An on-line operation simulation system and method for an ozone-biological activated carbon process of a water plant

Abstract

The application discloses an online operation simulation system of an ozone-biological activated carbon process of a water plant, and relates to the technical field of water treatment. The system comprises a data acquisition module, an analog analysis module and an optimization control module. The data acquisition module is used for acquiring operation parameters and water quality parameters of a target process in real time. The analog analysis module is used for performing cooperative simulation based on the operation parameters and the water quality parameters to obtain simulation results. The optimization control module is used for generating process optimization suggestions according to the simulation results. The application further provides an online operation simulation method. The system provided by the application can solve the problem that a traditional static model is disconnected with actual dynamic operation.

Description

Technical Field

[0001] This invention belongs to the field of advanced drinking water treatment and process optimization control technology, and particularly relates to an online operation simulation system and method for an ozone-biological activated carbon process in a water plant. Background Technology

[0002] The O3-BAC combined process is one of the most effective advanced treatment technologies in the field of water treatment. This process combines the strong oxidizing power of ozone oxidation with the synergistic effect of adsorption and biodegradation of biological activated carbon, which can efficiently remove organic matter, ammonia nitrogen, odor, and disinfection by-product precursors from water, significantly improving the quality and safety of drinking water. It has been widely used in water plants in large and medium-sized cities in my country.

[0003] However, the operation, control, and management of the O3-BAC process face numerous severe challenges in actual production. This process is essentially a complex interplay of ozonation (chemical oxidation), activated carbon (physical adsorption), and microbial biodegradation within the activated carbon layer, involving synergistic, sequential, and even competitive processes. Its treatment efficiency is influenced by a complex interplay of factors, including influent water quality (such as organic matter composition, concentration, pH, and temperature), ozone dosage, contact time, and the operating status of the activated carbon filter (empty bed contact time, backwashing strategy, and biofilm activity). Currently, most water plants operate primarily based on manual experience, periodic testing, and limited fixed control logic, lacking a precise understanding of the process's inherent processes and real-time performance, operating in a "black box" or "grey box" mode. Furthermore, in actual production, laboratory test results for water quality indicators are severely delayed, typically by several hours or even a day, failing to reflect the real-time status of the process. While online water quality instruments can provide some real-time data, they often cannot directly characterize the removal efficiency of organic matter or the biological activity of the activated carbon. This results in reactive, "post-event" adjustments to operations, failing to anticipate and intervene before water quality fluctuations occur, leading to potential risks to effluent quality. Currently, the ozone dosing system and activated carbon filter control system typically operate independently, lacking coordination. For example, if the ozone dosage is not optimized based on the subsequent biological activity of the activated carbon, insufficient ozone oxidation may lead to excessive load on subsequent biological treatment or over-oxidation. This fragmented control approach cannot achieve optimal efficiency and minimum energy and chemical consumption for the entire O3-BAC system. Existing water plant automation systems primarily rely on data acquisition and simple control, lacking a core online simulation and optimization decision support system capable of integrating real-time data, fusing process mechanisms, and possessing self-learning and adaptive capabilities.

[0004] Patent document CN117088542A discloses a multifunctional water treatment process technology verification platform. The processes and processes involved in the platform include conventional coagulation sedimentation sand filtration treatment processes, ozone biological activated carbon treatment processes, ultraviolet hydrogen peroxide biological activated carbon processes, ultrafiltration membrane processes, nanofiltration membrane processes, disinfection processes, and processes using multiple water treatment processes in combination. By setting overpass pipes and valves, different water treatment processes can be freely and flexibly combined. While conducting simulation studies of various water treatment processes, it can also conduct performance evaluation studies on different products such as pretreatment agents, coagulants, disinfectants, and ultrafiltration membranes and nanofiltration membranes.

[0005] Patent document CN115947490A discloses a method for optimizing the operating parameters of an ozone-biological activated carbon process. This method includes establishing a small-scale laboratory setup. Firstly, by varying the pre-ozone dosage and oxidation contact time, the changes in effluent turbidity, COD, and bromate concentration during the subsequent coagulation stage are investigated. A kinetic equation is established to determine the optimal pre-ozone dosage and oxidation contact time. Secondly, the relationship between different main ozone dosages and the ratio of main ozone to biological activated carbon reaction time with effluent turbidity, COD, bromate, and microbial concentration is examined. A kinetic equation is then established to determine the optimal ratio of main ozone dosage and main ozone to biological activated carbon reaction time. This method achieves the goal of precisely adding ozone and rationally controlling the ozone / biological activated carbon reaction time while improving effluent quality, thus ensuring the efficient operation of the ozone-biological activated carbon process. Summary of the Invention

[0006] The purpose of this invention is to provide an online operation simulation system and method for the ozone-biological activated carbon process in water plants, which can solve the problem of the disconnect between traditional static models and actual dynamic operation.

