A method of software simulation for optimizing wastewater treatment processes

Abstract

This invention discloses a method for software simulation and optimization of wastewater treatment processes, relating to the field of wastewater treatment technology. The invention includes the following steps: establishing a full-process digital twin model covering pretreatment, biological treatment, advanced treatment, and sludge disposal, and integrating multi-source data for initialization and parameter sensitivity analysis; using historical operating data to perform multi-dimensional calibration and comprehensive verification of the model; based on the verified model, constructing a scenario simulation system to simulate the entire process operation and establishing a response relationship database of parameter combinations, treatment effects, and overall performance; constructing a comprehensive performance evaluation system, and based on the response relationship database from step S3, using a multi-objective evolutionary algorithm for collaborative optimization to generate a Pareto optimal solution set and a corresponding adaptive operating strategy library; establishing a closed-loop mechanism of simulation prediction, optimization decision-making, on-site implementation, real-time monitoring, performance evaluation, and model feedback, and periodically updating the model and strategies through online data.

Description

Technical Field

[0001] This invention belongs to the field of wastewater treatment technology, and in particular relates to a method for software simulation and optimization of wastewater treatment processes. Background Technology

[0002] In the field of wastewater treatment, various wastewater treatment processes remain a core challenge. A key concern in the industry is how to scientifically determine the optimal operating parameters for the entire process, covering pretreatment (grinding gap, grit chamber hydraulic retention time, coagulant dosage, flotation pressure, oil separator retention time, etc.), core treatment (internal reflux ratio, external reflux ratio, dissolved oxygen concentration, sludge age, carbon source dosage, biofilm packing filler rate, MBR membrane flux, anaerobic digestion temperature, etc.), advanced treatment (filtration rate, membrane flux, transmembrane pressure, ozone / UV-H2O2 dosage, disinfection contact time, backwashing cycle, etc.), and sludge disposal (sludge thickening solids load, conditioning agent dosage, dewatering pressure, etc.), while ensuring stable effluent quality and effectively controlling operating costs. Currently, existing parameter optimization methods in the wastewater treatment field mainly have the following limitations:

[0003] 1. Trial-and-error method based on human experience: Heavily reliant on engineer experience, parameter tuning results are linked to individual engineer experience, lacking universality and scientific rigor. Lagging and blind approach: Parameter adjustments are usually made only when effluent quality fluctuates or exceeds standards, a reactive method that is slow and increases subsequent operating costs. Inability to perform multi-objective synergistic optimization: Difficult to simultaneously achieve synergistic compliance with multiple effluent indicators such as COD, ammonia nitrogen, and total nitrogen.

[0004] 2. Single-factor-based experimental optimization: It fails to consider the interactions between process parameters, neglecting the complex synergistic effects between different parameters (e.g., the combined influence of dissolved oxygen and sludge age on denitrification), which are precisely the most critical aspects of synergistic biological treatment. It is also less efficient, requiring numerous experiments, and the parameters ultimately found represent only a local optimum, not the optimal solution for the entire wastewater treatment system.

[0005] 3. Preliminary simulation based on static models (using various wastewater treatment software such as Biowin, GPS-X, GEMSS, and STOAT to build models for steady-state operation simulation): Insufficient model accuracy: Many existing studies merely simulate using the software's default parameters without rigorous model calibration and dynamic verification based on actual influent and operational data. This leads to significant discrepancies between model predictions and actual results, resulting in low reliability. Insufficient optimization depth: These studies are only used for operational simulation or simple sensitivity analysis, failing to deeply integrate simulation outputs with advanced mathematical optimization methods to accurately find the optimal solution. Summary of the Invention

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0007] This invention relates to a method for software simulation and optimization of wastewater treatment processes, comprising the following steps:

[0008] Step S1: Establish a digital twin model covering the entire process from pretreatment to biological treatment, advanced treatment, and sludge disposal, and integrate multi-source data for initialization and parameter sensitivity analysis;

[0009] Step S2: Use historical running data to perform multi-dimensional calibration and comprehensive verification of the model to ensure the accuracy of model predictions;

[0010] Step S3: Based on the validated model, construct a scenario simulation system covering different combinations of influent load, temperature and operating parameters, conduct full-process operation simulation, and establish a response relationship database of parameter combination-treatment effect-overall performance;

[0011] Step S4: Construct a comprehensive performance evaluation system covering water quality, economy, environment and stability, and based on the response relationship database in Step S3, use a multi-objective evolutionary algorithm to perform collaborative optimization and solve the problem, generating a Pareto optimal solution set and a corresponding adaptive operation strategy library.

[0012] Step S5: Establish a closed-loop operation and continuous improvement mechanism of simulation prediction, optimization decision-making, on-site implementation, real-time monitoring, performance evaluation and model feedback. The model and strategy are updated regularly through online data to achieve adaptive optimization operation of the wastewater treatment system.

