Industrial AI-based sewage treatment plant process control system
Through the industrial AI-based sewage treatment plant process control system, automation and precise control of sewage treatment are achieved, solving the problems of high energy consumption, delayed response and unstable treatment effects of traditional sewage treatment plants, and improving the efficiency and economic benefits of sewage treatment.
Patent Information
- Application Number
- CN202510711165.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-29
- Publication Date
- 2025-09-05
AI Technical Summary
Traditional sewage treatment plants rely on fixed rules or manual experience to adjust process parameters, resulting in high energy consumption, delayed response, unstable treatment effects, and a lack of adaptability to complex water quality fluctuations. Existing automation systems are insufficient in data fusion, model generalization, and real-time closed-loop control.
A sewage treatment plant process control system based on industrial AI is adopted, including a data acquisition layer, an edge computing layer, an AI core layer, and a control execution layer. Through multimodal data fusion, industrial AI algorithm analysis and prediction, control instructions are generated to achieve automated control and closed-loop feedback.
It improves the automation level and control accuracy of sewage treatment, reduces the consumption of chemicals and energy, and improves the stability of effluent water quality and economic benefits.
Smart Images
Figure FDA0005427074770000021
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of sewage treatment and relates to a sewage treatment plant process control system based on industrial AI. Background Art
[0002] Traditional sewage treatment plants rely on fixed rules or manual experience to adjust process parameters (such as aeration volume and reagent dosage), resulting in high energy consumption, delayed response, and unstable treatment results. Existing automation systems lack the ability to adapt to complex water quality fluctuations and struggle to optimize multivariable processes in real time. The introduction of industrial AI technology can overcome the limitations of traditional control methods, but existing solutions lack data fusion, model generalization, and real-time closed-loop control. Summary of the Invention
[0003] The purpose of this invention is to address the defects of the existing technology and provide a sewage treatment plant process control system based on industrial AI to improve the efficiency and quality of sewage treatment, reduce operating costs, and realize intelligent control of sewage treatment processes.
[0004] The technical purpose of the present invention is achieved through the following technical solutions:
[0005] A sewage treatment plant process control system based on industrial AI includes a data acquisition layer, an edge computing layer, an AI core layer, and a control execution layer; the data acquisition layer collects real-time data during the sewage treatment process and uses a multimodal data fusion mechanism to fuse multi-source heterogeneous data; the edge computing layer cleans and extracts features from the collected data; the AI core layer uses industrial AI algorithms to analyze, process, and predict data and generate control instructions; the control execution layer adjusts operating parameters according to the control instructions to achieve automatic control of the sewage treatment process, and feeds back the newly collected data to the AI core layer to form a closed-loop control.
[0006] Preferably, the real-time data collected by the data acquisition layer include water quality data, water quantity data, and equipment operation status data; the water quality data include pH, DO, ORP, SS, MLS, chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen, total phosphorus, total nitrogen, turbidity index data and sludge status visual perception data; the equipment operation status data include motor current, vibration spectrum, and bearing temperature data; environmental data is also collected, and the environmental data includes water temperature, light intensity, and rainfall data.
[0007] Preferably, the edge computing layer deploys an industrial edge server, equipped with a time series database and a preprocessing module. The preprocessing module uses sliding window filtering and 3σ outlier processing to clean data, and uses Fourier transform to extract spectral features.
[0008] Preferably, the AI core layer includes a data storage module, a data preprocessing module, an AI algorithm module and a control strategy generation module; the data storage module stores the collected historical data and the data generated during the AI algorithm training process; the data preprocessing module cleans, filters and normalizes the collected data; the AI algorithm module integrates neural networks, support vector machines, and reinforcement learning algorithms, and establishes a mathematical model of the sewage treatment process through learning and training of historical data; the control strategy generation module generates specific control instructions based on the output results of the AI algorithm module.
[0009] Preferably, the mathematical model of the AI algorithm module includes a mechanism model based on the ASM3 activated sludge model, a data-driven model of a deep spatiotemporal network using ConvLSTM combined with Attention, and a real-time optimization engine based on the NSGA-II multi-objective optimization algorithm.
[0010] Preferably, the AI algorithm module uses a neural network algorithm to establish a mapping relationship between water quality indicators and process parameters, and predicts the optimal process parameters by inputting real-time water quality data and equipment operating status data; uses a reinforcement learning algorithm to optimize the sewage treatment process in real time, and automatically adjusts the process parameters according to the current operating status and objective function.