[0007] To achieve the first objective of this invention, the following technical solution is provided: an online operation simulation system for an ozone-biological activated carbon process in a water treatment plant, comprising: The data acquisition module is used to acquire the operating parameters and water quality parameters of the target process in real time; The simulation analysis module performs collaborative simulations based on operating parameters and water quality parameters to obtain simulation results. The optimization control module is used to generate process optimization suggestions based on simulation results.

[0008] This invention combines the actual operation of the O3-BAC process with real-time data for dynamic simulation, prediction of the effluent water quality, and the provision of optimized control commands in an online operation simulation technology. This technology enables the intelligent upgrade of the process from "experience-driven" to "data and model-driven", ensuring stable water supply quality, improving operational efficiency, and reducing production costs.

[0009] Specifically, the operating parameters include one or more of the following: ozone dosage, ozone contact time, activated carbon filter rate, backwashing frequency, and head loss. The water quality parameters include one or more of the following: ultraviolet absorbance of influent and effluent, permanganate index, ammonia nitrogen content, pH value, and water temperature.

[0010] Specifically, the simulation analysis module includes: The ozone oxidation unit dynamic sub-model is based on ozone mass transfer kinetics and reaction kinetics to simulate the oxidation process of organic matter by ozone and the change in the content of biodegradable organic matter. The dynamic sub-model of the biological activated carbon filter unit, coupled with the dynamic sub-model of the ozone oxidation unit, is based on adsorption kinetics and biodegradation kinetics to simulate the adsorption of pollutants by activated carbon and the microbial degradation process.

[0011] Specifically, the output parameters of the ozone oxidation unit dynamic sub-model include the predicted effluent electro-oxidation level index and the increase in biodegradable organic matter, which serve as the input parameters of the biological activated carbon filter unit dynamic sub-model.

[0012] Specifically, it also includes a model calibration module, which is used to periodically compare the predicted effluent water quality data with the actual monitored effluent water quality data, and adaptively adjust the parameters in the simulation analysis module based on the comparison results.

[0013] Specifically, the process optimization suggestions include ozone dosage, activated carbon filter filtration rate, or backwashing cycle, and can be sent to the water plant's automatic control system for execution via a communication interface.

[0014] Specifically, it also includes a user interface for visually displaying real-time operating data, simulation prediction curves, process status evaluation results, and process optimization suggestions generated by the optimization control module.

[0015] To achieve the second objective of this invention, the following technical solution is provided: an online operation simulation method, implemented through the online operation simulation system of the above-mentioned water plant ozone-biological activated carbon process.

[0016] Specifically, the steps of the online simulation method are as follows: Real-time acquisition of operating parameters and water quality parameters of the ozone-biological activated carbon process; inputting the acquired parameters into a pre-established dynamic coupling mathematical model to simulate the synergistic process of ozone oxidation and biological activated carbon adsorption and degradation online, and predicting key effluent water quality indicators for future periods; based on the simulation and prediction results, optimization analysis is performed and process optimization suggestions or control instructions are generated.

[0017] Specifically, the dynamic coupling mathematical model is a mechanistic model, a data-driven model based on machine learning, or a hybrid model combining both.

[0018] Specifically, the optimization analysis is performed with the dual objectives of achieving effluent quality standards and optimizing operating costs.

[0019] Specifically, the prediction results of the dynamic coupling mathematical model are verified by using actual effluent water quality monitoring data, and the model parameters are dynamically adjusted to maintain simulation accuracy.

[0020] Specifically, the simulation process can diagnose abnormal process conditions, including abnormal decrease in ozone oxidation efficiency, depletion of bioactive carbon bioactivity, or near-saturation of adsorption capacity, and trigger early warning accordingly.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: By combining the actual operation of the O3-BAC process with real-time data for dynamic simulation, predicting the effluent water quality, and providing optimized control commands, the online operation simulation technology has an urgent practical need and significant technical value for realizing the intelligent upgrade of this process from "experience-driven" to "data and model-driven", ensuring stable water supply quality, improving operational efficiency, and reducing production costs. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the online operation simulation system for the ozone-biological activated carbon process in the water plant provided in this embodiment. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0024] like Figure 1 As shown, this embodiment provides an online operation simulation system for a water plant ozone-biological activated carbon process, comprising: The data acquisition module is used to acquire the operating parameters and water quality parameters of the target process in real time; The simulation analysis module performs collaborative simulations based on operating parameters and water quality parameters to obtain simulation results. The optimization control module is used to generate process optimization suggestions based on simulation results.