[0013] Furthermore, step S1 includes the following steps:

[0014] Step S11, Establishment of the full-process model framework: Based on the actual process flow of the target wastewater treatment plant, a systematic integrated digital twin model is constructed, covering pretreatment units (such as physical pretreatment facilities such as bar screens, grit chambers, primary sedimentation tanks, and equalization tanks, as well as chemical and special pretreatment devices such as coagulation sedimentation, chemical phosphorus removal, dissolved air flotation tanks, and oil separators), biological treatment units (such as A2 / O, SBR, oxidation ditch activated sludge process, MBBR, aerated biological filter (BAF), biological contact oxidation biofilm process, UASB, IC anaerobic treatment process, and MBR membrane bioreactor), and advanced treatment units (such as UF / RO membrane separation system, denitrification deep bed filter, ozone / UV-H2O2 advanced oxidation device, quartz sand / fiber filtration facilities, and ultraviolet / chlorine / ozone disinfection system).

[0015] Step S12, Multi-source Data Integration and Model Initialization: The system collects and integrates core data from the entire process of the target wastewater treatment plant, constructing a complete model input database covering pretreatment, biological treatment, advanced treatment, and sludge disposal. Specifically, this includes: real-time water quality data (COD, BOD5, ammonia nitrogen, total nitrogen, total phosphorus, SS, oil, heavy metals) and water volume data (instantaneous flow rate, daily average flow rate, water volume fluctuation coefficient) from the influent side; water quality characteristic parameters (pH value, water temperature, alkalinity, organic matter composition); operating parameters of the pretreatment unit (grid gap size, hydraulic retention time in the grit chamber, dissolved air pressure and reflux ratio in the dissolved air flotation tank, retention time in the oil separator, type and dosage of coagulant (PAC / PAM), dosage of neutralizing agent, and pH control data); key operating data of the biological treatment unit (internal reflux ratio, external reflux ratio, dissolved oxygen concentration, sludge age, carbon source dosage and dosing point for activated sludge processes, and biofilm process). The data included: packing material filling rate and biofilm thickness monitoring data; digestion temperature, organic load, and VFA concentration in the anaerobic process; membrane flux, transmembrane pressure, and aeration intensity in the MBR; operating parameters of the advanced treatment unit (filtration rate, backwashing cycle and intensity, operating pressure and recovery rate of the membrane separation system, ozone / UV-H2O2 dosage and reaction time, disinfection contact time and reagent dosage); relevant data of the sludge treatment unit (sludge thickening solids load, conditioning reagent dosage, dewatering pressure and speed, anaerobic digester gas production rate, drying / incineration energy consumption parameters); in addition, equipment operating status data (aeration fan frequency, pump flow rate, stirring power), environmental monitoring data (temperature, humidity) and historical effluent water quality compliance data (pollutant concentration in effluent of each unit), reagent consumption ledger, sludge return and discharge records; the first simulation used a validated default parameter set as the basis to ensure the rationality of the initial model operation.

[0016] Step S13, Global Sensitivity Analysis and Parameter Identification: Using variance-based analysis or Morris screening, global sensitivity analysis is performed on the key design and operating parameters of each treatment unit in the model to identify parameters affecting the final effluent quality, overall operating cost, and greenhouse gas emission environmental indicators of the system, providing a basis for subsequent targeted calibration.

[0017] Furthermore, step S2 includes the following steps:

[0018] Step S21, Parameter System Calibration and Optimization: Based on long-term operation monitoring data of the wastewater treatment plant, use manual iteration or automatic optimization algorithms (such as genetic algorithm, particle swarm optimization algorithm) to systematically calibrate the identified high-sensitivity parameters to ensure that the model can accurately reflect the actual process performance under both steady-state and dynamic conditions.

[0019] Step S22, Multi-index Cross-validation: Using an independent running dataset that was not involved in model calibration, the predictive performance of the calibrated model is comprehensively validated from multiple dimensions, including the stability of effluent quality compliance, the accuracy of energy and material consumption, and the rationality of interactions between process units, to ensure that it has the reliability required for practical optimization applications.

[0020] Furthermore, step S3 includes the following steps:

[0021] Step S31, Multi-Scenario Full-Process Operation Simulation: Based on a digital twin model of the entire process from pretreatment to biological treatment to advanced treatment to sludge disposal (adapted to the core functions of mainstream software such as BioWin and GPS-X) verified by measured data, the system constructs a multi-dimensional scenario simulation system—covering influent load scenarios (normal water volume and quality fluctuations, high concentration of recalcitrant organic matter shocks, special influent with high salinity / high nitrogen / high oil content, and sudden increase in flow during rainstorms), temperature scenarios (normal temperature throughout the year, low temperature critical nitrification temperature, and high temperature anaerobic digestion range), and scenarios combining full-process operation parameters (pretreatment coagulant dosage, dissolved air pressure during air flotation, internal / external reflux ratio in biological treatment, and DO concentration in each functional area). The system simulates and outputs pollutant removal efficiencies (oil / SS removal rate in pretreatment, nitrogen and phosphorus removal rate in biological treatment, and recalcitrant COD removal rate in deep treatment) and energy and chemical consumption distributions (aeration energy consumption, pump energy consumption, and PAC / PAM / disinfectant consumption) for each unit of the entire process under various scenarios. It also simulates and outputs sludge production and characteristics (sludge moisture content and organic matter content). Finally, it constructs a high-precision response relationship database covering the entire process parameter combination, unit treatment effect, and overall system performance, providing data support for subsequent optimization scheme selection.

