being more complex, such as pH, temperature, and coexisting ions, then it is necessary to correct or introduce the influence of more intermediate products.

[0694] The COD degradation rate equation reflects that, under conditions of sufficient ozone or when the reaction rate is limited by COD concentration, the degradation of COD exhibits first-order reaction kinetics. If the ozone concentration remains stable, it can also be regarded as a pseudo-first-order reaction.

[0695] The reaction mechanism for the degradation rate of COD is based on the first-order reaction kinetics of ozone oxidation, assuming that the reaction rate is proportional to the COD concentration and the ozone concentration.

[0696] The degradation equations for each component are established based on ozone oxidation reaction for COD, nitrogen, phosphorus and petroleum pollutants, expressing their respective relationships with ozone concentration and reaction rate constant.

[0697] S3.212 Reaction of nitrogen components: The rate equation describing the reaction of ammonia nitrogen with ozone is used to calculate the oxidative degradation process of ammonia nitrogen;

[0698] First, the ammonia nitrogen oxidation equation:

[0699]

[0700] in, is the rate constant for the reaction of ammonia nitrogen with ozone;

[0701] The ammonia nitrogen oxidation equation represents the oxidation reaction of ammonia nitrogen [NH3] under the action of ozone [O3], and the rate of change of ammonia nitrogen concentration over time. The negative sign "-" indicates that the ammonia nitrogen concentration decreases or is consumed over time.

[0702] The ammonia nitrogen oxidation equation indicates that the reaction between ammonia nitrogen and ozone is considered as a first-order or pseudo-first-order kinetic. Specifically, the reaction rate is directly proportional to the concentrations of both ammonia nitrogen [NH3] and ozone [O3]. It is assumed that the reactants are fully mixed in the solution and that the influence of other side reactions is weak, with the direct oxidation of ammonia nitrogen and ozone being the main driver.

[0703] The rate constant It is the rate constant for the oxidation of ammonia nitrogen by ozone, and its unit is generally L·mol. -1 ·s -1 or m 3 ·mol -1 ·s -1 The concentration unit may vary depending on the actual concentration used, reflecting the ease and speed of the reaction between ammonia nitrogen and ozone.

[0704] Secondly, the dynamic change equations for nitrate and nitrite nitrogen: During the reaction, ammonia nitrogen is oxidized to nitrite. Further oxidation to nitrate The concentrations of nitrite and nitrate exhibit a dynamic change over time, initially increasing and then decreasing, or continuing to increase; among them, and Here, denoted as the reaction rate constants for the conversion of ammonia to nitrite and nitrite to nitrate, respectively, can be dynamically described by the following equation:

[0705] First, the formation and consumption of nitrite are as follows:

[0706]

[0707] in, Describe the rate of the reaction from ammonia nitrogen to nitrite; the first positive term. The term represents the rate of nitrite formation—that is, the process of ammonia nitrogen oxidation to nitrite; the second negative term. This indicates the rate at which nitrite is further oxidized to nitrate by ozone;

[0708] The generation and consumption of nitrite are assumed to follow first-order or pseudo-first-order kinetics, i.e., proportional to [NH3] and [O3], and the process of nitrite being oxidized back to nitrate also follows similar first-order kinetics.

[0709] Second, the formation of nitrates is as follows:

[0710]

[0711] in, Describe the rate of the reaction from nitrite to nitrate; there is only one positive term, representing the rate at which the nitrate concentration increases with time, originating from the further oxidation of nitrite; under the following assumptions, It is no longer further oxidized or consumed;

[0712] The application in modeling is that by simulating the above equations, the entire process of ammonia nitrogen being gradually consumed, nitrite increasing and then decreasing, and nitrate gradually accumulating can be simulated. If excessive nitrite accumulation is observed in the actual water treatment process, the ozone dosage, pH, and temperature operating parameters need to be adjusted to balance the rates of these two reaction steps.

[0713] S3.213 Changes in phosphorus composition: Considering the direct reaction between phosphate and ozone, the rate expression for the reaction equation between phosphate and ozone is:

[0714]

[0715] in, This is the rate constant for the reaction of phosphate with ozone; the negative sign indicates that the phosphate is consumed during the oxidation process.

[0716] The rate of the reaction equation between phosphate and ozone represents the phosphate... The rate of change of phosphate concentration over time when it undergoes a direct oxidation reaction with ozone [O3];

[0717] The mechanism of the change in phosphorus composition is based on the assumption that the reaction between phosphate and ozone follows first-order kinetics, with the rate related to the phosphate content. Proportional to ozone [O3] concentration, this reaction is often not the main pathway in actual water treatment processes, but phosphates also undergo a certain degree of oxidation or transformation under high ozone dosage or specific conditions.

[0718] The rate constant This reflects the rate at which phosphates are oxidized by ozone. A small constant value indicates that the ozone oxidation capacity of phosphate is relatively weak; if the experiment shows that the phosphate degradation rate is very small, the reaction can be ignored and the phosphate concentration can be regarded as basically unchanged.

[0719] S3.214 Oxidation of petroleum components: Describes a kinetic model of the reaction between petroleum pollutants, such as alkanes, cycloalkanes, and aromatics, and ozone. This reaction model is based on ozone oxidation and describes the reaction kinetics between petroleum pollutants and ozone. The reaction rate is proportional to the product of the concentration of petroleum pollutants and the concentration of ozone.

[0720] The decomposition rate equations for alkanes, cycloalkanes, and aromatic hydrocarbons are established as follows:

[0721]

[0722] in, This indicates the concentration of petroleum pollutants [P]. oil The rate of change over time reflects the oxidation rate of petroleum pollutants under the action of ozone; the negative sign "-" indicates that the concentration of petroleum pollutants decreases over time.

[0723] [P oil [] represents the concentration of petroleum pollutants, expressed in mol / L or mg / L. The concentration can be obtained from experimental data, or estimated based on the actual type, concentration, and distribution of pollutants in the water body. Alternatively, independent equations can be established based on the actual situation.

[0724] [O3] represents the concentration of ozone, with units of mol / L or mg / L. During the reaction process, ozone reacts with petroleum pollutants, so the ozone concentration plays a decisive role in the reaction rate.

[0725] k R This represents the rate constant for the reaction of petroleum products with ozone, expressed in L·mol⁻¹. -1 ·s -1 k R The constant characterizes the reaction rate between petroleum pollutants and ozone, and its value is related to the reaction temperature, pH value, and ozone concentration.

[0726] The oxidation reaction mechanism of the petroleum components, including alkanes, cycloalkanes and aromatics, can also be regarded as a first-order or pseudo-first-order reaction with ozone.

[0727] The reaction process of the decomposition rate equations for alkanes, cycloalkanes, and aromatics is based on the assumption that the reaction between petroleum pollutants and ozone follows first-order reaction kinetics, i.e., the reaction rate is proportional to the product of the concentration of petroleum pollutants and the concentration of ozone; the reactants are petroleum pollutants and ozone; and the reaction products are petroleum pollutants oxidized into smaller organic compounds or other non-pollutants.

[0728] The rate constant k RThe significance of this parameter lies in its role as a key indicator reflecting the reaction rate of petroleum pollutants with ozone, specifically the rate constant k. R The rate constant k is determined from experimental data, based on the type of petroleum pollutants, such as alkanes and aromatics, and environmental conditions. R The value is affected by several factors, such as reaction temperature, ozone concentration, and reaction time;

[0729] The oxidation reaction characteristics of petroleum pollutants are discussed. Petroleum pollutants are diverse, and different pollutants, such as alkanes, cycloalkanes, and aromatic hydrocarbons, react with ozone at different rates. Furthermore, the reaction constant k for different petroleum substances varies under different conditions. R There will be differences, and in practical applications, different k values ​​need to be set for different types of petroleum pollutants based on experimental results or data. R value;

[0730] The application of the decomposition rate equations for alkanes, cycloalkanes, and aromatics is that these equations can be used in reactor models to simulate the degradation process of petroleum pollutants and to analyze their changing trends through numerical solutions.

[0731] The numerical solution of the decomposition rate equations of alkanes, cycloalkanes, and aromatics can be used to solve the equations together with the degradation equations of other pollutants, such as COD, nitrogen, and phosphorus, when establishing the mechanism model of the ozone reactor. Then, through numerical integration, such as using an ODE solver, the concentration change of petroleum pollutants over time during the ozone oxidation process can be obtained.

[0732] The decomposition rate equations for alkanes, cycloalkanes, and aromatics were modeled and optimized. If there were discrepancies between the experimental data and the model predictions, the rate constant k could be adjusted. R To optimize the model so that it more accurately reflects the oxidation process of petroleum pollutants;

[0733] S3.22 Mass transfer and reaction combined: The mass transfer process of ozone from the gas phase to the liquid phase is coupled with the chemical reaction rate in the liquid phase, thereby revealing and predicting how the overall reaction rate is affected by the concentration difference at the gas-liquid interface and the mass transfer coefficient.

[0734] The mass transfer and reaction are combined in practical applications. The mass transfer process of ozone from the gas phase to the liquid phase has a significant impact on the overall reaction rate. It is necessary to consider the effects of coupling the mass transfer process with the chemical reaction and the gas-liquid two-phase mass transfer process. This model describes the mass transfer process of ozone from the gas phase to the liquid phase. Through this model, the dissolution and reaction rate of ozone in the liquid phase can be estimated, and the relationship between the reaction rate and the mass transfer process can be shown.

[0735] The mass transfer rate equation is as follows:

[0736] J = KL α·A·([O3] gas -[O3] liquid )

[0737] Where J represents the mass flux of ozone at the gas-liquid interface, in mg / m³. 2 ·s;K L α represents the liquid-phase mass transfer coefficient, describing the rate at which ozone is transferred in the liquid phase; α represents the gas solubility coefficient, describing the gas's ability to dissolve in the liquid; A is the effective specific surface area within the reactor, i.e., the total area of ​​the gas-liquid interface, in m². 2 [O3] gas The concentration of ozone in the liquid phase at equilibrium is determined by the concentration and solubility of ozone in the gas phase; [O3] liquid This represents the actual ozone concentration in the liquid phase.

[0738] The mass transfer rate equation states that the ozone mass transfer rate is proportional to the ozone concentration difference between the gas and liquid phases, meaning the mass transfer rate J depends on multiple factors, including the mass transfer coefficient K. L The gas-liquid interface area A, and the difference in ozone concentration between the gas and liquid phases ([O3]). gas -[O3] liquid During the reaction, ozone enters the liquid phase from the gas phase through the interface, and the concentration in the liquid phase is affected by the concentration in the gas phase.

[0739] The role of the mass transfer coefficient K in the mass transfer process. L It reflects the efficiency of gas transfer through the liquid interface. This coefficient is related to the properties of the liquid, such as viscosity, surface tension and gas solubility. α is the solubility coefficient, which represents the ability of gas to dissolve in liquid, that is, the distribution and dissolution state of gas in liquid.

[0740] The concentration difference in the mass transfer process, ([O3]) gas -[O3] liquid The concentration difference between the gas phase and the liquid phase is the driving force for the transfer of ozone from the gas phase to the liquid phase. If the ozone concentration in the gas phase is higher than that in the liquid phase, ozone will tend to enter the liquid phase through the gas-liquid interface until the concentrations of the two phases tend to be in equilibrium.

[0741] The coupling of the mass transfer process is achieved using the mass transfer rate equation J = K L α·A·([O3] gas -[O3] liquid The ozone mass transfer process at the gas-liquid interface is incorporated into the overall model, so that the model can reflect both the rate of chemical reaction and the limiting factors when ozone enters the liquid phase.

[0742] The model combines gas reaction and mass transfer processes, describing the reaction rate of gas as it enters the liquid phase. The product in the equation reflects the overall mass transfer effect in the liquid phase. This quantitative model can predict the reaction rate of ozone when it enters the liquid phase, thereby optimizing the reaction process in the liquid.

[0743] S3.3 Mechanism Model Integration: The reaction equations of each component are assembled and coupled to form a multi-component mechanism model of the pre-ozone contact tank, providing a chemical reaction basis for subsequent CFD coupling and numerical simulation.

[0744] The model is solved and optimized by combining reaction and mass transfer equations and using numerical methods to simulate the degradation process of each component, providing a scientific basis for the design and optimization of operating parameters of the pre-ozone contact tank.

[0745] S3.31 Determine the equation structure: Summarize all rate equations and establish a total rate equation system that includes multiple components, such as COD, nitrogen, phosphorus, and petroleum pollutants;

[0746] S3.32 Simultaneous Equations: The rate equation and mass transfer equation are integrated into a system of simultaneous equations using software. The system of equations is solved using finite element analysis or matrix calculation tools. At the same time, the coupling effect of mass transfer and reaction in the spatiotemporal distribution is described. The system of equations is solved using finite element analysis or matrix calculation tools.

[0747] The simultaneous equations are formed by combining the rate equation and the mass transfer equation using software. All equations are compiled into a system of simultaneous equations using finite element analysis tools such as COMSOL or matrix calculation tools such as ODE45 in MATLAB. The spatiotemporal distribution characteristics of mass transfer and reaction are also taken into account. If necessary, partial differential equations can also be used to describe them.