[0025] More specifically, the core of this embodiment lies in constructing a closed-loop intelligent system of "data perception - model simulation - decision output".

[0026] Operating parameters: These are obtained in real time through the water plant's existing automatic control system (such as PLC, DCS), including but not limited to raw water and process flow rates, ozone dosage concentration and cumulative amount, ozone contact time, filtration rate of activated carbon filter, head loss, backwashing frequency and duration, and key pressure and level signals of each process unit.

[0027] Water quality parameters: Data are obtained through online water quality instruments and manual laboratory testing. When monitoring conditions are available, online data may include influent and effluent turbidity, pH, temperature, conductivity, dissolved oxygen, residual ozone, and UV. 254 Permanganate index, etc. Laboratory data, used as high-precision calibration data, are periodically input into the system to calibrate the online monitoring data.

[0028] Core simulation and computation: This includes the dynamic coupling mechanism model constructed in this embodiment, which is a framework in which multiple sub-models work together.

[0029] Dynamic sub-model of ozone oxidation unit: This sub-model is based on the principle of ozone mass transfer-reaction kinetics, and the input parameters are influent water quality, ozone dosage, contact time, water temperature, pH, etc. The reaction kinetic parameters of this sub-model can be dynamically correlated through the characteristics of influent and effluent water quality.

[0030] Dynamic sub-model of biological activated carbon filter unit: This sub-model is a dynamic simulator of the synergistic effect of adsorption and biodegradation. It comprehensively considers the following processes: 1) Physical adsorption: Based on a multi-component adsorption kinetic model, the dynamic consumption of activated carbon adsorption capacity is simulated.

[0031] 2) Biodegradation: Based on biofilm growth kinetics and substrate consumption kinetics, the model simulates the removal process of substances such as BDOC and ammonia nitrogen by microorganisms in the filter. The key state variable of the model is the bioactivity index of activated carbon, which dynamically changes with operating time, backwashing, influent substrate concentration, and water temperature.

[0032] 3) Hydraulics and mass transfer: Consider the impact of empty bed contact time (EBCT) on the treatment effect.

[0033] Model Coupling and Integration: Outputs of Ozone Oxidation Unit Sub-models (e.g., effluent UV) 254The incremental BDOC is directly used as input to the biological activated carbon filter sub-model. The two sub-models are tightly coupled over time to achieve online continuous simulation.

[0034] Optimize decision-making and output: Transform the simulation results of the core model into instructions or suggestions that directly guide production.

[0035] The model has predictive capabilities: based on current operating parameters and real-time influent water quality, the model can predict the changing trends of key water quality indicators in the activated carbon filter effluent over the next few hours.

[0036] The model has diagnostic and early warning functions: by comparing simulated values ​​with design values ​​or safety thresholds, the system can diagnose process abnormalities such as ozone oxidation process efficiency abnormalities, biological activity depletion in biological activated carbon process, and adsorption saturation, and issue early warnings in advance.

[0037] The model features optimized control capabilities: the system incorporates an optimization algorithm to provide recommended optimal setpoints, aiming to achieve both water quality compliance and minimize operating costs. Typical outputs include: optimal ozone dosage, optimal filtration rate range for activated carbon filters, and backwashing recommendations triggered by simulated filter head loss and biological activity status. These recommendations can be pushed to operators via a human-machine interface or directly transmitted to the water plant's automation system for automatic adjustment via standard communication protocols.

[0038] This embodiment also provides an online operation simulation method, which is implemented through the online operation simulation system for the ozone-biological activated carbon process in water plants provided in the above embodiments.

[0039] To better illustrate the effectiveness of the solution provided in this embodiment, the following specific implementation examples are provided.

[0040] 1. System hardware and software environment setup This embodiment was implemented in a waterworks with a daily treatment capacity of 200,000 tons using the O3-BAC deep treatment process.