[0022] Step S32, Multi-criteria Comprehensive Performance Evaluation: Construct a comprehensive performance evaluation system covering four dimensions: water quality, economy, environment, and stability, providing an objective basis for the quantitative comparison of different operating schemes; specific indicators include:

[0023] Step S321, Water quality compliance dimensions: Concentration of key pollutants in the effluent of each unit throughout the entire process (COD, BOD5, ammonia nitrogen, total nitrogen, total phosphorus, SS, oil, heavy metals), final effluent compliance rate, and water quality exceedance risk coefficient;

[0024] Step S322, Economic Cost Dimension: Total operating cost of the entire process (electricity consumption cost, reagent consumption cost, sludge disposal cost, equipment maintenance cost), unit water volume treatment cost, carbon trading cost (related carbon emission indicators);

[0025] Step S323, Environmental Benefit Dimension: Carbon footprint intensity (CO2 equivalent emissions per unit volume of water), greenhouse gas emission reduction potential, and impact coefficient of pesticide residues on receiving water bodies;

[0026] Step S324, System stability dimension: parameter fluctuation adaptability (such as the stability of treatment effect under sudden changes in influent load), equipment fault early warning response efficiency, sludge settling performance, and membrane module fouling control effect; the evaluation process adopts the entropy weight method-TOPSIS combined objective quantitative method to rank the comprehensive performance of each operation scheme and select the globally optimal scheme that achieves stable water quality compliance, optimal operating cost, maximizes environmental benefits, and ensures reliable system operation.

[0027] Furthermore, step S4 includes the following steps:

[0028] Step S41, Quantitative Relationship Model Construction: Using response surface methodology, artificial neural networks, or machine learning tools, establish a quantitative mapping relationship model between key process operating parameters and multiple performance indicators based on simulation data;

[0029] Step S42, Multi-objective collaborative optimization solution: Use multi-objective evolutionary algorithms (such as NSGA-II, MOEA / D) to optimize the solution and obtain the Pareto optimal solution set under the constraints of multiple objectives such as effluent quality, operating cost and carbon emissions, and clarify the trade-off relationship between each objective;

[0030] Step S43, Development of Adaptive Operation Strategy Library: Based on different influent characteristics, seasonal temperature changes, and different management priorities (such as cost priority or low-carbon operation), generate corresponding optimal combination of operating parameters to form an intelligent decision-making strategy library with adaptive capabilities.

[0031] Furthermore, in step S5, a closed-loop operation mechanism is established, encompassing simulation prediction, optimized decision-making, on-site implementation, real-time monitoring, performance evaluation, and model feedback. By deploying online monitoring instruments and data acquisition systems, system operation data is acquired in real time, the model is periodically recalibrated and updated, and decision-making strategies are continuously optimized based on actual operating performance. This ensures that the wastewater treatment system can adapt to changes in external conditions in the long term and maintain a highly efficient, low-carbon, and economical operating state.

[0032] The present invention has the following beneficial effects:

[0033] 1. This invention constructs a dynamic model that accurately reflects the operational patterns of various wastewater treatment systems (including industrial wastewater and domestic sewage) using wastewater treatment process simulation software. The model has undergone rigorous calibration and verification to ensure its accuracy and reliability under different water quality characteristics. Based on this, statistical and intelligent optimization methods such as response surface methodology (RSM), orthogonal experimental design, and genetic algorithm coupled optimization are introduced to construct a comprehensive evaluation system with multiple process parameters and dual core objectives (effluent quality compliance + greenhouse gas emission reduction). This system systematically covers key parameters throughout the entire process of pretreatment, biological treatment, advanced treatment, and sludge disposal, including the dosage of coagulants (PAC / PAM), dissolved air pressure and reflux ratio in the pretreatment stage, retention time in the oil separator, and neutralization pH adjustment value; and the internal reflux... Flow ratio, external return ratio, dissolved oxygen (DO) concentration in each functional zone, sludge age (SRT), carbon source dosage and addition point, biofilm packing filling rate, MBR membrane flux and aeration intensity, anaerobic digestion temperature and organic load; filtration rate, backwashing cycle and intensity, transmembrane pressure (TMP) and recovery rate of the membrane separation system in the advanced treatment stage, ozone / UV-H2O2 dosage, disinfection contact time; sludge thickening solids load, conditioning agent dosage, dewatering pressure, and anaerobic digester stirring intensity in the sludge disposal stage, etc.

[0034] 2. By establishing a multi-objective optimization model that covers water quality compliance, operating costs, and greenhouse gas emissions, this invention can find the optimal combination of process parameters that meets all effluent standards and minimizes carbon emissions within a multi-dimensional objective space. This not only improves the stability and reliability of the effluent quality but also simultaneously enhances the environmental benefits of the treatment system.