[0748] S3.33 Set initial conditions: Input the initial concentration values ​​obtained from the experiment, such as the initial concentration of COD, nitrogen and phosphorus, and set the initial ozone concentration to ensure that the initial values ​​of each variable in the model are reasonable;

[0749] S3.4 Parameter Input: By importing the kinetic constants, mass transfer coefficients, initial concentrations, and setting boundary conditions, time steps, and total simulation time obtained from experiments, a parameter library that truly reflects the actual working conditions is constructed, providing basic data for subsequent model solving;

[0750] The parameter input stage uses the kinetic constants, mass transfer coefficients and initial concentration data obtained from experiments as the basic data for the model, and the boundary conditions, time steps and total simulation time settings ensure that the model is consistent with the actual working conditions.

[0751] S3.41 Input experimental parameters: Input the kinetic constants, mass transfer coefficients, and reaction rate constants obtained in the experiment into the model one by one;

[0752] The input experimental parameters require the import of experimental data. Using the various kinetic and physical property parameters obtained from previous experiments, the reaction rate constants corresponding to each component are input into the model.

[0753] The experimental data imported includes the reaction rate constant k between COD and ozone. COD Oxidation rate constants of ammonia nitrogen and ozone Decomposition rate constant k of petroleum components oil Or simply R; the reaction rate constant k between COD and ozone COD This reflects the reaction rate between ozone and organic matter during COD decomposition; the oxidation rate constant of ammonia nitrogen and ozone. Used to describe the oxidation process of ammonia nitrogen under ozone; the decomposition rate constant k of the petroleum components. oil Or simply R, the reaction rate is determined for alkanes, cycloalkanes and aromatic petroleum substances respectively;

[0754] The parameter library construction involves organizing these experimental parameters into a parameter library to ensure that each input parameter has a clear value, dimension, and applicable conditions, providing basic data for subsequent model solving;

[0755] S3.42 Set boundary conditions: By inputting ozone gas input concentration and influent parameters consistent with the experimental conditions into the pool model, ensure that the model can truly reflect the actual operation process and provide the necessary initial and boundary data for subsequent solutions;

[0756] The boundary conditions are set by inputting boundary conditions into the pool model, such as the input concentration of ozone gas, and the concentrations of influent COD, nitrogen, and phosphorus.

[0757] The boundary conditions are determined by setting boundary conditions in the pool model that are consistent with the experimental conditions.

[0758] The boundary conditions include ozone gas input concentration and influent parameters; the ozone gas input concentration is the gas phase ozone concentration input according to the ozone supply system settings in the experiment; the influent parameters, such as COD concentration, nitrogen (e.g., ammonia nitrogen, nitrite, nitrate), phosphorus concentration and petroleum pollutant concentration in the influent, ensure that the initial state of the model is consistent with the experiment.

[0759] The practical significance of setting boundary conditions is that reasonable boundary conditions are a prerequisite for ensuring that the model can truly reflect the actual operation process, and at the same time provide necessary initial and boundary data for subsequent solutions.

[0760] S3.43 Setting the time step and simulation range: By selecting an appropriate time step and total simulation time, we can ensure that the dynamic changes in the reaction process can be accurately captured and that the stability and accuracy of the numerical solution are guaranteed.

[0761] The set time step and simulation range are determined based on the experimental reaction time range. The simulation time step is set, for example, 0.1 seconds, and the total simulation time is set, for example, 10 minutes.

[0762] The time step setting is based on the characteristic time of the reaction in the experiment, such as the time nodes of the start, progress and termination of the reaction, and selects an appropriate time step. For example, setting the step to 0.1 seconds can capture the rapid changes in the early stage of the reaction.

[0763] The total simulation time is set according to the experimental design or actual reaction time, such as 10 minutes or 600 seconds, in order to fully simulate the entire reaction process;

[0764] The selection of numerical stability and accuracy, time step and total simulation time must take into account the stability and accuracy requirements of the numerical solver to ensure that the dynamic changes in the reaction process can be accurately captured.

[0765] S3.5 Solving the Mechanism Model: By selecting an appropriate solver, combining the mass transfer and reaction equations, running the simulation, and verifying the consistency between the model results and experimental data, we can ensure the accuracy of numerical calculations and the correct capture of the dynamic changes in the reaction process.

[0766] The mechanism model is solved by selecting an appropriate time-dependent solver to solve the mass transfer and reaction equations simultaneously, running the simulation to observe the dynamic changes in the concentration of each component, and the model results need to be preliminarily verified with experimental data to ensure the accuracy of numerical calculations.

[0767] S3.51 Choosing a solution method: By selecting an appropriate time-dependent or steady-state solver, the mass transfer and reaction kinetic equations are combined to construct a complete coupled model, thereby accurately simulating the dynamic changes in the reaction process;

[0768] The appropriate solver, such as a time-dependent solver or a steady-state solver, is selected to solve the simultaneous equations.

[0769] The suitable solver can be a time-dependent solver, a dynamic solver, or a steady-state solver. The time-dependent solver or dynamic solver, such as ODE45 and ODE15s in MATLAB, is used to solve nonlinear ordinary differential equations and simulate the dynamic changes in the reaction process. As for the steady-state solver, if the final equilibrium state of the system is of interest, a steady-state solver can be selected. However, in the ozone contact tank, the reaction process is mostly dynamic, so a time-dependent solver is more suitable.

[0770] The coupled solution involves combining the mass transfer equation with each reaction kinetic equation to form a complete coupled model, ensuring the interaction between the gas-liquid mass transfer and the chemical reaction.

[0771] S3.52 Simulation: By inputting parameters, initial conditions, and boundary conditions, numerical integration calculations are performed to observe the dynamic trends of the concentrations of different components such as COD, nitrogen, and phosphorus over time, so as to analyze their degradation process;

[0772] The simulation requires calculations to observe the dynamic trends of COD, nitrogen, and phosphorus concentrations over time.

[0773] The model execution involves inputting all parameters, initial conditions, and boundary conditions, then running the solver to perform numerical integration calculations.

[0774] The observation of dynamic trends refers to the key observation of the changes in the concentration of each component over time during the simulation process, such as COD concentration, ozone concentration, or nitrogen, phosphorus, and petroleum components. The COD concentration should show a gradual decreasing trend over time; the ozone concentration should gradually decrease as the reaction proceeds, indicating that it participates in the oxidation reaction; the nitrogen, phosphorus, and petroleum components should show different degradation rates and dynamic changes according to their respective reaction equations.

[0775] S3.53 Check the model results: By comparing the simulation results with the experimental data, verify whether the model output meets expectations, in order to identify potential problems and ensure the accuracy of the model;

[0776] The results of the inspection model need to be verified to see if the model output meets expectations; for example, the COD concentration should decrease over time and the ozone concentration should gradually decrease, indicating that it participated in the reaction.

[0777] The preliminary verification involves comparing simulation results with experimental data. Specifically, it involves first checking whether the COD concentration decreases as expected, then observing whether the ozone concentration is gradually consumed with the reaction, and then checking whether the changing trends of nitrogen and phosphorus components match the experimental measurements.

[0778] The problem identification process involves backtracking and checking whether there are errors in the parameter input, boundary condition settings, and numerical solution process if the simulation results deviate significantly from the expectations.

[0779] S3.6 Result Analysis and Correction: By comparing simulation results with experimental data, model parameters are adjusted to improve simulation accuracy, and finally a reliable mechanism model is generated to provide support for engineering optimization;

[0780] The results analysis and correction involves using charts and visualizations to output the changes in key parameters during the reaction process, and then gradually correcting the model parameters based on the deviation between the simulation results and experimental data, ultimately generating an accurate and reliable mechanism model to support optimization in practical engineering.

[0781] S3.61 Results Output and Visualization: Outputs pollutant concentration change curves, degradation rate charts, and visualizes the distribution of ozone in the pool;

[0782] The data output refers to outputting the concentration data of each component during the simulation process as curves and degradation rate graphs, such as plotting the changes in the concentrations of COD, ammonia nitrogen, phosphorus and petroleum pollutants over time.

[0783] The visualization uses charts and 3D distribution maps to show the distribution and mass transfer process of ozone in the tank, so as to intuitively reflect the mass transfer and reaction inside the reactor.

[0784] S3.62 Correcting Model Parameters: If there is a deviation between the model results and experimental data, adjust the kinetic constants, mass transfer parameters or initial conditions, and rerun the model;

[0785] The comparison between the results of the corrected model parameters and the experiment involves comparing the simulated output with the experimental data to determine whether the model parameters are reasonable.

[0786] The parameter adjustment involves adjusting the kinetic constant, mass transfer coefficient, or initial concentration parameters if deviations exist, rerunning the model, and then using sensitivity analysis to identify the parameters that have the most significant impact on the results through multiple calibrations, thereby gradually reducing the simulation error.

[0787] The aforementioned feedback mechanism involves continuously recording the impact of parameter adjustments during the calibration process, establishing a feedback mechanism, and providing data support for subsequent engineering applications.

[0788] S3.63 Generate the final mechanism model: Based on the corrected parameters, save the final model for subsequent fluid dynamics coupling and practical engineering optimization;

[0789] The finalized model is determined after repeated corrections, by identifying a set of parameters that accurately reflects the actual reaction process, and the final model is saved.

[0790] The engineering application preparation mentioned above ensures that the final model can not only be used for current dynamic simulation, but also provides a basis for subsequent coupling with fluid dynamics models, reactor structure optimization, and adjustment of operating parameters.

[0791] The subsequent optimizations, i.e., the final model, can be further used for sensitivity analysis and process simulation, providing a reliable reference for operation and design in actual engineering.

[0792] Ultimately, the result of "S3. Establishing a Mechanism Model for the Pre-Ozone Contact Tank" is a multi-component mechanism model of the pre-ozone contact tank. This multi-component mechanism model integrates the reaction kinetics, mass transfer equations, and corresponding boundary and initial conditions for various influent pollutants, such as COD, nitrogen, phosphorus, and petroleum, forming a coupled differential equation system that can describe the oxidative degradation and mass transfer processes between ozone and pollutants. The multi-component mechanism model of the pre-ozone contact tank not only quantitatively reflects the dynamic behavior of pollutant concentration and ozone depletion over time, but also provides a theoretical basis and numerical simulation foundation for subsequent CFD coupling and engineering optimization.

[0793] Furthermore, the significance of the phased achievement of "S3. Establishing the Mechanism Model of the Pre-Ozone Contact Pool" is that "S3. Establishing the Mechanism Model of the Pre-Ozone Contact Pool" is not only the core part of the entire mechanism modeling process, but also provides a solid theoretical and data foundation for subsequent numerical simulation, fluid dynamics coupling and practical engineering optimization.

[0794] Firstly, the quantitative description of the reaction process, specifically "S3. Establishing a Mechanism Model of the Pre-Ozone Contact Tank," transforms experimental data into a mathematical model, accurately describing the oxidative degradation and mass transfer processes of various pollutants in the pre-ozone contact tank, thus providing a quantitative basis for understanding the reaction mechanism. Regarding the oxidative degradation process, "S3. Establishing a Mechanism Model of the Pre-Ozone Contact Tank" accurately describes the reaction pathways and degradation rates of different pollutants (such as COD, nitrogen, phosphorus, and petroleum hydrocarbons) in the ozone contact tank by mathematically modeling the degradation rates of pollutants. For example, for COD degradation, a first-order reaction kinetic equation is established using the reaction rate constant between ozone and COD, quantitatively predicting the trend of COD concentration changes over time. Regarding the mass transfer process, the mass transfer of ozone to the liquid phase during the reaction is also a key factor; "S3. Establishing a Mechanism Model of the Pre-Ozone Contact Tank" quantifies the ozone transfer process from the gas phase to the liquid phase by introducing a mass transfer rate equation, helping to simulate the gas-liquid interface mass transfer phenomenon in the reactor. In terms of providing quantitative evidence, "S3. Establishing a Mechanism Model for the Pre-Ozone Contact Tank" enables quantitative analysis of reaction rate, pollutant removal efficiency, and ozone depletion through the establishment of a mathematical model. This provides important quantitative support for understanding the reaction mechanism and optimizing reactor design.

[0795] Secondly, model coupling and system integration, specifically "S3. Establishing the Mechanism Model of the Pre-Ozone Contact Tank," constructs a holistic coupled model system by simultaneously establishing the reaction rate equations and mass transfer equations for each individual component. This lays the foundation for subsequent integration with computational fluid dynamics (CFD) models. From the perspective of the combined reaction and mass transfer, the degradation of each type of pollutant is not only controlled by its own reaction kinetics but also influenced by ozone concentration, and changes in ozone concentration are closely related to its mass transfer rate in the liquid phase. "S3. Establishing the Mechanism Model of the Pre-Ozone Contact Tank" solves the problem of reaction and mass transfer coupling by simultaneously establishing the reaction rate equations and mass transfer equations, enabling the model to consider both processes concurrently and to more realistically simulate actual operating conditions. Building upon the foundation laid by CFD coupling, computational fluid dynamics (CFD) models are used to simulate fluid flow, mixing, and mass transfer processes. "S3. Establishing a Mechanism Model for the Pre-Ozone Contact Tank" has already combined mass transfer and chemical reaction processes. In the future, it can be coupled with CFD models to further improve the accuracy of simulations, which will help optimize reactor design, improve reaction efficiency, and enhance the removal capacity of different pollutants.