[0041] Data acquisition server: An industrial-grade server is used to deploy data acquisition and storage software. It communicates with the water plant’s existing distributed control system via the OPCUA protocol to read operating parameters such as ozone generator power, ozone dosing flow rate, activated carbon filter valve opening, filtration rate, and head loss in real time.

[0042] Water quality data interface: Connects to an online water quality instrument network via Modbus TCP protocol to acquire real-time data on pH, temperature, turbidity, and UV of raw water and process effluent. 254 Dissolved oxygen data. The Laboratory Information Management System (LIMS) uploads manually measured COD, ammonia nitrogen, and DOC data to a designated database daily at set times.

[0043] Application server: Deploys the core simulation and optimization software system of this invention. This software adopts a modular design, is developed using languages ​​such as Python or C#, and includes a dynamic coupling mechanism model, an optimization algorithm library, and a model calibration engine.

[0044] Client: The operator station in the water plant's central control room accesses the system via a web browser or client software. The graphical user interface (GUI) displays all simulation results and optimization suggestions.

[0045] 2. Software Module Function Implementation The system software operates according to the following process: The system synchronizes data from the DCS, online instruments, and LIMS database every 1 minute. Abnormal data is cleaned and interpolated to create a complete dataset with timestamp alignment.

[0046] Real-time data after cleaning, such as raw water UV... 254 = 0.035 cm⁻¹, water temperature 18°C, ozone dosage 2.0 mg / L, input into the core model. The ozone oxidation unit dynamic sub-model immediately starts calculation. The model determines the difficulty of organic matter degradation based on the current water quality, such as calculating SUVA=UV 254 / DOC=3.5 L / (mg·m). Based on built-in logic, the model dynamically estimates: under the current ozone dosage of 2.0 mg / L, UV 254 The removal rate is expected to be 40%, while approximately 0.15 mg / L of organic matter will be converted into BDOC. This incremental BDOC value (ΔBDOC) is output in real time.

[0047] The dynamic sub-model of the biological activated carbon filter receives effluent prediction data from the ozone unit, including residual UV. 254 And ΔBDOC. The model, taking into account the current state of the filter, such as having operated continuously for 120 hours, extrapolates its current bioactivity index as "moderate" based on historical data, and simulates the adsorption and biodegradation process of these pollutants in the filter layer. Ultimately, the model predicts that the effluent COD of the filter will be 1.8 mg / L, and determines that its remaining adsorption capacity is approximately 60%.

[0048] The optimization decision-making module compared the predicted effluent COD (1.8 mg / L) with the internal control target (≤2.0 mg / L). It found that the current operation was safe but not optimal. The module then initiated an optimization algorithm, performing calculations with the goal of "lowest ozone cost." The algorithm found that slightly reducing the ozone dosage from 2.0 mg / L to 1.8 mg / L resulted in a predicted effluent COD of 1.95 mg / L, still achieving stable compliance, and saving approximately 5% in ozone cost per ton of water.

[0049] The system issues a graphic warning through the operator station interface: "Recommendation: The ozone dosage can be optimized to 1.8 mg / L, which is expected to save on chemical consumption and stabilize water quality." The operator can confirm and adjust manually, or authorize the system to automatically send the set value to the ozone generator control system via OPC command.

[0050] The system automatically performs calibration tasks during the low-load period in the early morning each day. It compares the model data from the past 24 hours with the effluent UV levels. 254 All predicted values ​​are compared with laboratory measurements. If a systematic bias is found, the calibration engine is invoked, using data from the past 7 days to recalibrate key parameters in the model, such as ozone and UV. 254 The reaction rate is fine-tuned to make the model's predicted curve closer to the actual measurement point, thus completing self-learning.

[0051] Example 2: This embodiment demonstrates the core value of the present invention in responding to sudden pollution of raw water.

[0052] One day, the system's data acquisition module detected UV in the raw water. 254 The value increased continuously from 0.030 cm⁻¹ to 0.045 cm⁻¹ within 2 hours.

[0053] When raw water UV 254 As the ozone concentration began to rise, the dynamic coupling model was immediately triggered to recalculate. The model simulation showed that, operating at the current ozone dosage, the COD of the BAC effluent would exceed the internal control limit of 2.0 mg / L after 1.5 hours.