[0035] 3. This invention replaces the extensive and time-consuming physical experiments and parameter adjustments at physical sewage treatment plants by establishing an offline digital twin virtual model of the wastewater treatment system. This method greatly shortens the parameter optimization cycle, and can complete the exploration process that would take months in real production in a few days or even hours. At the same time, it avoids the waste of reagents, excessive energy consumption and unnecessary carbon emissions caused by repeated trial and error, and achieves dual savings in time and economic costs.

[0036] 4. The accurate model constructed in this invention possesses powerful simulation and prediction capabilities, enabling it to predict the system's treatment efficiency and greenhouse gas emission dynamics under varying influent conditions or process parameters without affecting actual production. This allows operators to proactively formulate optimal operating strategies and carbon reduction plans, achieving a leap from passive response to active optimization, and providing a core decision support tool for the refined, intelligent, and low-carbon operation and management of wastewater treatment plants.

[0037] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic flowchart of a software simulation optimization method for wastewater treatment processes according to the present invention.

[0040] Figure 2 For A 2 / O process diagram;

[0041] Figure 3 This is a schematic diagram of the AO process;

[0042] Figure 4 A schematic diagram of the Bardenpho process;

[0043] Figure 5 This is a schematic diagram of the improved UCT process;

[0044] Figure 6 This is a schematic diagram of the MBR process;

[0045] Figure 7 This is a schematic diagram of the oxidation ditch process;

[0046] Figure 8 This is a schematic diagram of the SBR process;

[0047] Figure 9 This is a schematic diagram of an advanced oxidation process;

[0048] Figure 10 This is a schematic diagram of the chlorination and disinfection process;

[0049] Figure 11 A schematic diagram illustrating the construction of a GPS-X-based preprocessing process model;

[0050] Figure 12 A schematic diagram illustrating the construction of a GPS-X-based biological treatment process model;

[0051] Figure 13 A schematic diagram illustrating the construction of a GPS-X-based deep processing technology model. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figures 1-13 As shown, the present invention is a method for software simulation and optimization of wastewater treatment processes, comprising the following steps:

[0054] Step S1: Establish a digital twin model covering the entire process from pretreatment to biological treatment, advanced treatment, and sludge disposal, and integrate multi-source data for initialization and parameter sensitivity analysis;

[0055] Step S2: Use historical running data to perform multi-dimensional calibration and comprehensive verification of the model to ensure the accuracy of model predictions;

[0056] Step S3: Based on the validated model, construct a scenario simulation system covering different combinations of influent load, temperature and operating parameters, conduct full-process operation simulation, and establish a response relationship database of parameter combination-treatment effect-overall performance;

[0057] Step S4: Construct a comprehensive performance evaluation system covering water quality, economy, environment and stability, and based on the response relationship database in Step S3, use a multi-objective evolutionary algorithm to perform collaborative optimization and solve the problem, generating a Pareto optimal solution set and a corresponding adaptive operation strategy library.

[0058] Step S5: Establish a closed-loop operation and continuous improvement mechanism of simulation prediction, optimization decision-making, on-site implementation, real-time monitoring, performance evaluation and model feedback. The model and strategy are updated regularly through online data to achieve adaptive optimization operation of the wastewater treatment system.

[0059] Furthermore, step S1 includes the following steps:

[0060] Step S11, Establishment of the full-process model framework: Based on the actual process flow of the target wastewater treatment plant, a systematic integrated digital twin model is constructed, covering pretreatment units (such as physical pretreatment facilities such as bar screens, grit chambers, primary sedimentation tanks, and equalization tanks, as well as chemical and special pretreatment devices such as coagulation sedimentation, chemical phosphorus removal, dissolved air flotation tanks, and oil separators), biological treatment units (such as A2 / O, SBR, oxidation ditch activated sludge process, MBBR, aerated biological filter (BAF), biological contact oxidation biofilm process, UASB, IC anaerobic treatment process, and MBR membrane bioreactor), and advanced treatment units (such as UF / RO membrane separation system, denitrification deep bed filter, ozone / UV-H2O2 advanced oxidation device, quartz sand / fiber filtration facilities, and ultraviolet / chlorine / ozone disinfection system).

[0061] Step S12, Multi-source Data Integration and Model Initialization: The system collects and integrates core data from the entire process of the target wastewater treatment plant, constructing a complete model input database covering pretreatment, biological treatment, advanced treatment, and sludge disposal. Specifically, this includes: real-time water quality data (COD, BOD5, ammonia nitrogen, total nitrogen, total phosphorus, SS, oil, heavy metals) and water volume data (instantaneous flow rate, daily average flow rate, water volume fluctuation coefficient) from the influent side; water quality characteristic parameters (pH value, water temperature, alkalinity, organic matter composition); operating parameters of the pretreatment unit (grid gap size, hydraulic retention time in the grit chamber, dissolved air pressure and reflux ratio in the dissolved air flotation tank, retention time in the oil separator, type and dosage of coagulant (PAC / PAM), dosage of neutralizing agent, and pH control data); key operating data of the biological treatment unit (internal reflux ratio, external reflux ratio, dissolved oxygen concentration, sludge age, carbon source dosage and dosing point for activated sludge processes, and biofilm process). The data included: packing material filling rate and biofilm thickness monitoring data; digestion temperature, organic load, and VFA concentration in the anaerobic process; membrane flux, transmembrane pressure, and aeration intensity in the MBR; operating parameters of the advanced treatment unit (filtration rate, backwashing cycle and intensity, operating pressure and recovery rate of the membrane separation system, ozone / UV-H2O2 dosage and reaction time, disinfection contact time and reagent dosage); relevant data of the sludge treatment unit (sludge thickening solids load, conditioning reagent dosage, dewatering pressure and speed, anaerobic digester gas production rate, drying / incineration energy consumption parameters); in addition, equipment operating status data (aeration fan frequency, pump flow rate, stirring power), environmental monitoring data (temperature, humidity) and historical effluent water quality compliance data (pollutant concentration in effluent of each unit), reagent consumption ledger, sludge return and discharge records; the first simulation used a validated default parameter set as the basis to ensure the rationality of the initial model operation.