[0796] Thirdly, the transition to engineering applications—specifically, the phased achievement of "S3. Establishing a Mechanism Model of the Pre-Ozone Contact Tank"—provides a simulated and predictable tool for subsequent engineering optimization and control strategy formulation, marking a crucial step in the transition from experimental exploration to engineering application. From an engineering optimization perspective, by establishing the model, the reactor's performance can be predicted based on different influent water quality, ozone concentration, and reaction time variables. The model helps select optimal operating conditions, such as ozone dosage and reactor operating time, thereby achieving efficient pollutant removal. From a control strategy formulation perspective, based on the mechanism model, the control system can set appropriate automatic control strategies, such as automatically adjusting the ozone dosage when the ozone concentration is too low, ensuring maximum pollutant degradation and contributing to improved system stability and operational efficiency. In the transition from experimentation to practical application, "S3. Establishing a mechanistic model of the pre-ozone contact tank" strengthens the bridge between laboratory research and field application. By incorporating the kinetic constants and mass transfer coefficient parameters obtained from experiments into the model, it ensures that the model can accurately reflect the actual working conditions. Furthermore, as the model is improved, it provides theoretical support and decision-making basis for future engineering construction, operation and maintenance, and technical optimization.

[0797] Example 5: Specific Implementation of "S4.CAD Modeling and Fluid Dynamics (CFD) 3D Modeling" in the Technical Solution of the Present Invention

[0798] This embodiment provides a detailed explanation of the S4.CAD modeling and CFD 3D modeling in the model building method for an advanced oxidation pre-ozone contact tank of petroleum wastewater described in this invention. "S4.CAD modeling and CFD 3D modeling" mainly involves the combination of 3D geometric modeling and fluid dynamics simulation, gas-liquid two-phase flow and mass transfer simulation, application and optimization of turbulence models, and balancing optimized mesh generation with computational accuracy. Regarding the combination of 3D geometric modeling and fluid dynamics simulation, "S4.CAD modeling and CFD 3D modeling" integrates the CAD drawings of the pre-ozone contact tank with CFD 3D modeling. Advanced simulation tools such as COMSOL and ANSYS Fluent are used to accurately model the tank and perform flow field analysis. Furthermore, by converting the reactor's geometry, dimensions, and inlet / outlet location information into a computational model, a more accurate physical basis is provided for the simulation of fluid dynamics and the reaction process. In terms of simulating gas-liquid two-phase flow and mass transfer, "S4.CAD Modeling and Fluid Dynamics (CFD) 3D Modeling" accurately simulates gas-liquid mixing processes and ozone mass transfer, decay, and oxidation reactions by introducing gas-liquid two-phase flow models and ozone mass transfer and decay models, such as the two-film theory and first-order reaction kinetics equations. It is suitable for handling complex gas-liquid reaction processes, helping to improve the accuracy and predictability of simulations. Regarding the application and optimization of turbulence models, "S4.CAD Modeling and Fluid Dynamics (CFD) 3D Modeling" uses the standard k-ε turbulence model to simulate turbulent flow, considering the characteristics of jet diffusion, rotating flow, and separated flow. This helps to better understand complex turbulent phenomena in fluid flow processes and can realistically simulate flow field changes in gas-liquid mixing, especially the velocity distribution and bubble distribution in non-uniform flow regions. In terms of balancing mesh generation and computational accuracy, "S4.CAD Modeling and Fluid Dynamics (CFD) 3D Modeling" emphasizes mesh generation and refinement strategies, especially at key locations such as inlets and outlets and gas-liquid interfaces. This ensures that the simulation results of flow velocity, mass transfer, and flow regime remain stable under different mesh degrees of freedom, thus guaranteeing a balance between computational accuracy and computational load and avoiding problems of over-computation or insufficient accuracy.

[0799] The core function of "S4.CAD modeling and CFD 3D modeling" in the technical solution of this invention lies in providing a detailed and accurate simulation platform for gas-liquid mixing and mass transfer processes by combining 3D geometric modeling with fluid dynamics simulation. S4.CAD modeling and CFD 3D modeling can accurately predict the flow regime, bubble diffusion, and ozone decay process in the pre-ozone contact tank, and optimize the tank's geometric design, inlet and outlet arrangement, and gas-liquid mixing efficiency to ensure maximum reaction efficiency. This provides theoretical basis and data support for subsequent reaction kinetics model optimization, engineering design, and actual operation. The main innovation of "S4.CAD modeling and CFD 3D modeling" in the technical solution of this invention is the combination of 3D geometric modeling and fluid dynamics simulation to provide a high-precision gas-liquid two-phase flow and mass transfer model. This makes the design and optimization of the pre-ozone contact tank more scientific and controllable, enabling numerical simulation to predict flow regimes, optimize gas-liquid mixing efficiency, and provide theoretical support for engineering design and reaction optimization.

[0800] Furthermore, the specific implementation method of the model building method for an ozone contact tank before advanced oxidation of petroleum wastewater described in this invention, namely "S4.CAD modeling and fluid dynamics (CFD) 3D modeling", is as follows:

[0801] S4.1 Three-dimensional geometric model construction: Based on the CAD drawing of the pre-ozone contact tank, a complete three-dimensional geometric model and computational domain are established in COMSOL to accurately reflect the actual structure and key areas, providing a real physical space basis for subsequent fluid dynamics simulation;

[0802] The construction of the three-dimensional geometric model requires the CAD construction drawings of the actual pre-ozone contact tank as a basis. A three-dimensional geometric model is established in COMSOL software to determine the calculation domain and fully present the key structure, shape and size of the pre-ozone contact tank.

[0803] The construction of the three-dimensional geometric model is mainly based on the CAD construction drawings of the pre-ozone contact tank, and the three-dimensional geometric model is established in COMSOL modeling software;

[0804] The determination of the computational domain involves including key features in the model such as the structure and size of the pool, and the location of the inlet and outlet. At the same time, the boundary of the computational domain is determined to ensure that the simulation reflects the real physical environment.

[0805] The purpose of constructing the three-dimensional geometric model is to provide a detailed digital model foundation by establishing an accurate three-dimensional geometric model, so that subsequent fluid dynamics simulations can be carried out on this model. Furthermore, the definition of the computational domain ensures that the simulation process covers the actual operating area of ​​the pre-ozone contact tank, including the gas-liquid interface and key areas of the inlet and outlet, thereby providing comprehensive flow field data.

[0806] The mechanism of constructing the three-dimensional geometric model is that geometric modeling ensures the accuracy of the calculation. By accurately reflecting the design features of the pool size, inlet and outlet, and pool depth, it provides a foundation for subsequent fluid dynamics analysis. Furthermore, the setting of the computational domain provides a simulated physical space, ensuring that physical phenomena such as fluid flow and gas-liquid mass transfer are accurately represented during the simulation process.

[0807] The operation of constructing the three-dimensional geometric model is as follows: First, based on the CAD drawings, use modeling software such as COMSOL to draw the three-dimensional geometric model, then determine the calculation domain, including the inlet, outlet, liquid region and gas region, and then refine the model to remove parts that are not related to the analysis, such as sludge discharge pipes and valves, to ensure the integrity of key areas.

[0808] S4.11 Data Collection: By acquiring CAD drawings of the pre-ozone contact tank and physicochemical parameters of the wastewater, we ensure the accurate setting of the geometric model and boundary conditions, providing a precise data foundation for subsequent fluid dynamics simulations;

[0809] The data collection involves collecting CAD drawings of the ozone contact tank to ensure detailed description of the tank dimensions, inlet and outlet locations, and other structural features. It also involves collecting physicochemical parameters of the wastewater, including density, viscosity, temperature, pollutant concentration, and gas-liquid mixing data.

[0810] The data collection process involves first collecting CAD drawings of the pre-ozone contact tank to ensure detailed descriptions of the tank's dimensions and inlet / outlet locations. Simultaneously, it involves collecting physicochemical parameters of the wastewater, including density, viscosity, temperature, and pollutant concentrations, for subsequent physical field setting and boundary condition configuration.

[0811] The purpose of data collection is to ensure the accuracy of model input parameters, provide necessary physical and chemical parameters for fluid dynamics simulation, and thus provide a basis for the selection of physical fields and the setting of boundary conditions during the simulation process.

[0812] The data collection mechanism involves collecting the physicochemical parameters of wastewater to simulate the actual characteristics of the wastewater, such as the effect of temperature on fluid viscosity and the effect of pollutant concentration on gas-liquid mixing. CAD drawings ensure the accuracy and completeness of the geometric model.

[0813] The data collection operation specifically involves: first, obtaining the CAD construction drawings of the pre-ozone contact tank to ensure that they contain all relevant dimensions and structural information; then, collecting physicochemical data related to wastewater to ensure the accuracy of the parameters; and finally, summarizing the data and organizing it into a format that can be used by modeling software.

[0814] S4.12 Software Tool Selection: Select appropriate modeling and fluid dynamics simulation software based on project requirements to improve simulation efficiency and accuracy, and ensure a balance between the accuracy of the model and simulation process and the calculation speed.

[0815] The selection of software tools should be based on the appropriate software tools for modeling and simulation, such as COMSOL Multiphysics, ANSYS Fluent, or OpenFOAM, for 3D modeling and fluid dynamics simulation.

[0816] The selection of software tools is based on project requirements, choosing suitable modeling and simulation tools. Common software includes COMSOL Multiphysics, ANSYS Fluent, and OpenFOAM.

[0817] The purpose of selecting software tools is to improve the efficiency and accuracy of modeling and fluid dynamics simulation by choosing appropriate software tools. Different software has different functions, and choosing the right tool can achieve a balance between accuracy and calculation speed.

[0818] The selection mechanism for the software tools is based on the performance of different software tools, including COMSOL Multiphysics, ANSYS Fluent, and OpenFOAM. COMSOL Multiphysics is suitable for simulations of multiphysics coupling and can handle complex gas-liquid mixing and reaction dynamics. ANSYS Fluent excels at handling complex turbulent flow and multiphase flow problems and is suitable for fluid dynamics simulations. OpenFOAM is open source and flexible, suitable for highly customized simulation tasks.

[0819] The process of selecting the software tool is as follows: First, select the most suitable simulation software based on project requirements, budget, and timeframe; then install the software and perform basic configuration to prepare for the modeling and simulation process.

[0820] S4.2 Physical field selection: Based on the fact that the Reynolds number Re is greater than the critical value, a turbulence model is selected, and the Standard k-ε model is adopted to more accurately simulate parameters such as jet diffusion, rotating flow and separation flow in the flow field;

[0821] The physical field selection is based on the fluid flow characteristics of the pre-ozone contact tank, i.e., the Reynolds number is greater than the critical value. A turbulence model is selected, especially the Standard k-ε model, which can more accurately simulate the complex phenomena of jet diffusion, rotating flow and separation flow in the flow field.

[0822] The role of the physical field selection is to accurately describe the turbulence effect in fluid flow by selecting an appropriate turbulence model. Especially at high Reynolds numbers, turbulent flow becomes the main flow form, providing the necessary physical basis for fluid dynamics simulation and ensuring the accuracy of simulation results.

[0823] The mechanism for selecting the physical field is based on the Standard k-ε model, a widely used turbulence model suitable for describing typical flows that include diffusion, rotation, and separation phenomena. It describes the intensity and behavior of turbulence by calculating the turbulent kinetic energy (k) and turbulent dissipation rate (ε).

[0824] The operation of selecting the physical field is as follows: first, based on the calculation of the Reynolds number, determine whether the flow is turbulent, select an appropriate turbulence model, then set the relevant parameters of the turbulence model, such as the initial values ​​of k and ε, and configure them in the software;

[0825] S4.21 Establishing a 3D geometric model: Based on CAD drawings, a 3D model containing only the key pool structure is constructed in the modeling software, and irrelevant attachments are removed, thereby ensuring that the model is consistent with the actual structure in the key areas, providing an accurate physical basis for subsequent fluid dynamics simulation;

[0826] The establishment of the three-dimensional geometric model requires the use of modeling software to draw the three-dimensional geometric model based on CAD drawings, retaining only the pool structure and removing irrelevant accessories such as sludge discharge pipes and valves, to ensure that the model is consistent with the actual structure in key areas, such as the position of the inlet and outlet, the depth, width and length of the pool.

[0827] The establishment of the three-dimensional geometric model is based on CAD drawings and constructed in modeling software. The focus is on the pool structure, and non-critical accessories such as sludge discharge pipes and valves are removed to ensure that the model is consistent with the actual structure in key areas.

[0828] The purpose of establishing a three-dimensional geometric model is that an accurate three-dimensional geometric model is the foundation for subsequent fluid dynamics simulation. By ensuring that the model is consistent with the actual situation in the areas of inlet and outlet and pool depth, a reliable physical space is provided for fluid flow and reaction processes.

[0829] The mechanism for establishing the three-dimensional geometric model is that geometric modeling is implemented through software, which transforms the real structure into a virtual model, providing space for subsequent numerical analysis. Irrelevant attachments are then removed to ensure that the simulation is more focused on the core area of ​​the pool, avoiding unnecessary computation.

[0830] The operation of establishing a three-dimensional geometric model is as follows: First, use modeling software (such as COMSOL) to draw a geometric model to ensure that key areas, such as inlet and outlet water outlets and pool dimensions, are consistent with the actual situation, and remove irrelevant accessories;

[0831] S4.22 Define the computational domain: Define the computational domain as the internal area of ​​the pre-ozone contact tank, including the inlet, outlet, liquid region, and gas region, to ensure that the numerical simulation covers all critical fluid flow and reaction areas;

[0832] The defined calculation domain includes the internal area of ​​the pre-ozone contact tank, defined as a liquid area (wastewater) and a gas area (ozone gas), and also includes the inlet and outlet.

[0833] The purpose of defining the computational domain is to define the scope of the numerical simulation, which is a key part of the subsequent fluid dynamics analysis. It ensures that the fluid dynamics simulation can cover all operating areas, such as the inlet, outlet, and gas-liquid mixing zone.