[0054] The system immediately issued a flashing red alarm on the interface and simultaneously provided decision-making suggestions: "Warning: Raw water organic matter concentration is rising. Simulation predicts that the COD of the effluent may exceed the standard in 90 minutes. Recommended solution: Immediately increase the ozone dosage from 1.8 mg / L to 2.2 mg / L to offset the impact in advance and maintain water quality stability." The operator can confirm and implement the recommended plan within minutes of receiving the alarm. The system has already increased the ozone dosage before the pollution load reaches the ozone contact tank, thus intercepting the water quality fluctuation.

[0055] This invention transforms the traditional "delayed response" mode into an "advanced prediction and proactive interception" mode, which significantly enhances the process's resilience and ensures water supply security.

[0056] Example 3: This embodiment illustrates how the present invention breaks down unit barriers and achieves global optimization.

[0057] When the water temperature is low in winter, the activity of microorganisms in the BAC filter is reduced.

[0058] The BAC dynamic sub-model assesses its bioactivity index as "low" by analyzing historical removal efficiency and water temperature.

[0059] The optimization decision-making module uses "minimizing the total system operating cost (ozone cost + power consumption)" as the global objective to perform simulation optimization. When the biological activity of BAC is "low", blindly increasing the ozone dosage has limited benefits. Instead, the filtration rate should be appropriately reduced to prolong the contact time between wastewater and activated carbon and compensate for the insufficient biological activity.

[0060] The system's final recommendation was a set of combined instructions: ① Reduce the filtration rate of the third BAC filter group from 8 m / h to 7 m / h to improve treatment efficiency; ② Maintain the ozone dosage at 1.7 mg / L. After implementing this strategy, the effluent quality stabilized, and the system's total energy consumption was reduced by 8% compared to the traditional empirical method.

[0061] Furthermore, the terms "upper," "lower," "inner," "outer," "front," and "rear" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0062] Of course, the above description is only a specific embodiment of the present invention and is not intended to limit the scope of the present invention. All equivalent changes or modifications made to the structure, features and principles described in the claims of the present invention should be included in the scope of the claims of the present invention.

[0063] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An online operation simulation system for an ozone-biological activated carbon process of a water plant, characterized by, include: The data acquisition module is used to acquire the operating parameters and water quality parameters of the target process in real time; The simulation analysis module performs collaborative simulations based on operating parameters and water quality parameters to obtain simulation results. The optimization control module is used to generate process optimization suggestions based on simulation results.

2. The online operation simulation system of an ozone-BAC process of a water plant according to claim 1, characterized in that, The operating parameters include one or more of the following: ozone dosage, ozone contact time, activated carbon filter rate, backwashing frequency, and head loss. The water quality parameters include one or more of the following: ultraviolet absorbance of influent and effluent, permanganate index, ammonia nitrogen content, pH value, and water temperature.

3. The online operation simulation system of an ozone-BAC process of a water plant according to claim 1, wherein The simulation analysis module includes: The ozone oxidation unit dynamic sub-model is based on ozone mass transfer kinetics and reaction kinetics to simulate the oxidation process of organic matter by ozone and the change in the content of biodegradable organic matter. The dynamic sub-model of the biological activated carbon filter unit, coupled with the dynamic sub-model of the ozone oxidation unit, is based on adsorption kinetics and biodegradation kinetics to simulate the adsorption of pollutants by activated carbon and the microbial degradation process.

4. The online operation simulation system of an ozone-BAC process of a water plant according to claim 3, characterized in that, The output parameters of the ozone oxidation unit dynamic sub-model include the predicted effluent electro-oxidation level index and the increase in biodegradable organic matter, which serve as the input parameters of the biological activated carbon filter unit dynamic sub-model.

5. The online operation simulation system of an ozone-BAC process of a water plant according to claim 1, wherein It also includes a model calibration module, which is used to periodically compare the predicted effluent water quality data with the actual monitored effluent water quality data, and adaptively adjust the parameters in the simulation analysis module based on the comparison results.

6. The online operation simulation system for the ozone-biological activated carbon process in water plants according to claim 1, characterized in that, The proposed process optimization suggestions include ozone dosage, activated carbon filter filtration rate, or backwashing cycle, and can be sent to the water plant's automatic control system for execution via a communication interface.

7. The online operation simulation system for the ozone-biological activated carbon process in water plants according to claim 1, characterized in that, It also includes a user interface for visually displaying real-time operating data, simulation prediction curves, process status evaluation results, and process optimization suggestions generated by the optimization control module.

8. An online simulation method, characterized in that, This is achieved through an online operation simulation system for the ozone-biological activated carbon process in water plants as described in any one of claims 1 to 7.

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