[0062] Step S13, Global Sensitivity Analysis and Parameter Identification: Using variance-based analysis or Morris screening, global sensitivity analysis is performed on the key design and operating parameters of each treatment unit in the model to identify parameters affecting the final effluent quality, overall operating cost, and greenhouse gas emission environmental indicators of the system, providing a basis for subsequent targeted calibration.

[0063] Furthermore, step S2 includes the following steps:

[0064] Step S21, Parameter System Calibration and Optimization: Based on long-term operation monitoring data of the wastewater treatment plant, use manual iteration or automatic optimization algorithms (such as genetic algorithm, particle swarm optimization algorithm) to systematically calibrate the identified high-sensitivity parameters to ensure that the model can accurately reflect the actual process performance under both steady-state and dynamic conditions.

[0065] Step S22, Multi-index Cross-validation: Using an independent running dataset that was not involved in model calibration, the predictive performance of the calibrated model is comprehensively validated from multiple dimensions, including the stability of effluent quality compliance, the accuracy of energy and material consumption, and the rationality of interactions between process units, to ensure that it has the reliability required for practical optimization applications.

[0066] Furthermore, step S3 includes the following steps:

[0067] Step S31, Multi-Scenario Full-Process Operation Simulation: Based on a digital twin model of the entire process from pretreatment to biological treatment to advanced treatment to sludge disposal (adapted to the core functions of mainstream software such as BioWin and GPS-X) verified by measured data, the system constructs a multi-dimensional scenario simulation system—covering influent load scenarios (normal water volume and quality fluctuations, high concentration of recalcitrant organic matter shocks, special influent with high salinity / high nitrogen / high oil content, and sudden increase in flow during rainstorms), temperature scenarios (normal temperature throughout the year, low temperature critical nitrification temperature, and high temperature anaerobic digestion range), and scenarios combining full-process operation parameters (pretreatment coagulant dosage, dissolved air pressure during air flotation, internal / external reflux ratio in biological treatment, and DO concentration in each functional area). The system simulates and outputs pollutant removal efficiencies (oil / SS removal rate in pretreatment, nitrogen and phosphorus removal rate in biological treatment, and recalcitrant COD removal rate in deep treatment) and energy and chemical consumption distributions (aeration energy consumption, pump energy consumption, and PAC / PAM / disinfectant consumption) for each unit of the entire process under various scenarios. It also simulates and outputs sludge production and characteristics (sludge moisture content and organic matter content). Finally, it constructs a high-precision response relationship database covering the entire process parameter combination, unit treatment effect, and overall system performance, providing data support for subsequent optimization scheme selection.

[0068] Step S32, Multi-criteria Comprehensive Performance Evaluation: Construct a comprehensive performance evaluation system covering four dimensions: water quality, economy, environment, and stability, providing an objective basis for the quantitative comparison of different operating schemes; specific indicators include:

[0069] Step S321, Water quality compliance dimensions: Concentration of key pollutants in the effluent of each unit throughout the entire process (COD, BOD5, ammonia nitrogen, total nitrogen, total phosphorus, SS, oil, heavy metals), final effluent compliance rate, and water quality exceedance risk coefficient;

[0070] Step S322, Economic Cost Dimension: Total operating cost of the entire process (electricity consumption cost, reagent consumption cost, sludge disposal cost, equipment maintenance cost), unit water volume treatment cost, carbon trading cost (related carbon emission indicators);

[0071] Step S323, Environmental Benefit Dimension: Carbon footprint intensity (CO2 equivalent emissions per unit volume of water), greenhouse gas emission reduction potential, and impact coefficient of pesticide residues on receiving water bodies;

[0072] Step S324, System stability dimension: parameter fluctuation adaptability (such as the stability of treatment effect under sudden changes in influent load), equipment fault early warning response efficiency, sludge settling performance, and membrane module fouling control effect; the evaluation process adopts the entropy weight method-TOPSIS combined objective quantitative method to rank the comprehensive performance of each operation scheme and select the globally optimal scheme that achieves stable water quality compliance, optimal operating cost, maximizes environmental benefits, and ensures reliable system operation.