[0834] The mechanism for determining the computational domain is that the definition of the computational domain is directly related to the pool structure and operating conditions, ensuring that the model can simulate the fluid behavior within the pool, especially the changes in the gas-liquid interface and fluid flow.

[0835] The operation of determining the computational domain specifically involves: calibrating the internal region of the pool in the modeling software, specifying the inlet and outlet, liquid region, and gas region, and ensuring that the computational domain covers all flow and reaction-related regions.

[0836] S4.3 Boundary Condition Setting: Set specific fluid boundary conditions for each key area (inlet, free surface, wall and outlet) of the front ozone contact tank in the numerical simulation, and define convergence criteria to ensure that the fluid flow and gas-liquid exchange in the simulation process accurately reflect the actual operating environment.

[0837] The boundary conditions are set, including the inlet, free surface, wall, outlet, and convergence determination. The inlet is set as a velocity inlet, with a calculated flow velocity of approximately 0.12 m / s. The free surface is set as degassing to simulate gas (such as ozone) escaping from the surface. The wall is set as a no-slip wall, meaning the fluid velocity on the wall is zero, and friction is not considered. The outlet is a free outflow, operating under standard atmospheric pressure (101.325 kPa) and a gravitational acceleration of 9.8 m / s². 2 Vertically downward; The convergence determination, which determines whether convergence has occurred, is mainly based on the stability of variable residuals, flow rate, and quality, primarily judging whether the variable residuals have reached preset values ​​and whether the detection surface speed and quality are stable.

[0838] The role of boundary condition setting is that it is the foundation of numerical simulation, determining the calculation range and fluid behavior of the model. By correctly setting boundary conditions, it is ensured that the fluid in the simulation is consistent with the actual operating environment, thereby improving the accuracy of the model. Setting the boundary conditions for velocity inlet and degassing can accurately simulate the fluid inlet and outlet, gas-liquid exchange and reaction process in the contact pool. At this time, the no-slip wall condition ensures that the fluid velocity in contact with the wall is zero, which is crucial for the correct description of the flow. In addition, the free outflow setting ensures that the outlet pressure is balanced with the atmospheric pressure, avoiding unreasonable pressure fluctuations.

[0839] The mechanism for setting the boundary conditions includes the functions of an inlet, a degassing free surface, a no-slip wall, and free outflow. The inlet (velocity inlet) sets the flow velocity when the fluid enters the contact pool, ensuring that the simulated water flow matches the actual input. The degassing free surface simulates gas escape from the liquid surface, applicable to gas dissolution and release processes, such as ozone dissolving into water or escaping. The no-slip wall model assumes that the fluid velocity on the wall is zero, simulating the interaction between the fluid and the solid wall in real-world conditions. The free outflow condition is set at the outlet, assuming that the fluid at that location is not restricted by external forces and that the pressure is below atmospheric pressure.

[0840] The boundary condition setting operation is as follows: First, in the modeling software COMSOL, a velocity inlet is set for the inlet, and the water flow velocity is calculated according to the design flow rate and pool size, such as 0.12 m / s. At this time, a degassing boundary is set for the free liquid surface to simulate the gas escape process. Then, a no-slip wall condition is set to ensure that the fluid velocity on the wall is zero. Then, the outlet is set to free outflow to ensure that the fluid pressure at the outlet is the same as the atmospheric pressure. Finally, convergence criteria are set, including the stability of variable residuals, flow velocity, and mass, to ensure that the numerical simulation results are converged and reliable.

[0841] S4.31 Pool Material Settings: Define the pool as concrete and set the physical properties of the pool (such as density and thermal conductivity) to ensure that the heat transfer and flow characteristics of the solid part in the simulation are consistent with the actual situation;

[0842] The pool material needs to be defined as concrete, and corresponding physical properties such as density and thermal conductivity need to be set.

[0843] The pool material setting refers to defining the pool material as concrete and setting corresponding physical properties, such as density and thermal conductivity.

[0844] The role of setting the pool material is to ensure that the solid parts in the model reflect the real physical properties, and the physical properties of concrete can affect the characteristics of heat transfer and fluid flow resistance, thereby affecting the simulation results.

[0845] The mechanism of the pool material setting is based on the pool material, which is constructed of concrete. The density and thermal conductivity of concrete have a significant impact on the flow of fluid and heat exchange.

[0846] The process of setting the pool material is as follows: First, select concrete as the material type for the pool in the modeling software, and then input the physical properties of the concrete, such as a density of 2400 kg / m³. 3 Thermal conductivity;

[0847] S4.32 Liquid and Gas Parameter Settings: Set key physical parameters (such as density, viscosity, temperature and diffusion coefficient) for wastewater and air-ozone mixtures to ensure the accuracy of gas-liquid two-phase flow, mass transfer process and related reactions in numerical simulation.

[0848] The liquid and gas parameters are set as follows: the liquid is set to sewage, and its density, viscosity and temperature are input; the gas is set to a mixture of air and ozone, and its density, viscosity, diffusion coefficient and component ratio are input.

[0849] The liquid and gas parameter settings include liquid and gas; the liquid is set to sewage, and its physical parameters such as density, viscosity and temperature are input; the gas is set to a mixture of air and ozone, and its density, viscosity, diffusion coefficient and component ratio are input.

[0850] The purpose of setting the liquid and gas parameters is that they are crucial for simulating gas-liquid two-phase flow. Accurate physical parameters ensure that the flow and mass transfer processes are correctly simulated. By setting the parameters of the liquid (sewage) and gas (ozone mixture), the bubble behavior, gas-liquid mass transfer efficiency, and ozone decay can be accurately predicted.

[0851] The mechanism of setting the liquid and gas parameters specifically refers to the physical parameters of the liquid and the gas. The physical parameters of the liquid determine the flow properties of the water flow; density affects buoyancy and flow resistance, viscosity affects the viscosity of the flow, and temperature also has a certain influence on these parameters. The physical parameters of the gas, such as the density, viscosity, and diffusion coefficient of the mixture of air and ozone, affect the diffusion rate of bubbles and the dissolution process of the gas in water.

[0852] The operation of setting the liquid and gas parameters is as follows: first, set the liquid parameters in the modeling software, and then input the density of the wastewater (approximately 1000 kg / m³). 3The viscosity (approximately 0.001 Pa·s) is determined and adjusted according to the design temperature. Subsequently, gas parameters such as the density of air and ozone (oxygen density is approximately 1.225 kg / m³) are set. 3 Viscosity and diffusion coefficient are used to ensure that the simulated gas-liquid mass transfer process conforms to the actual situation, and the gas component ratio is set to oxygen and ozone, with an ozone volume fraction of 0.15.

[0853] S4.4 Pre-ozone contact tank flow reaction simulation: A numerical model based on the two-film theory and first-order reaction kinetics is constructed to evaluate and optimize the wastewater treatment effect by simulating the mass transfer, decay and oxidation reaction process of ozone and organic matter in wastewater in the gas-liquid two phases.

[0854] The simulation of the flow reaction in the pre-ozone contact tank requires that after ozone is introduced into the contact tank, the gas and liquid phases mix and undergo gas-liquid mass transfer, ozone decay, and oxidation reactions with organic matter. The mass transfer process then employs the two-film theory, with the mass transfer rate determined by the mass transfer coefficient and the bubble specific surface area parameter. Finally, the ozone decay process in the water is represented by a first-order reaction kinetic equation, with a commonly used decay coefficient of 2.5 × 10⁻⁶. -4 s -1 And the selected ozone mass transfer coefficient K L =1.0×10 -6 m / s;

[0855] The flow reaction simulation of the pre-ozone contact tank illustrates the process after ozone is introduced into the tank. The gas and liquid phases mix, and gas-liquid mass transfer, ozone decay, and oxidation reactions with organic matter occur. The two-film theory is then used to simulate the gas-liquid mass transfer rate, which is influenced by the mass transfer coefficient and the specific surface area of ​​the bubbles. Therefore, the ozone decay process in the water is represented by a first-order reaction kinetic equation, with a commonly used decay coefficient of 2.5 × 10⁻⁶. -4 s -1 At the same time, the ozone mass transfer coefficient K is selected. L =1.0×10 -6 m / s;

[0856] The role of the pre-ozone contact tank flow reaction simulation includes gas-liquid mass transfer simulation and attenuation process modeling. The gas-liquid mass transfer simulation, by simulating the gas-liquid two-phase flow and ozone mass transfer process, can evaluate the diffusion capacity of bubbles and the efficiency of ozone attenuation in water, helping to optimize the reaction performance of the contact tank. The attenuation process modeling is crucial in wastewater treatment; by simulating ozone attenuation, the optimal dosage can be determined to ensure the best wastewater treatment effect.

[0857] The mechanism of the flow reaction simulation in the pre-ozone contact tank is based on the two-membrane theory and first-order reaction kinetics.

[0858] The two-film theory assumes that gas-liquid mass transfer occurs through two thin films at the gas-liquid interface. Gas molecules are transferred from the gas phase to the liquid phase through these two films. The mass transfer coefficient of the films and the surface area of ​​the bubbles are the key factors affecting the gas-liquid mass transfer efficiency.

[0859] The first-order reaction kinetics described above represent the ozone decay process, where the decay rate is directly proportional to the ozone concentration, and the rate constant (decay coefficient) is 2.5 × 10⁻⁶. -4 s -1 ;

[0860] The operation of the mechanism for simulating the flow reaction in the pre-ozone contact tank is as follows: First, define the mass transfer coefficient K. L Based on the previous experimental data or to determine a reasonable value, the gas-liquid mixing process is established in the COMSOL simulation software, and the two-film theoretical parameters are set. Then, by solving the reaction equation, the attenuation process of ozone in water and its effects are obtained.

[0861] S4.41 Selection of Turbulence Model: The flow regime is determined based on the fluid Reynolds number Re, and the standard k-ε turbulence model is used to simulate complex turbulence phenomena such as diffusion, separation, and rotating flow to ensure the accuracy of numerical simulation;

[0862] The selection of the turbulence model requires determining the fluid characteristics based on the Reynolds number Re. The standard k-ε model is selected to simulate turbulence, which is suitable for diffusion, separation flow and rotating flow.

[0863] The selection of the turbulence model is based on the Reynolds number Re to determine the characteristics of the fluid. The standard k-ε turbulence model is selected to simulate turbulent flow, which is suitable for handling jet diffusion, rotating flow and separated flow phenomena.

[0864] The purpose of selecting a turbulence model is to accurately simulate the turbulence effects in complex flows by choosing a suitable turbulence model, ensuring that the simulation results reflect the actual flow characteristics and improving the accuracy of reactor design.

[0865] The mechanism of selecting the turbulence model is based on the k-ε model, which describes the intensity and dissipation characteristics of turbulence by calculating the turbulent kinetic energy k and the turbulent dissipation rate. This model has good performance in dealing with complex flows such as diffusion, rotating flow and fluid separation.

[0866] The operation of selecting a turbulence model specifically involves first calculating the Reynolds number to determine whether the fluid is in a turbulent state, then selecting the k-ε turbulence model in the modeling software, and setting initial values ​​and boundary conditions.

[0867] S4.42 Define boundary conditions: Set various boundary conditions for the model, including inlet, free surface, no-slip wall and outlet, to ensure that the flow behavior of the fluid in the simulation truly reflects the actual operating environment;

[0868] The defined boundary conditions are to set different boundary conditions to simulate real-world conditions, including inlet velocity inlet, free liquid surface degassing, wall no-slip boundary conditions, and outlet free outflow.

[0869] The purpose of defining boundary conditions is to define the physical limitations of the simulation region, ensuring that the fluid in the simulation is consistent with the actual operating environment, thereby ensuring the reliability of the simulation results.

[0870] The mechanism for defining boundary conditions includes the inlet, free liquid surface, non-slip wall surface, and outlet.

[0871] The inlet is set with a velocity inlet, i.e., the inlet flow velocity. The flow velocity is calculated and input, such as 0.12 m / s, to ensure that the flow conditions at the inlet meet the actual operation.

[0872] The free liquid surface is set to Degassing, which simulates gas detaching from the liquid surface and escaping through the liquid surface.

[0873] The no-slip wall is set as a no-slip boundary condition, ignoring friction and setting the wall surface velocity to zero, to simulate the interaction between a solid wall and a fluid.

[0874] The outlet is set to free outflow, and the free outflow boundary conditions of the outlet are set to maintain pressure balance;

[0875] The operation of defining boundary conditions specifically involves: first setting each boundary condition in the modeling software, and then adjusting it according to the inlet flow rate, liquid surface gas escape, and wall friction parameters.

[0876] S4.5 Gas-Liquid Two-Phase Flow Model Selection and Parameter Settings: By selecting a multiphase flow model, reasonably configuring drag and lift models, and customizing ozone mass transfer and decay functions, the gas-liquid mixing process and the mass transfer and decay of ozone in water can be accurately simulated.

[0877] The selection and parameter settings of the gas-liquid two-phase flow model involve selecting a multiphase flow model to simulate the gas-liquid mixing process, setting relevant mass transfer parameters, including the drag force model Symmetric and the lift force model Tomiyama, and defining custom functions (UDFs) for the ozone mass transfer and decay process.

[0878] The purpose of selecting and setting the parameters of the gas-liquid two-phase flow model is to enable the multiphase flow model to simulate the gas-liquid two-phase flow characteristics, provide support for the ozone mass transfer process, and improve the simulation accuracy by reasonably setting the drag and lift models, so as to ensure the effective mixing and decay of ozone in water.