[0073] Furthermore, step S4 includes the following steps:

[0074] Step S41, Quantitative Relationship Model Construction: Using response surface methodology, artificial neural networks, or machine learning tools, establish a quantitative mapping relationship model between key process operating parameters and multiple performance indicators based on simulation data;

[0075] Step S42, Multi-objective collaborative optimization solution: Use multi-objective evolutionary algorithms (such as NSGA-II, MOEA / D) to optimize the solution and obtain the Pareto optimal solution set under the constraints of multiple objectives such as effluent quality, operating cost and carbon emissions, and clarify the trade-off relationship between each objective;

[0076] Step S43, Development of Adaptive Operation Strategy Library: Based on different influent characteristics, seasonal temperature changes, and different management priorities (such as cost priority or low-carbon operation), generate corresponding optimal combination of operating parameters to form an intelligent decision-making strategy library with adaptive capabilities.

[0077] Furthermore, in step S5, a closed-loop operation mechanism is established, encompassing simulation prediction, optimized decision-making, on-site implementation, real-time monitoring, performance evaluation, and model feedback. By deploying online monitoring instruments and data acquisition systems, system operation data is acquired in real time. The model is periodically recalibrated and updated, and decision-making strategies are continuously optimized based on actual operational performance. This ensures that the wastewater treatment system can adapt to changes in external conditions over the long term and maintain a highly efficient, low-carbon, and economical operating state. This invention is the first to achieve digital integrated simulation and multi-objective collaborative optimization of the entire physical-chemical-biological process of a wastewater treatment plant. It expands traditional single-dimensional water quality compliance management to the improvement of comprehensive performance across multiple dimensions, including economic, environmental, and energy aspects. It provides a complete technical solution from theoretical methods to engineering implementation for the refined, intelligent, and low-carbon operation of wastewater treatment plants, demonstrating technological innovation and broad engineering application prospects.

[0078] One specific application of this embodiment is:

[0079] Example 1: Pretreatment process (oil removal, sedimentation, filtration, etc.)

[0080] Target: A wastewater treatment plant with a treatment capacity of 320m³ 3 / h, mainly treating wastewater with high suspended solids and high oil content.

[0081] Implementation steps:

[0082] Construct a complete pretreatment model that includes a bar screen, equalization tank, oil removal tank, and dissolved air flotation unit;

[0083] The focus is on simulating the oil separation process and the efficiency of suspended solids removal;

[0084] Key parameters in the calibration model, such as the oil flotation rate and solid settling velocity, were adjusted.

[0085] Analyze the effect of temperature changes (15-35℃) on the treatment effect;

[0086] Optimize the operating parameters of the air flotation unit: dissolved air pressure, bubble size distribution, and surface load;

[0087] Simulate the optimal operating scheme under different seasonal conditions;

[0088] Establish an adaptive control strategy to automatically adjust operating parameters based on the influent water quality;

[0089] Expected implementation results:

[0090] The grease removal rate increased from 75% to over 85%;

[0091] The removal rate of suspended solids remains stable at over 90%.

[0092] Energy consumption is reduced by 20%, mainly through aeration operation;

[0093] The system's ability to withstand impact loads has been significantly enhanced.

[0094] Example 2: Optimization of multi-stage AO process for industrial park wastewater (biological treatment: A2O, AO, SBR processes, etc.);

[0095] Target: Wastewater treatment plant in an industrial park, with a treatment capacity of 320m³. 3 The system employs a two-stage AO process to treat integrated industrial wastewater. The influent COD is 2000-3000 mg / L, NH3-N is 150-300 mg / L, and TN is 170-350 mg / L.

[0096] Implementation steps:

[0097] Construct a two-level AO process accurate model in GPS-X, including:

[0098] Level 1: Anoxic tank (HRT=23.4h) + Aerobic tank (HRT=72.2h);

[0099] Level 2: Anoxic tank (HRT=12h) + Aerobic tank (HRT=6h);

[0100] DRSC pool: HRT=28.9h;

[0101] Primary sedimentation tank, intermediate sedimentation tank, and final sedimentation tank;

[0102] Key parameters for model calibration:

[0103] Heterotrophic bacteria yield coefficient YH: 0.45-0.65;

[0104] Maximum specific growth rate (μAOB) of ammonia-oxidizing bacteria: 0.35-0.55 d -1 ;

[0105] Denitrification rate correction factor ηNO3: 0.6-0.8;

[0106] Key operating parameters were determined through sensitivity analysis.

[0107] Internal reflux ratio (200%)

[0108] External reflux ratio (100%-150%);

[0109] DO in aerobic tank (2-5 mg / L);

[0110] Sludge age (10-20 days);

[0111] Establish a multi-objective optimization model, considering the following:

[0112] Effluent water quality (COD≤200mg / L, NH3-N≤1mg / L, TN≤20mg / L);

[0113] Operating costs (electricity consumption, carbon source consumption, etc.);

[0114] Multi-objective decision-making using the entropy weight method-TOPSIS combined objective weight method:

[0115] First, GPS-X simulation is used to generate operating plans under different parameter combinations;

[0116] Establish an evaluation index system: effluent COD, NH3-N, TN, TP removal rates, unit treatment energy consumption, carbon source consumption, etc.