[0879] The mechanism for selecting and setting parameters of the gas-liquid two-phase flow model includes the Symmetric drag model, the Tomiyama lift model, UDF, degassing boundary, air intake, and ignoring other components in the air and the liquid phase. The Symmetric drag model describes the interaction force between the bubble and the liquid, helping to accurately simulate bubble motion. The Tomiyama lift model simulates the forces acting on the bubble, affecting its lift and interaction with the liquid. The UDF is a custom function characterizing ozone mass transfer and decay; custom functions are used to model more complex mass transfer and reaction processes, such as ozone mass transfer and decay. The degassing boundary defines the free liquid surface. The air intake is set as a mass inlet with intake rates of 0.0091 kg / s and 0.013 kg / s, respectively, and the ozone volume fraction in the bubble is 0.15%. Ignoring other components in the air means only setting two gas phase components: oxygen and ozone. The liquid phase also includes water, liquid ozone, and liquid oxygen to realize the process of ozone decaying into oxygen.

[0880] The operation of selecting the gas-liquid two-phase flow model and setting parameters is as follows: first, select the multiphase flow model in the simulation software, then set the relevant mass transfer coefficient, drag force and lift force model, and then write and integrate custom function (UDF) to represent the ozone mass transfer and decay process.

[0881] S4.51 Selecting the gas-liquid model: By adopting a multiphase flow model and setting the mass transfer parameters of ozone, the mass exchange between bubbles and liquid in the reactor and the transfer process of ozone from the gas phase to the liquid phase are accurately simulated.

[0882] The selection of the gas-liquid model requires the use of a multiphase flow model to simulate the gas-liquid mixing process; then ozone is used as a soluble gas, and its mass transfer parameters are set.

[0883] The selection of the gas-liquid model refers to selecting a multiphase flow model to simulate the gas-liquid mixing process, especially the process of gas (such as ozone) dissolving into liquid in a reactor. Ozone is then used as a soluble gas, and its mass transfer parameters are set to accurately simulate the process of ozone being transferred from the gas phase to the liquid phase.

[0884] The purpose of selecting the gas-liquid model is that the multiphase flow model is used to simulate the interaction between bubbles and liquids, especially the mass transfer process of bubbles in liquids. Bubble transfer and dissolution are the core of the gas-liquid reaction process. Correctly selecting the gas-liquid model can effectively improve the simulation accuracy. In addition, the ozone mass transfer parameter settings ensure that ozone can be effectively transferred from the gas phase to the liquid phase through the gas-liquid interface, optimizing its dissolution efficiency and reactivity.

[0885] The mechanism for selecting the gas-liquid model includes a multiphase flow model and ozone mass transfer. The multiphase flow model involves bubbles rising in the liquid and exchanging mass with it. Using an appropriate multiphase flow model, such as an Eulerian-Lagrange or Eulerian-Eulerian model, bubble transport, turbulence effects, and liquid mixing processes at the gas-liquid interface can be simulated. Regarding ozone mass transfer, ozone, as a soluble gas, dissolves in the liquid following the gas-liquid mass transfer process, transferring ozone from the gas phase to the liquid phase through the bubble interface.

[0886] The operation of selecting the gas-liquid model is as follows: first, select the multiphase flow model in the modeling software, such as COMSOL or Fluent, then set ozone as a soluble gas and input its gas-phase and liquid-phase mass transfer parameters, such as the mass transfer coefficient of ozone and the surface area of ​​the bubbles.

[0887] S4.52 Force Setting: By defining gas-liquid interaction forces, including drag force, virtual mass force, lift force and surface tension, the interaction forces between bubbles and liquids can be accurately simulated, thereby simulating bubble motion trajectory, size change and mass transfer process;

[0888] The force setting requires defining the interaction forces, including drag, virtual mass force, lift, and surface tension;

[0889] The force setting defines the forces between the bubble and the liquid, including drag, virtual mass force, lift, and surface tension. The drag is the force exerted by the liquid on the bubble as it moves within the liquid. The virtual mass force is the additional force required by the liquid to respond to the bubble's acceleration or deceleration. The lift is the effect of buoyancy on the bubble within the liquid, particularly its rotation and path. The surface tension is the intermolecular interaction force at the gas-liquid interface, affecting the bubble's shape and stability.

[0890] The purpose of the force setting is that the force setting has an important influence on gas-liquid flow and bubble behavior, especially during the process of bubble rising, propagation and interaction with liquid. The above forces will significantly affect the bubble trajectory, bubble size and bubble mass transfer efficiency.

[0891] The mechanisms of these forces include drag model, virtual mass force, lift model, and surface tension. The drag model determines the velocity of the bubble by calculating the drag force of the bubble in the liquid. Commonly used drag models include the Schiller-Naumann model. The virtual mass force describes the virtual mass effect generated by the bubble due to acceleration, which plays a crucial role in the dynamics of bubbles in flow. The lift model calculates lift based on the bubble shape and flow velocity, affecting the bubble's path in the liquid, especially in irregular or rotating flows. Surface tension affects the shape and size of the bubble, and plays a decisive role, particularly in the case of small and microbubbles.

[0892] The operation of the force setting is as follows: First, in the multiphase flow simulation, enable the bubble model and define the interaction forces. Then, input the required drag force model, such as the Schiller-Naumann model, virtual mass force, lift force and surface tension coefficient. Then, set the parameters to ensure that the influence of these forces can be accurately calculated and considered in the simulation process.

[0893] S4.53 Mass transfer and decay model setting: By adopting the two-film theory and first-order reaction kinetics, the mass transfer process and decay reaction of ozone in water are accurately simulated to ensure that the simulation results are consistent with the actual reaction conditions.

[0894] The mass transfer and attenuation model includes a mass transfer model and an attenuation model; the mass transfer model uses the two-film theory and sets the overall mass transfer coefficient K = 1.0 × 10⁻⁶. -6 m / s; the decay model uses a first-order reaction kinetic equation, with an ozone decay coefficient of 2.5 × 10⁻⁶ m / s. -4 s -1 ;

[0895] The functions of the mass transfer model and the decay model are specifically defined as follows: the mass transfer model, based on the two-film theory, provides an accurate description of the gas-liquid mass transfer process, simulates the ozone transport efficiency at the bubble interface, and thus determines the ozone dissolution and oxidation reaction rates; the decay model, by setting the ozone decay coefficient, can simulate the ozone consumption process in water, ensuring that the ozone concentration decay in the system is consistent with the actual reaction kinetics.

[0896] The mechanisms underlying the mass transfer and attenuation models are the two-film theory and first-order reaction kinetics, respectively. The two-film theory assumes the bubble surface is a thin film layer, with gas moving from the gas phase through this layer to the gas-liquid interface, and then through the liquid film layer to the liquid phase. The mass transfer rate is influenced by the gas-liquid interface area, the mass transfer coefficient, and the concentration gradient. The first-order reaction kinetics posits that ozone attenuation in water follows first-order reaction kinetics, with the attenuation rate proportional to the ozone concentration and a rate constant of 2.5 × 10⁻⁶. -4 s -1 ;

[0897] The operation of setting the mass transfer model and attenuation model specifically involves: setting the two-film theory in the simulation software to describe the mass transfer process at the gas-liquid interface, and inputting the overall mass transfer coefficient K = 1.0 × 10⁻⁶. -6 m / s, then a first-order reaction kinetic equation is selected to describe the ozone decay process, and the decay coefficient is set to 2.5 × 10 m / s. -4 s -1 Subsequently, the mass transfer coefficient and reaction rate in the simulation were adjusted to reflect the actual reaction conditions and to verify the mass transfer and decay behavior of ozone in the system.

[0898] S4.6 Mesh Generation and Refinement: By meshing the 3D geometric model and refining the mesh in key areas, a balance between computational accuracy and computational cost in the numerical simulation is ensured.

[0899] The mesh generation and refinement process requires the established three-dimensional geometric model to be meshed, representative points to be selected to compare the flow velocity changes under different mesh degrees of freedom until it does not change with mesh refinement, and then appropriate mesh refinement is performed at key locations to achieve a balance between computational accuracy and computational load.

[0900] The mesh generation and refinement are performed on the established three-dimensional geometric model, especially in key areas such as the water inlet and gas-liquid interface, to ensure a balance between computational accuracy and computational load.

[0901] The purpose of mesh generation and refinement is that mesh generation is a key step in ensuring the accuracy of numerical simulation. By refining the mesh in key areas, the local calculation accuracy can be improved and calculation errors can be avoided.

[0902] The mechanism of mesh generation and refinement is that mesh generation determines the resolution of the solver's calculation. Especially in areas with large flow velocity variations, such as inlets and gas-liquid interfaces, mesh refinement can capture more detailed flow field changes.

[0903] The mesh generation and refinement operation is as follows: First, the mesh is generated in the simulation software, and the key areas are refined. Then, the flow velocity changes under different mesh degrees of freedom are compared to ensure the stability of the calculation results. Finally, an appropriate solver, such as a static separation solver, is selected, and preliminary calculations are performed.

[0904] S4.61 Mesh Generation: The computational domain is meshed, and the mesh is densified in key areas such as the inlet and outlet and the gas-liquid interface to ensure computational accuracy and stability.

[0905] The aforementioned meshing requires dividing the computational domain into meshes, with key areas such as inlets and outlets and gas-liquid interfaces requiring mesh refinement to determine the mesh degrees of freedom and ensure that the flow velocity and flow regime remain stable under different degrees of freedom.

[0906] The aforementioned meshing involves dividing the computational domain into meshes, especially refining the mesh in key areas such as the inlet / outlet and the gas-liquid interface, and then determining the mesh degrees of freedom to ensure that the flow velocity and flow regime remain stable under different degrees of freedom.

[0907] The purpose of mesh generation is that mesh generation is a fundamental step in numerical simulation. Good mesh generation can ensure the accuracy and stability of calculation results. Mesh refinement can improve the calculation accuracy of important areas, such as inlets and outlets and gas-liquid interfaces, avoid the amplification of numerical errors, and capture the complex flow characteristics of gas-liquid interfaces, especially the effects of bubble transport, gas-liquid mixing and reaction processes.

[0908] The mechanism of mesh generation is based on the fact that the fineness of mesh generation directly affects the accuracy and efficiency of calculation. That is, by refining the mesh in key areas, such as the inlet, outlet and gas-liquid interface, the analytical accuracy of local flow can be improved, and the flow instability or numerical inaccuracy caused by an overly coarse mesh can be avoided.

[0909] The mechanism of mesh generation is also based on degrees of freedom. That is, the degrees of freedom of the mesh determines the accuracy and complexity of the calculation. Increasing the degrees of freedom helps to capture the flow characteristics of the fluid more accurately.

[0910] The mesh generation operation is as follows: First, a preliminary mesh generation is performed in the calculation software to ensure that the computational domain is reasonably divided. Then, the mesh is refined in important areas such as the inlet and outlet and the gas-liquid interface to ensure that the details of these areas are captured, thereby ensuring that the mesh has a moderate degree of freedom, neither too much nor too little, and ensuring the stability of the calculation.

[0911] S4.62 Solver Selection: By selecting a static separation solver, setting weak constraints, and inputting initial values, stable calculations of the flow regime and pollutant mass transfer process of the gas-liquid mixture are ensured.

[0912] The solution selection requires the use of a static separation solver, setting weak constraints, inputting initial values, and calculating the flow regime and pollutant mass transfer process of the gas-liquid mixture.

[0913] The solver selection involves choosing a static separation solver, setting weak constraints, inputting initial values, performing calculations, and simulating the flow regime and pollutant mass transfer process of the gas-liquid mixture.

[0914] The role of the solver selection is that the solver selection determines the stability and efficiency of the calculation process. The static separation solver is suitable for handling flow fields that do not change with time, especially the mass transfer process in mul...

Claims

1. A method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater, characterized in that, Includes the following steps: S1. Analysis of influent components: Analysis of the components of each pollutant in the influent of the ozone contact tank and their initial concentrations; S2. Experimental simulation of the reaction process in the pre-ozone contact tank: The reaction process of each sub-substance in the pre-ozone contact tank is simulated by experimental methods to determine the concentration changes, chemical structure changes and corresponding reaction kinetic equations and constants of each sub-component; S3. Establish a mechanism model for the pre-ozone contact tank: Based on the mechanism models established in S1. and S2., a multi-component degradation reaction model for the pre-ozone contact tank is formed, taking the influent components and their reaction kinetics and the reaction process of the substances as inputs. S4. CAD drawing modeling and CFD three-dimensional modeling: Based on the CAD drawings, a three-dimensional geometric model of the pre-ozone contact tank is established, and a fluid dynamics model is constructed in CFD software, including flow field establishment, turbulence model selection, boundary condition setting, and numerical simulation of gas-liquid mass transfer and ozone decay reaction. S5. Coupling of Mechanism Model and Fluid Dynamics Model: The mechanism model described in S3. is coupled with the CFD fluid dynamics model described in S4. to form a comprehensive pre-ozone contact tank simulation model that simultaneously reflects the hydraulic flow state and chemical reaction mechanism; S6. Steady-state simulation, sensitivity analysis and parameter correction: Perform steady-state simulation, sensitivity analysis and parameter correction on the simulation model; S7. Model self-learning and self-calibration and output of optimal ozone control: Using a self-learning and self-calibration mechanism, the optimal ozone dosing control strategy for the front ozone contact tank is output.

2. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 1, characterized in that, S1. Analyzing the influent components includes the following sub-steps: S1.1 Sampling and Detection: Collect raw petroleum wastewater samples from sampling points and use different experimental detection methods to determine different pollutants in the influent; S1.11 Determine the sampling method: Collect uniformly mixed wastewater samples from representative sampling points, and determine an appropriate sampling volume according to the testing requirements to ensure that the samples can accurately reflect the influent water quality; S1.12 Sample Preservation: By employing specialized preservation measures for different components, we ensure that the collected samples remain stable in composition during analysis, unaffected by microbial activity or chemical reactions. S1.2 The influent components are divided into four categories: COD components, nitrogen components, phosphorus components, and petroleum components; S1.21 COD Component Analysis: By measuring total COD, particulate and dissolved COD, biodegradable and non-biodegradable COD, and types of organic matter, the organic pollutant load and degradation characteristics in wastewater are assessed. S1.22 Nitrogen Component Analysis: By measuring the concentrations of total nitrogen, ammonia nitrogen, nitrate nitrogen, and nitrite nitrogen in wastewater, the distribution of nitrogen speciation is analyzed to assess the nitrogen pollution level, nitrogen conversion process, and the need for subsequent denitrification treatment. S1.23 Phosphorus Component Analysis: The phosphorus pollution level in wastewater is assessed by measuring the concentrations of total phosphorus, orthophosphate, particulate and dissolved phosphorus, and organic phosphorus. S1.24 Petroleum Component Analysis: By measuring the concentration and separation of total petroleum substances, specific components and their ozone requirements are analyzed; S1.3 Microbial component test: Analysis of microbial communities such as autotrophic bacteria and aerobic bacteria; S1.31 Total bacterial concentration determination: The total concentration of microorganisms in the sample is determined by microscopic counting or flow cytometry; S1.32 Population composition analysis: Microbial population composition, including autotrophic and heterotrophic bacteria, was analyzed using 16S rRNA gene sequencing or fluorescence in situ hybridization (FISH) technology. S1.33 Activity Assay: The bioactivity of microorganisms is assessed using an activity staining method. S1.34 Bioload Assessment: Calculate the bioload level in wastewater based on microbial concentration and population structure; S1.4 Determine the final influent component list: Based on the experimental measurement results, confirm the specific influent components and the initial concentration levels of various pollutants in the pre-ozone contact tank; S1.41 Summary of test results: Compile the test results of COD, nitrogen, phosphorus, microorganisms and petroleum components, and construct a comprehensive spectrum of influent components; S1.42 Component Correlation Analysis: This involves analyzing the relationships between different pollutants. S1.43 Preliminary assessment of water quality characteristics: Based on the component data, assess the main pollution characteristics of the wastewater.

3. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 1, characterized in that, S2. The experimental method simulates the reaction process of the pre-ozone contact tank, including the following sub-steps: S2.1 Construction of small-scale test device: Customize a small-scale pre-ozone contact pool test device according to CAD drawings, and then arrange ozone dosing in the small contact pool of the laboratory according to the ozone controller dosing method at the sampling location. S2.2 Small-scale test water configuration: Based on the components and concentrations in the petroleum wastewater, water is allocated according to the actual concentration range to ensure that the composition of the influent to the small pre-ozone contact tank matches the actual working conditions as closely as possible. S2.21 Preparation of influent sample: Based on the sampling results and the actual wastewater characteristics, prepare simulated influent according to the proportion; S2.22 Ozone generation system configuration: Install the ozone generator and calibrate its output concentration and flow rate, and determine the ozone dosing method and dosage range; S2.23 Monitoring Equipment Preparation: Configure online monitoring instruments and laboratory equipment, including COD analyzer, ammonia nitrogen, nitrate nitrogen, total phosphorus analyzer, GC-MS gas chromatography-mass spectrometry system, pH meter and dissolved oxygen meter, for detecting changes in chemical structure and concentration changes of each component; S2.3 Determine the reaction process of sub-substances in the pre-ozone contact tank: In a small-scale experimental device, measure the concentration changes and chemical structure changes of each sub-component under the action of ozone, analyze its degradation pathway, and determine the reaction kinetic equations and constants of each sub-substance; S2.31 Experimental Operation: By setting initial conditions, starting the reaction, and taking samples at different time intervals, the ozone contact process under actual working conditions is simulated to monitor pollutant degradation and changes in related parameters; S2.32 Detailed Component Reaction Analysis: By measuring the changes in the concentration and chemical structure of each pollutant, and analyzing the effects of ozone concentration, gas-liquid mixing, temperature and pH on reaction efficiency, the degradation mechanism of each component is sought and the optimal operating conditions are determined. S2.33 Kinetic Parameter Extraction: The reaction kinetic model is fitted using experimental data to extract reaction kinetic parameters in order to quantitatively describe the reaction process of each pollutant component; S2.34 Experimental Data Analysis and Optimization: By analyzing the degradation efficiency, ozone utilization rate and degradation mechanism of each pollutant at different time points, and adjusting the experimental parameters based on data feedback, the best removal effect can be obtained.

4. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 1, characterized in that, S3. Establish a mechanistic model of the pre-ozone contact tank, including the following sub-steps: S3.1 Model Input Components: The various components and their initial concentrations determined in S1. and S2. are used as model input parameters; S3.11 Collect experimental data: Under strictly controlled sampling conditions, collect the concentrations of organic matter, nutrients, and petroleum pollutants in the influent, as well as the corresponding reaction kinetic parameters; S3.12 Determine the reaction type: Identify the main chemical reactions and physical processes in the pre-ozone contact tank, and construct a kinetic model to describe their coupling effects; S3.13 Selecting Modeling Tools: Select a suitable modeling tool for writing and solving equations; S3.2 Kinetic Equations and Constants: The reaction kinetic equations and reaction kinetic constants obtained in S2. are incorporated into the model, and degradation equations are established for each type of substance; S3.21 Classification and Development of Rate Equations: Establish specific degradation equations for different types of pollutants; S3.22 Mass transfer and reaction combined: The mass transfer process of ozone from the gas phase to the liquid phase is coupled with the chemical reaction rate in the liquid phase to reveal and predict how the overall reaction rate is affected by the concentration difference at the gas-liquid interface and the mass transfer coefficient. The mass transfer rate equation is as follows: J=K L α·A·([O3] gas -[O3] liquid ) Where J represents the mass transfer flux of ozone at the gas-liquid interface; K L α represents the liquid-phase mass transfer coefficient; α represents the gas solubility coefficient; A is the effective specific surface area within the reactor, i.e., the total area of ​​the gas-liquid interface; [O3] gas [O3] represents the concentration of ozone in the liquid phase at equilibrium. liquid This represents the actual ozone concentration in the liquid phase. S3.3 Integrated Mechanism Model: The reaction equations of each component obtained from S3.1 and S3.2 are assembled and coupled to form a multi-component mechanism model of the pre-ozone contact tank as a whole; S3.31 Determine the equation structure: Summarize all rate equations and establish a total rate equation system containing multiple components; S3.32 Simultaneous Equations: The rate equation and mass transfer equation are integrated into a system of simultaneous equations using software. The system of equations is solved using finite element analysis or matrix calculation tools. At the same time, the coupling effect of mass transfer and reaction in the spatiotemporal distribution is described. The system of equations is solved using finite element analysis or matrix calculation tools. S3.33 Set initial conditions: Input the initial concentration value obtained from the experiment and set the initial ozone concentration; S3.41 Input experimental parameters: Input the kinetic constants, mass transfer coefficients, and reaction rate constants obtained in the experiment into the model one by one; S3.42 Set boundary conditions: Input boundary conditions in the pool model; S3.43 Set the time step and simulation range: Based on the experimental reaction time range, set the simulation time step and total simulation time; S3.4 Parameter Input: By importing the kinetic constants, mass transfer coefficients, initial concentrations, and setting boundary conditions, time steps, and total simulation time obtained from experiments, a parameter library that truly reflects the actual working conditions is constructed. S3.41 Input experimental parameters: Input the kinetic constants, mass transfer coefficients, and reaction rate constants obtained in the experiment into the model one by one; S3.42 Set boundary conditions: By inputting ozone gas concentration and influent parameters consistent with the experimental conditions into the pool model, the model can truly reflect the actual operation process and provide necessary initial and boundary data for subsequent solutions; S3.43 Setting the time step and simulation range: By selecting an appropriate time step and total simulation time, the dynamic changes in the reaction process can be accurately captured; S3.5 Solving the Mechanism Model: By selecting an appropriate solver, combining the mass transfer and reaction equations, running the simulation, and verifying the consistency between the model results and experimental data; S3.51 Choosing a solution method: By selecting an appropriate time-dependent or steady-state solver, the mass transfer and reaction kinetic equations are combined to construct a complete coupled model; S3.52 Simulation: Numerical integration calculations are performed by inputting parameters, initial conditions, and boundary conditions to observe the dynamic trends of the concentration of different components over time. S3.53 Check the model results: Verify whether the model output meets expectations by comparing the simulation results with the experimental data; S3.6 Result Analysis and Correction: By comparing the simulation results with the experimental data, the model parameters are adjusted to improve the simulation accuracy, and finally a reliable mechanism model is generated; S3.61 Results Output and Visualization: Outputs pollutant concentration change curves, degradation rate charts, and visualizes the distribution of ozone in the pool; S3.62 Correcting Model Parameters: When there is a deviation between the model results and experimental data, adjust the kinetic constants, mass transfer parameters or initial conditions, and rerun the model; S3.63 Generate the final mechanism model: Based on the corrected parameters, save the final model.

5. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 1, characterized in that, S4. CAD drawing modeling and fluid dynamics CFD 3D modeling includes the following sub-steps: S4.1 Three-dimensional geometric model construction: Based on the CAD drawing of the pre-ozone contact tank, a complete three-dimensional geometric model and computational domain are established in COMSOL to accurately reflect the actual structure and key areas; S4.11 Data Collection: Obtain CAD drawings of the pre-ozone contact tank and physicochemical parameters of the wastewater; S4.12 Software Tool Selection: Select suitable modeling and fluid dynamics simulation software based on project requirements; S4.2 Physical field selection: Based on the Reynolds number Re being greater than the critical value, the Standard k-ε turbulence model is selected to simulate jet diffusion, rotating flow, and separated flow in the flow field; S4.21 Establishing a 3D geometric model: Based on CAD drawings, construct a 3D model in modeling software that contains only the key pool structure; S4.22 Define the calculation domain: Define the calculation domain as the internal area of ​​the front ozone contact tank; S4.3 Boundary Condition Setting: Set specific fluid boundary conditions for each key region of the front ozone contact tank in the numerical simulation, and define the convergence criteria; S4.31 Pool Material Settings: Define the pool and set its physical properties; S4.32 Liquid and Gas Parameter Settings: Set key physical parameters for wastewater and air-ozone mixtures respectively; S4.4 Pre-ozone contact tank flow reaction simulation: A numerical model based on the two-film theory and first-order reaction kinetics is constructed to evaluate and optimize the wastewater treatment effect by simulating the mass transfer, decay and oxidation reaction process of ozone and organic matter in wastewater in the gas-liquid two phases. S4.41 Selecting a turbulence model: Determine the flow regime based on the fluid Reynolds number and use the standard k-ε turbulence model to simulate complex turbulence phenomena; S4.42 Define boundary conditions: Set various boundary conditions for the model, including inlet, free surface, no-slip wall and outlet; S4.5 Gas-Liquid Two-Phase Flow Model Selection and Parameter Settings: By selecting a multiphase flow model, reasonably configuring the drag and lift models, and customizing the ozone mass transfer and decay functions; S4.51 Selecting the gas-liquid model: By adopting a multiphase flow model and setting the mass transfer parameters of ozone, the mass exchange between bubbles and liquid in the reactor and the transfer process of ozone from the gas phase to the liquid phase are accurately simulated. S4.52 Force Setting: By defining gas-liquid interaction forces, including drag force, virtual mass force, lift force and surface tension, the interaction forces between bubbles and liquids can be accurately simulated, thereby simulating bubble motion trajectory, size change and mass transfer process; S4.53 Mass transfer and decay model: By employing the two-film theory and first-order reaction kinetics, the mass transfer process and decay reaction of ozone in water are accurately simulated; S4.6 Mesh Generation and Refinement: By meshing the 3D geometric model and refining the mesh in key areas, a balance between computational accuracy and computational cost in the numerical simulation is ensured. S4.61 Mesh Generation: The computational domain is meshed, and the mesh is refined in key areas; S4.62 Solver Selection: Select the static segregated solver, set weak constraints, and input initial values; S4.63 Convergence Check: By monitoring the stability of variable residuals, flow rate, and mass, determine whether the numerical simulation has converged and obtained accurate results; S4.7 Solution and Flow Optimization: Solve the turbulence equations using a static separation solver, and optimize the structural dimensions or flow velocity based on the flow distribution analysis; S4.71 Simulation Result Analysis: By extracting the flow pattern distribution, ozone diffusion path, and concentration distribution, we checked for dead zones, short-circuiting phenomena, and areas of uneven flow velocity. S4.72 Geometric Adjustment: Solve dead zone or short-circuit phenomena by optimizing the system geometry; S4.73 Optimize gas-liquid mixing: Optimize the mass transfer efficiency at the gas-liquid interface by adjusting the inlet flow rate, bubble size, and ozone component ratio; S4.8 Subsequent Reaction Kinetics Optimization: After optimization through flow simulation, the reaction kinetic parameters are further optimized according to the process flow of the upstream ozone contact tank to ensure that the system meets the expected effluent water quality standards. S4.81 Optimization based on reaction kinetics: By combining the simulation results of mass transfer and decay processes, the ozone dosage and bubble residence time are adjusted to optimize the reactor operating parameters; S4.82 Generate design parameters: Determine the tank structure dimensions, equipment installation depth, inlet and outlet water flow velocities, and operating conditions; S4.83 Generate Model Report: Summarizes the output 3D model, mesh generation scheme, flow distribution map, and key parameters to generate a model report.

6. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 1, characterized in that, S5. Coupling of the mechanistic model and the fluid dynamics model includes the following sub-steps: S5.1 Dilute Substance Transport Module Setup: Set up dilute substance transport modules for each component in the CFD model, define the transport properties of each component, and set the reaction domain condition interface. S5.11 Determine the content of the mechanism model: collect reaction kinetic data and confirm the degradation pathway of pollutants; S5.12 Prepare the fluid dynamics (CFD) model: When the CFD model is completed, accurately calculate the flow distribution, gas-liquid mixing and mass transfer in the pool to ensure that the mesh is suitable for the reaction calculation; S5.2 Coupled Reaction Rates: The mechanism model is coupled in CFD by defining and incorporating the reaction rate equations of each component into the local variables of the CFD model, and incorporating the reaction rate as a source term into the CFD solver. S5.21 Defines mass transfer properties: By setting the liquid flow velocity field and direction in the CFD model, the convective transfer, molecular diffusion and turbulent diffusion behavior of pollutants in the liquid are defined, and the turbulent mixing coefficient is coupled through the k-ε turbulence model to further couple the convection term, diffusion term and turbulent mixing term to simulate the pollutant transport process; S5.22 Initialize substance concentration: Set the initial concentration value of pollutant components based on experimental or monitoring data; S5.3 Forming a complete process model: By combining hydraulic flow calculations and chemical reaction mechanisms, the reaction zone is defined, reaction kinetic equations are imported, and reaction rate parameters are set to establish a comprehensive pre-ozone contact tank simulation model; S5.31 Define the reaction zone: Determine the extent of the liquid phase region within the tank and set the reaction zone, then define the reaction zone in the CFD model; S5.32 Importing Reaction Kinetic Equations: Define reaction kinetic equations for COD, ammonia nitrogen, and petroleum components based on the reaction mechanism of pollutants, and input the reaction rate constants and related parameters; S5.33 Set reaction rate parameters: Input the oxidation rate constant of ozone for COD and ammonia nitrogen based on experimental data, and set the mass transfer coefficient according to the gas-liquid mass transfer characteristics of ozone, and adjust other mass transfer coefficients to adapt to the mass transfer behavior of different pollutants. S5.4 Coupled Mass Transfer and Reaction Process: By applying the two-film theory to define the mass transfer rate equation, setting various mass transfer conditions, and coupling the mass transfer process with the reaction rate equation, the mass conservation equation is finally solved in the CFD model. S5.41 Apply the two-film theory to define the mass transfer rate equation: determine the values ​​of the overall mass transfer coefficient K and the bubble specific surface area a, apply the mass transfer rate equation as the boundary condition to the CFD model, and calculate and update the ozone concentration in the gas and liquid phases in real time. The mass transfer rate equation is defined using the two-film theory as follows: J=K·a·(C gas -C liquid ) Where K represents the overall mass transfer coefficient; a represents the specific surface area of ​​the bubble; C gas and C liquid This indicates the ozone concentration in the gas and liquid phases. S5.42 Set mass transfer conditions: Determine bubble size, gas flow rate and liquid phase turbulent mixing intensity, and set each mass transfer condition in the CFD model. At the same time, couple the mass transfer rate equation with the reaction rate equation for calculation. S5.43 Coupled Mass Transport and Reaction: By constructing a mass conservation equation in a CFD model, the diffusion term, turbulent mixing term and reaction rate are integrated, the diffusion coefficient and turbulent mixing coefficient are adjusted, and the results are compared with experimental data; S5.5 Solution Model: The coupled mass transfer and reaction processes are solved using numerical methods, and the changes in pollutant concentration and flow pattern distribution are dynamically calculated. S5.51 Setting up the Coupled Solver: Select the multiphysics coupled solver and set the calculation accuracy; S5.52 Initialization Calculation: Input all boundary conditions, then define the convergence criteria; S5.53 Simulation: Start the solver to perform calculations, monitor the convergence of the physical field, adjust the time step or refine the mesh to improve accuracy, and analyze the changes in flow field and pollutant concentration through post-processing results. At the same time, simulate the flow distribution and pollutant reaction process, and dynamically calculate the changes in pollutant concentration. S5.6 Results Analysis and Optimization: Visual analysis is performed on the output coupled simulation results to analyze the simulation results, pollutant degradation pathways and reaction efficiency, identify problem areas, and then adjust the pool structure and operating conditions based on the results, and optimize through multiple iterations; S5.61 Result Analysis and Optimization: The simulation results are exported through the post-processing module, a visualization image is generated, and the pollutant degradation rate and ozone utilization rate are calculated, thereby obtaining data on pollutant concentration distribution, ozone diffusion path and reaction efficiency, while extracting relevant key parameters. S5.62 Analyze pollutant degradation pathways: By dividing regions, analyzing degradation rate data, and comparing the degradation of each pollutant in different regions, identify problem areas with low degradation rates, and assess flow regime and mass transfer characteristics; S5.63 Optimized Design and Operating Parameters: Based on simulation results and degradation pathway analysis, the tank structure and operating conditions were adjusted and iteratively optimized multiple times.

7. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 1, characterized in that, S6. Steady-state simulation, sensitivity analysis, and parameter correction, including the following sub-steps: S6.1 Steady-state simulation: By setting input conditions and running the steady-state solver, continuous steady-state simulation is performed to simulate the steady state of the system under continuous operation, determine whether the concentration field and flow field are stable and compare them with actual detection data, and evaluate pollutant conversion and ozone utilization efficiency. S6.11 Determine steady-state simulation conditions: Set the influent pollutant concentration, ozone dosage, and gas-liquid mixing parameters; select the reaction mechanism model and the CFD coupling model; and check the rationality of all initial and boundary conditions. S6.12 Simulation Run: Start the steady-state solver, monitor the convergence of key variables, observe and record the changing trends of the concentrations of each component in the system by running the steady-state solver, adjust the time step or initial conditions until the system parameters no longer change significantly, and confirm whether the model has reached steady state. S6.13 Output steady-state results: Export steady-state calculation results through post-processing tools, extract key data of the system under steady state and visualize the data, then compare the extracted data with the design standards to conduct preliminary analysis and evaluation of the reactor performance; S6.14 Steady-state result verification: Collect actual operating data and compare it with simulation results, analyze the source of error, adjust model parameters or initial conditions, and reset and calculate if necessary until the model results match the actual data; S6.2 Sensitivity Analysis: For changes in influent conditions or reaction kinetic parameters, each key parameter is adjusted step by step, the sensitivity coefficient is calculated, the sensitivity diagram is plotted, the impact of different parameters on the model output is evaluated, the key parameters with the greatest impact on the model output are identified, and optimization design and correction are carried out accordingly. S6.21 Select Analysis Parameters: List all parameters that affect the model output, filter and identify the key parameters that have a significant impact on the model's operation; S6.22 Stepwise parameter adjustment: By adjusting each key parameter in stages, observe and record the impact of each key parameter on the model output data before and after adjustment, analyze the impact of each parameter on pollutant removal rate and ozone depletion, and identify sensitive areas of system response; S6.23 Calculate the sensitivity coefficient: Quantify the sensitivity of the model output to parameter changes through normalized index, and calculate the sensitivity coefficient of the model output for each parameter; The specific formula for the sensitivity coefficient is as follows: Where S represents the sensitivity coefficient; ΔR represents the output change; ΔP represents the parameter change; P represents the parameter value; and R represents the output value. S6.24 Drawing Sensitivity Plots: Using data analysis tools to draw visualizations to help identify the key parameters that have the greatest impact on model output; S6.25 Determine sensitive parameters: Based on the sensitivity analysis results, identify the key parameters that have the greatest impact on the model output, i.e., the sensitive parameters; S6.3 Parameter Correction: Based on actual operating data, fine-tune the key parameters, including kinetic and mass transfer parameters, until the model simulation results match the actual situation. S6.31 Determine the parameters to be corrected: Prioritize correcting sensitive parameters that have a significant impact on the model output, and correct deviation parameters under actual operating conditions; S6.32 Parameter Adjustment Method: Fine-tune the dynamic parameters and mass transfer parameters in the model using actual operating data to make the simulation results gradually approach the actual operating data; S6.33 Re-simulation and verification: By adjusting the parameters and rerunning the model, compare the steady-state results with the actual data, analyze the deviations and continue to adjust until the simulation results are consistent with the expected requirements; S6.4 Model Stability Verification: Through multi-condition testing and convergence analysis, the model is ensured to have good adaptability, prediction accuracy, and stable convergence under different conditions. S6.41 Multi-condition simulation: Simulation is performed by setting multiple different operating conditions, comparing the simulation results with actual data under each condition, and analyzing the accuracy and stability of the model under different conditions; S6.42 Convergence Analysis: Set convergence criteria, monitor residual changes and trends during the iteration process, and adjust solver parameters as necessary to ensure that the model converges stably to a stable solution within a reasonable time and computational step size.

8. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 1, characterized in that, S7. The model undergoes self-learning and self-calibration, and outputs the optimal ozone control, including the following sub-steps: S7.1RBF Neural Network Adaptive Self-Learning: An adaptive self-learning module is constructed using an RBF neural network to randomly transform the collected influent parameters and iteratively calculate them repeatedly, recording and saving the optimal ozone dosage or optimal operating parameters. S7.11 Data Preparation: Collect actual running data, clean and remove outlier data, define model input and output parameters, organize historical running data to establish the mapping relationship between input and output, and further construct training and validation datasets; S7.12 Self-learning framework establishment: Construct RBF neural network, define the parameters of input layer and output layer, use training dataset to train model, optimize weights and activation function, and optimize optimal ozone dosage and removal efficiency through nonlinear mapping; S7.13 Initialize weights and parameters: Randomly initialize the weights, center and width parameters of the RBF neural network, set the learning rate and objective function, and use the backpropagation algorithm to optimize the network parameters to minimize the error and gradually approach the optimal solution; S7.2 Self-correction: Randomly transform sensitive parameters within a given range, continuously iterate and calculate, and replace old values ​​with better-performing parameters to optimize and self-correct the parameter settings within the model; S7.21 Input data perturbation: Randomly perturb the influent parameters to simulate fluctuations in the influent parameters and generate multiple sample points. Then run the model and record the output results. S7.22 Multiple Iteration Optimization: Perform multiple iterations of calculation and adjust model parameters for each set of perturbation data to find the optimal ozone dosage; S7.23 Optimal value storage: The calculated optimal ozone dosage and corresponding influent conditions are organized and stored in the database, and an index and retrieval mechanism are established so that the stored optimal solution can be called first under similar influent conditions; S7.3 Self-calibration process: By selecting sensitive parameters and subjecting them to random perturbations, the optimal parameter combination is repeatedly sought to optimize the model's prediction accuracy and performance; S7.31 Sensitive Parameter Selection: Based on the sensitivity analysis results, select the key parameters that have the greatest impact on the model output; S7.32 Randomly Varying Parameters: Determine the variation range of each sensitive parameter, use a random number generator to perturb the sensitive parameters, generate multiple perturbed parameter values, and synthesize multiple parameter combinations; S7.33 Finding the optimal value: Run the model for each set of parameters, calculate and record the pollutant removal efficiency, compare the results of all combinations, and select the best-performing parameter combination as the basis for the next parameter update. S7.34 Parameter Update: Updates the optimal parameter values ​​to the model, replacing the original parameter settings; S7.4 Model Validation and Adjustment: By validating the accuracy and adaptability of the model's predictions, errors are identified and adjusted. S7.41 Model Validation: Compare the model's predictions with the actual data to verify the model's accuracy and adaptability; S7.5 Output Optimal Ozone Control: By adjusting the structure and optimization algorithm of the RBF neural network, the deviation between the model output and the actual data is reduced; S7.51 Optimal Value Output: Based on the self-learning and self-correcting model, calculate and output the optimal ozone dosage and corresponding operation strategy under the current influent conditions; S7.52 Control System Feedback: The optimal ozone dosage output by the model is sent to the controller, and the ozone dosage is adjusted according to the actual operating effect; S7.53 Dynamic Optimization: By monitoring influent parameters in real time and calling the model for calculation, the ozone dosage is dynamically adjusted; S7.6 Result Storage and Application: Stores the optimal value and related data for each run; S7.61 Result Storage: By storing the optimal value and related data from each run into the database, a complete historical dataset is formed; S7.62 Empirical Rule Update: Updates existing empirical rules based on model output and runtime data.

9. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 2, characterized in that, S1.21 COD component analysis includes the following sub-steps: S1.211 Total COD determination: The total COD concentration is determined using the potassium dichromate method or spectrophotometry to provide overall data on organic load; S1.212 Separation of particulate and dissolved COD: Dissolved and particulate organic matter in the sample are separated using a 0.45μm filter membrane, and their COD is measured separately to distinguish components with different biodegradability. S1.213 Differentiation between biodegradable and non-biodegradable COD: By comparing the COD values ​​of samples that have not been treated with aerobic microbial degradation, the proportion of easily degradable and difficult-to-degrade organic matter in wastewater is quantitatively analyzed. S1.214 Organic matter type analysis: The main organic matter in the wastewater is separated, qualitatively and quantitatively analyzed by gas chromatography-mass spectrometry to determine the type and concentration of each component.

10. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 2, characterized in that, S1.22: Nitrogen component analysis, including the following sub-steps: S1.221 Total Nitrogen Determination: The total nitrogen content in wastewater is determined by using a total nitrogen analyzer or ultraviolet spectrophotometry to assess the overall level of nitrogen pollution and its potential impact on eutrophication of water bodies. S1.222 Ammonia nitrogen analysis: The concentration of ammonia nitrogen in wastewater was determined by Nessler's reagent colorimetric method or ion-selective electrode method; S1.223 Nitrate and Nitrite Nitrogen Analysis: Quantitative determination of nitrate nitrogen and nitrite nitrogen in wastewater by ion chromatography or spectrophotometry; S1.224 Nitrogen Component Distribution: By calculating the proportion of each nitrogen form in the total nitrogen and drawing a nitrogen distribution map, the source, transformation and removal difficulty of different nitrogen forms in wastewater are clarified.

11. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 2, characterized in that, S1.23 Phosphorus component analysis includes the following sub-steps: S1.231 Determination of total phosphorus (TP): The total phosphorus content is determined using the molybdenum blue method or a total phosphorus analyzer; S1.232 Orthophosphate Analysis: Orthophosphate concentration was determined by ammonium molybdate method or ion chromatography; S1.233 Separation of particulate and dissolved phosphorus: The sample was filtered using a 0.45 μm filter membrane, and the dissolved phosphorus in the filtrate and the particulate phosphorus in the filter residue were measured. S1.234 Organic phosphorus analysis: Organic phosphorus is converted into inorganic phosphorus by acid hydrolysis or heating digestion, and then the concentration is determined.

12. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 2, characterized in that, S1.24 Petroleum component analysis includes the following sub-steps: S1.241 Determination of total petroleum substances: The total concentration of petroleum substances in wastewater shall be determined by infrared photometry or ultraviolet spectrophotometry; S1.242 Component Separation and Qualitative Analysis: Petroleum-based substances are enriched by liquid-liquid extraction and their specific components are analyzed by gas chromatography or GC-MS. S1.243 Assessment of ozone demand from petroleum products: Calculate the ozone demand from petroleum products based on their chemical structure and content.

13. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 3, characterized in that, The S2.31 experiment consists of the following sub-steps: S2.311 Initial conditions setting: Adjust the water volume, inlet flow rate, and ozone gas flow rate in the experimental setup; S2.312 Reaction Start-up: Introduce the prepared simulated wastewater into the small ozone contact tank, start the circulation system, and then add ozone gas. Gradually increase the dosage according to the concentration and time designed in the experiment, and continuously record the operating parameters. S2.313 Time-segmented sampling: Sampling is performed in the contact pool at predetermined time intervals, and changes in temperature, pH, and ozone concentration are recorded.

14. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 3, characterized in that, S2.32 Detailed component reaction analysis includes the following sub-steps: S2.321 Pollutant Concentration Change Measurement: By sampling at regular intervals and using online and laboratory analysis methods, the particulate and dissolved COD in wastewater, as well as the concentration and chemical structure changes of various types of nitrogen, phosphorus, and petroleum substances before and after degradation, were detected. S2.322 Chemical Structure Change Analysis: GC-MS was used to detect structural changes in petroleum compounds before and after degradation, and to identify intermediate products such as lower alkanes or small molecule aromatic compounds. S2.323 Identify key influencing factors: Analyze the effects of ozone concentration, gas-liquid mixing effect, temperature, and pH on the degradation efficiency of each component, and then determine the optimal operating conditions for the reaction.

15. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 3, characterized in that, S2.33 Dynamic parameter extraction includes the following sub-steps: S2.331 Kinetic Equation Fitting: The kinetic models of each component are fitted using experimental data, and key parameters are extracted to quantitatively describe the reaction process of each pollutant component. The first-order reaction kinetics are as follows: in, (C) [C] indicates the concentration of a component, such as resin monomer or reactant, and k is the reaction rate constant; the negative sign "-" indicates that the concentration of reactant decreases over time. S2.332 Mass Transfer Parameter Calculation: Based on the two-film theory, the gas-liquid mass transfer rate constant and bubble specific surface area of ​​ozone between the gas and liquid phases are calculated to analyze the gas-liquid mass transfer limiting conditions. Calculate the ozone mass transfer rate constant K. L And the specific surface area of ​​the bubbles α: J=K L α·([O3] gas -[O3] liquid ) Where J is the mass transfer rate flux of ozone (O3); K L It is the mass transfer coefficient of the liquid phase, [O3] gas and [[O3]] liquid These represent the concentrations of ozone in the gas and liquid phases, respectively. S2.333 Data Validation and Adjustment: Compare and correct the fitted kinetic equations and parameters with the experimental data to ensure that the model accurately reflects the actual reaction process. If necessary, readjust the model parameters.

16. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 3, characterized in that, S2.34 Experimental data analysis and optimization includes the following sub-steps: S2.341 Degradation Efficiency Analysis: The degradation efficiency of each pollutant at different time points was calculated. The degradation efficiency formula is as follows: Where Co represents the initial concentration of pollutants at the start of treatment, i.e., the initial concentration; Ct represents the concentration of pollutants at any time during the treatment process; by calculating the reduction in pollutant concentration (Co-Ct) and then comparing it with the initial concentration Co, the degradation efficiency is obtained and expressed as a percentage. S2.342 Ozone Utilization Efficiency Analysis: Calculate the effect of unit ozone dosage on pollutant removal and evaluate ozone utilization rate; S2.343 Degradation Mechanism Summary: By integrating the concentrations of various pollutants and the chemical structural changes of intermediate products, the degradation pathways of different components from partial oxidation to final mineralization are summarized. S2.344 Optimize experimental conditions: Based on data feedback, adjust key parameters and conduct repeated experiments to find the optimal reaction conditions.

17. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 4, characterized in that, S3.21 Classify and write rate equations, including COD degradation, nitrogen component reaction, phosphorus component change and petroleum component oxidation, specifically the following sub-steps: S3.211 COD Degradation: Based on the first-order reaction kinetics of ozone oxidation, the COD degradation rate equation is established: Where [C] represents COD concentration; k COD [O3] represents the reaction rate constant between COD and ozone; [O3] represents the ozone concentration. It is a dynamic quantity, representing the rate of change of C (COD concentration) per unit time; S3.212 Reaction of nitrogen components: The rate equation describing the reaction of ammonia nitrogen with ozone is used to calculate the oxidative degradation process of ammonia nitrogen; First, the ammonia nitrogen oxidation equation: in, is the rate constant for the reaction of ammonia nitrogen with ozone; Secondly, the dynamic change equations for nitrate and nitrite nitrogen: During the reaction, ammonia nitrogen is oxidized to nitrite. Further oxidation to nitrate The concentrations of nitrite and nitrate will exhibit dynamic changes over time, initially increasing and then decreasing, or continuing to increase. First, the formation and consumption of nitrite are as follows: in, Describe the rate of the reaction from ammonia nitrogen to nitrite; the first positive term. The term represents the rate of nitrite formation—that is, the process of ammonia nitrogen oxidation to nitrite; the second negative term. This indicates the rate at which nitrite is further oxidized to nitrate by ozone; Next, nitrate is formed as follows: in, Describe the rate of the reaction from nitrite to nitrate; there is only one positive term, representing the rate at which the nitrate concentration increases with time; under the following assumptions, It is no longer further oxidized or consumed; S3.213 Changes in phosphorus composition: Considering the direct reaction between phosphate and ozone, the rate expression for the reaction equation between phosphate and ozone is: in, This is the rate constant for the reaction of phosphate with ozone; the negative sign "-" indicates that the phosphate is consumed during the oxidation process. S3.214 Oxidation of petroleum components: Describes a kinetic model of the reaction between petroleum pollutants and ozone. This model is based on ozone oxidation, and the reaction rate is proportional to the product of the concentration of petroleum pollutants and the concentration of ozone. The decomposition rate equations for alkanes, cycloalkanes, and aromatic hydrocarbons are established as follows: in, This indicates the concentration of petroleum pollutants [P]. oil The rate of change of [P] over time; oil [] represents the concentration of petroleum pollutants; [O3] represents the concentration of ozone; k R This represents the rate constant for the reaction between petroleum and ozone.

18. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 4, characterized in that, S5.32 introduces reaction kinetic equations, including the reaction kinetic equations for COD degradation, ammonia nitrogen (NH3) oxidation, and petroleum component degradation, specifically as follows: S5.321 COD Degradation Reaction Kinetic Equation: Add a first-order reaction kinetic equation to define the reaction rate between ozone and organic matter; The reaction rate equation for the COD oxidation reaction is defined as follows: R COD =k COD ·[CODE] Where, k COD Let be the oxidation rate constant of ozone for [COD]; S5.322 Ammonia nitrogen (NH3) oxidation reaction kinetic equation: A reaction kinetic equation needs to be defined, and the oxidation of ammonia nitrogen by ozone is the key reaction; The rate equation for the ammonia nitrogen oxidation reaction is: in, is the oxidation rate constant of ammonia nitrogen by ozone; S5.323 Kinetic equation for the degradation of petroleum components: Based on experimental results, a kinetic equation for the degradation is defined. Assuming a second-order reaction occurs between ozone and the pH of petroleum hydrocarbons, meaning the reaction rate is affected by the concentrations of both, the following kinetic equation, fitted to experimental data, describes the degradation process of petroleum components: Where [PH] represents the concentration of petroleum hydrocarbons or petroleum components; [O3] represents the ozone concentration; k PH It is the reaction rate constant, and its value needs to be determined through experimental data; If the ozone concentration remains relatively stable or in an excess state, the equation can be simplified to a pseudo-first-order kinetic model: Where, k obs =k PH [o3] 19. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 3, characterized in that, In the S2.22 ozone generation system configuration, the ozone addition method is either continuous addition or intermittent addition.

20. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 4, characterized in that, In the input components of model S3.1, the input variables include COD sub-components, nutrients, and petroleum pollutants; The input variables include COD sub-components, nutrients, and petroleum pollutants; The COD sub-components include organic compounds of different molecular weights or structural types; The nutrients include nitrogen and phosphorus; The petroleum pollutants mentioned include various hydrocarbons and their derivatives.

21. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 4, characterized in that, S3.12 defines the reaction type, including the main chemical reactions and physical processes in the pre-ozone contact tank, specifically: The main chemical reactions include ozone oxidation and auxiliary reactions; The ozone oxidation reaction is a direct reaction between ozone and organic matter, or indirect oxidation through the generation of hydroxyl radicals. Different organic matter may exhibit selective reaction or non-selective attack. The auxiliary reactions include the self-decomposition of ozone in water and side reactions with ions or other intermediate products; The main physical processes include mass transfer processes, mixing and diffusion, and degradation pathways; The mass transfer process is the transfer of ozone from the gas phase to the liquid phase. The mixing and diffusion mentioned above refer to the fact that uneven mixing or local concentration gradients within the reactor can also affect the reaction rate and conversion rate. The degradation pathway involves multi-stage degradation processes for different components, forming intermediate products, which are then further oxidized into harmless substances.

22. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 4, characterized in that, S3.13 Select the modeling tool, choosing any one of MATLAB, COMSOL Multiphysics, or Aspen Plus.

23. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 5, characterized in that, S4.42 defines boundary conditions, including various boundary conditions such as inlet, free surface, no-slip wall, and outlet, specifically as follows: The inlet is set with a velocity inlet, i.e., the inlet flow velocity. The flow velocity is calculated and input. The free liquid surface is set to Degassing, which simulates gas detaching from the liquid surface and escaping through the liquid surface. The non-slip wall is set as a non-slip boundary condition, friction is ignored, and the wall surface velocity is zero. The outlet is set to free flow, and free flow boundary conditions are set for the outlet.

24. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 5, characterized in that, S4.5 Gas-Liquid Two-Phase Flow Model Selection and Parameter Settings, including Symmetric drag model, Tomiyama lift model, UDF, Degassing boundary, air intake, ignoring other components in the air and the liquid phase, specifically as follows: The mechanism for selecting and setting parameters of the gas-liquid two-phase flow model includes Symmetric drag model, Tomiyama lift model, UDF, Degassing boundary, air intake, and ignoring other components in the air and the liquid phase; The Symmetric drag model describes the interaction force between the bubble and the liquid. The Tomiyama lift model represents the simulated force on a bubble; the UDF represents a custom function characterizing ozone mass transfer and decay. The Degassing boundary is used to define the free surface; The air intake is configured as a mass inlet; The other components in the air are ignored, and only two gaseous components, oxygen and ozone, are set. The liquid phase needs to include water, liquid ozone, and liquid oxygen.

25. The method for constructing a model of an ozone contact tank before advanced oxidation of petroleum wastewater according to claim 5, characterized in that, S4.52 force settings: Gas-liquid interaction forces include drag force, virtual mass force, lift force, and surface tension, specifically: The drag force refers to the drag force exerted by the liquid on the bubble as the bubble moves in the liquid; The virtual mass force refers to the additional force required by the liquid to respond to the acceleration of the bubble when the bubble accelerates or decelerates. The lift force refers to the buoyancy effect of the bubble in the liquid, i.e., the rotation and movement path of the bubble; The surface tension refers to the intermolecular forces at the gas-liquid interface.

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