[0117] The entropy weight method is used to objectively calculate the weights of each indicator, eliminating the influence of subjective factors.

[0118] The TOPSIS method was used to calculate the relative closeness of each scheme to the ideal solution;

[0119] Select the optimal solution based on proximity ranking;

[0120] Expected implementation results:

[0121] The TN removal rate increased from 70% to 85%, and the TN in the effluent remained stable below 20 mg / L;

[0122] The carbon source dosage was optimized and reduced by 10%, resulting in annual savings of approximately 1.2 million yuan in reagent costs.

[0123] By optimizing the aeration strategy, energy consumption is reduced by 18%, resulting in annual electricity savings of approximately 900,000 kWh.

[0124] The system's resistance to shock loads has been significantly enhanced, and the stability of the effluent water quality has been improved by 40%.

[0125] Example 3: Simulation and optimization of integrated depth processing technology based on GPS-X;

[0126] Target: Advanced treatment unit of a wastewater treatment plant in a coal chemical-chemical industrial park, with a treatment capacity of 320m³. 3 / h, adopts a combination process of "high-density sedimentation tank + mechanical filtration + ultrafiltration + reverse osmosis + electro-Fenton oxidation".

[0127] GPS-X modeling and simulation steps:

[0128] GPS-X platform model construction:

[0129] Use the Advanced Custom Modeling module of GPS-X to build an integrated process model;

[0130] High-density sedimentation tank: a sedimentation model coupled with chemical coagulation reaction kinetics is used;

[0131] Mechanical filtration: A filter bed clogging model is established based on the particulate transport equation;

[0132] Membrane treatment unit: Call the GPS-X membrane bioreactor module and configure ultrafiltration and reverse osmosis parameters;

[0133] Electro-Fenton oxidation: Establishing an electrochemical advanced oxidation kinetic model using a chemical reactor module;

[0134] GPS-X Sensitivity Analysis:

[0135] Use the SensitivityAnalysis tool built into GPS-X;

[0136] Identify key parameters affecting system performance: coagulant dosage, membrane operating pressure, current density, etc.

[0137] Determine the extent to which each parameter affects effluent COD, energy consumption, and operating costs;

[0138] GPS-X Dynamic Simulation:

[0139] Simulate the system response under different water inflow conditions;

[0140] Predicting the impact of seasonal water quality changes on treatment effectiveness;

[0141] Assess the system's ability to withstand impact loads;

[0142] Multi-objective optimization implementation:

[0143] Simulate different combinations of process parameters in GPS-X;

[0144] Export key indicators such as COD removal rate, energy consumption, and reagent consumption;

[0145] Use Design-Expert for response surface analysis;

[0146] Determining the optimal solution using the entropy weight method-TOPSIS;

[0147] The optimization results are fed back to GPS-X for verification simulation.

[0148] GPS-X model validation:

[0149] Validate the model's prediction accuracy using an independent dataset;

[0150] Compare the degree of agreement between simulated and measured values;

[0151] Adjust model parameters to improve prediction accuracy;

[0152] Expected optimization effects of the process:

[0153] The optimal surface loading of the high-density sedimentation tank was determined using GPS-X simulation: 12m³. 3 / (m 2 ·h);

[0154] Optimize ultrafiltration backwashing strategy: backwash for 2 minutes every 60 minutes;

[0155] The optimal operating pressure for reverse osmosis is determined to be 15 bar.

[0156] Optimal conditions for electric Fenton:

[0157] Projected economic benefits (based on GPS-X simulation results);

[0158] Chemical costs reduced by 32%: by optimizing coagulant dosage;

[0159] Energy consumption reduced by 26%: Optimized membrane system operating parameters;

[0160] Membrane replacement cycle extended by 40%: Improved pretreatment effect;

[0161] Annual operating cost savings;

[0162] Expected system performance improvement:

[0163] The stability of the GPS-X analog display system is improved by 55%;

[0164] The predicted COD value of the effluent was <55mg / L, and the actual measured value was <50mg / L.

[0165] The water reuse rate increased from 75% to 85%;

[0166] Concentrate discharge was reduced by 35%.

[0167] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0168] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for software simulation and optimization of wastewater treatment processes, characterized in that: Includes the following steps: Step S1: Establish a digital twin model covering the entire process from pretreatment to biological treatment, advanced treatment, and sludge disposal, and integrate multi-source data for initialization and parameter sensitivity analysis; Step S2: Use historical running data to perform multi-dimensional calibration and comprehensive verification of the model to ensure the accuracy of model predictions; Step S3: Based on the validated model, construct a scenario simulation system covering different combinations of influent load, temperature and operating parameters, conduct full-process operation simulation, and establish a response relationship database of parameter combination-treatment effect-overall performance; Step S4: Construct a comprehensive performance evaluation system covering water quality, economy, environment and stability, and based on the response relationship database in Step S3, use a multi-objective evolutionary algorithm to perform collaborative optimization and solve the problem, generating a Pareto optimal solution set and a corresponding adaptive operation strategy library. Step S5: Establish a closed-loop operation and continuous improvement mechanism of simulation prediction, optimization decision-making, on-site implementation, real-time monitoring, performance evaluation and model feedback. The model and strategy are updated regularly through online data to achieve adaptive optimization operation of the wastewater treatment system.

2. The method for software simulation and optimization of wastewater treatment process according to claim 1, characterized in that, Step S1 includes the following steps: Step S11, Establishment of the full-process model framework: Based on the actual process flow of the target wastewater treatment plant, a systematic integrated digital twin model covering the pretreatment unit, biological treatment unit, and advanced treatment unit is constructed. Step S12, Multi-source Data Integration and Model Initialization: The system collects and integrates data from the entire process of the target wastewater treatment plant to construct a complete model input database covering pretreatment, biological treatment, advanced treatment, and sludge disposal. Specifically, this includes: real-time water quality and quantity data and water quality characteristic parameters from the influent side; operating parameters of the pretreatment unit; key operating data of the biological treatment unit; operating parameters of the advanced treatment unit; relevant data from the sludge disposal unit; in addition, it also includes equipment operating status data, environmental monitoring data and historical effluent water quality compliance data, reagent consumption ledgers, and sludge return and discharge records. Step S13, Global Sensitivity Analysis and Parameter Identification: Using variance-based analysis or Morris screening, a global sensitivity analysis is performed on the key design and operating parameters of each treatment unit in the model to identify parameters that affect the final effluent quality, overall operating cost, and greenhouse gas emission environmental indicators of the system.

3. The method for software simulation and optimization of wastewater treatment process according to claim 1, characterized in that, Step S2 includes the following steps: Step S21, Parameter System Calibration and Optimization: Based on long-term operation monitoring data of the wastewater treatment plant, the identified high-sensitivity parameters are systematically calibrated using manual iteration or automatic optimization algorithms. Step S22, Multi-index cross-validation: Using an independent running dataset that did not participate in model calibration, the predictive performance of the calibrated model is comprehensively validated from multiple dimensions, including the stability of effluent quality compliance, the accuracy of energy and material consumption, and the rationality of interactions between process units.

4. The method for software simulation and optimization of wastewater treatment process according to claim 1, characterized in that, Step S3 includes the following steps: Step S31, Multi-scenario Full-Process Operation Simulation: Based on the digital twin model of the entire process of pretreatment-biological treatment-advanced treatment-sludge disposal verified by measured data, the system constructs a multi-dimensional scenario simulation system—covering influent load scenario, temperature scenario, and full-process operation parameter combination scenario. Through batch simulation output, the pollutant removal efficiency, energy and chemical consumption distribution, sludge production and characteristics of each unit in the entire process under each scenario are generated, and a response relationship database covering the full-process parameter combination, unit treatment effect and system comprehensive performance is constructed. Step S32, Multi-criteria Comprehensive Performance Evaluation: Construct a comprehensive performance evaluation system covering four dimensions: water quality, economy, environment, and stability, providing a basis for quantitative comparison of different operational schemes; specific indicators include: Step S321, Water quality compliance dimensions: Concentration of key pollutants in the effluent of each unit throughout the entire process, final effluent compliance rate, and risk coefficient of water quality exceeding standards; Step S322, Economic Cost Dimension: Total operating cost of the entire process, cost per unit of water treated, and carbon trading cost; Step S323, Environmental Benefit Dimension: Carbon Footprint Intensity, Greenhouse Gas Emission Reduction Potential, Impact Coefficient of Reagent Residue on Receiving Water Bodies; Step S324, System stability dimensions: parameter fluctuation adaptability, equipment fault early warning response efficiency, sludge settling performance, and membrane module fouling control effect; the evaluation process adopts the entropy weight method-TOPSIS combined objective quantitative method to rank the comprehensive performance of each operation scheme and select the globally optimal scheme that achieves stable water quality compliance, optimal operating cost, maximizes environmental benefits, and ensures reliable system operation.

5. The method for software simulation and optimization of wastewater treatment process according to claim 1, characterized in that, Step S4 includes the following steps: Step S41, Quantitative Relationship Model Construction: Using response surface methodology, artificial neural networks, or machine learning tools, establish a quantitative mapping relationship model between key process operating parameters and multiple performance indicators based on simulation data; Step S42, Multi-objective collaborative optimization solution: The multi-objective evolutionary algorithm is used to optimize the solution and obtain the Pareto optimal solution set under the multiple objective constraints of effluent water quality, operating cost and carbon emissions; Step S43: Development of Adaptive Operation Strategy Library: Based on different influent characteristics, seasonal temperature changes, and different management priorities, generate corresponding optimal operating parameter combinations to form an intelligent decision-making strategy library with adaptive capabilities.

6. The method for software simulation and optimization of wastewater treatment process according to claim 1, characterized in that, In step S5, a closed-loop operation mechanism is established, encompassing simulation prediction, optimized decision-making, on-site implementation, real-time monitoring, performance evaluation, and model feedback. By deploying online monitoring instruments and data acquisition systems, real-time system operation data is obtained, the model is periodically recalibrated and updated, and decision-making strategies are continuously optimized based on actual operational performance.

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