[0011] Preferably, the control instructions generated by the control strategy generation module include adjusting the dosage of sewage treatment agents, changing the operating power of aeration equipment, and regulating the opening of valves.
[0012] Preferably, after the execution device of the control execution layer adjusts the operating parameters according to the control instructions, the system monitors the control effect in real time, feeds back the newly collected data to the AI core layer, and optimizes the control strategy through the AI core layer.
[0013] Preferably, multimodal data fusion adopts a feature-level fusion strategy to perform tensor fusion on the sensor data matrix X∈R^m×n, the process parameter vector P∈R^k and the equipment state vector S∈R^l to generate a feature fusion vector:
[0014]
[0015] Where: X is the sensor data matrix, P is the process parameter vector, and S is the equipment state vector;
[0016] The data driven model is constructed as follows:
[0017] dy / dt=f_mechanism(y,u)+f_AI(y,u,ξ)
[0018] Where f_mechanism is the ASM3 dynamic equation, f_AI is the LSTM residual correction term (AI correction term), ξ is the environmental disturbance term, y is the state variable, and u is the control input.
[0019] Preferably, the real-time optimization engine of the multi-objective optimization algorithm is constructed as follows:
[0020] min[α·E_air+β·E_chemical];
[0021] Where: α and β are the weight coefficients of the two energy consumptions respectively;
[0022] The constraints are:
[0023] COD_out≤50mg / L;
[0024] NH3-N_out≤15mg / L;
[0025] DO∈[2,4]mg / L.
[0026] Compared with the prior art, the present invention has the following beneficial effects:
[0027] The present invention can achieve the following beneficial effects in wastewater treatment:
[0028] Improve the degree of automation: Through the collaborative work of the data acquisition layer, data processing layer and control execution layer, the automated control of the sewage treatment process is achieved, which reduces manual intervention and improves production efficiency.
[0029] Improve control accuracy: Using industrial AI algorithms to model and optimize sewage treatment processes can better adapt to the nonlinear, time-varying, and hysteresis characteristics of the sewage treatment process, achieve precise control of process parameters, and improve the stability of effluent water quality.
[0030] Make full use of data: By storing and analyzing large amounts of historical data and mining the relationships between data, we provide strong support for process optimization and decision-making, and maximize the value of data.
[0031] Reduce operating costs: By optimizing process parameters and reducing manual intervention, the chemical consumption, energy consumption and equipment maintenance costs in the sewage treatment process are reduced, and the economic benefits of the sewage treatment plant are improved. DETAILED DESCRIPTION
[0032] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the preferred embodiments of the present invention are described below in conjunction with specific examples, but this should not be construed as a limitation of this patent.
[0033] As a preferred embodiment of the present invention, we propose a sewage treatment plant process control system based on industrial AI, including a data acquisition layer, an edge computing layer, an AI core layer and a control execution layer; the data acquisition layer collects real-time data during the sewage treatment process and adopts a multimodal data fusion mechanism to fuse multi-source heterogeneous data; the edge computing layer cleans and extracts features from the collected data; the AI core layer uses industrial AI algorithms to analyze, process and predict the data and generate control instructions; the control execution layer adjusts the operating parameters according to the control instructions to achieve automatic control of the sewage treatment process, and feeds back the newly collected data to the AI core layer to form a closed-loop control.
[0034] Specifically, the real-time data collected by the data acquisition layer include water quality data, water quantity data, and equipment operation status data; the water quality data include pH, DO, ORP, SS, MLS, chemical oxygen demand COD, biochemical oxygen demand BOD, ammonia nitrogen NH3-N, total phosphorus TP, total nitrogen TN, turbidity index data and sludge status visual perception data; the equipment operation status data include motor current, vibration spectrum, and bearing temperature data; environmental data is also collected, and the environmental data includes water temperature, light intensity, and rainfall data.
[0035] In the above embodiment, the data acquisition layer is the "sensory organ" of this housekeeper, which collects various types of information during the sewage treatment process at all times: in water quality data, indicators such as pH value, dissolved oxygen (DO), and oxidation-reduction potential (ORP) are like the "health parameters" of sewage, the visual image of sludge status is like the "facial expression" of sewage, and water volume data is like a flowing "water volume ledger". Data such as motor current, vibration spectrum, and bearing temperature during equipment operation are the "physical indicators" of the equipment. Even environmental conditions such as water temperature, light intensity, and rainfall are recorded one by one as a "weather diary".
[0036] The edge computing layer is like the "primary brain" of a housekeeper. It puts the collected messy data into a "filter" - it uses sliding window filtering and 3σ outlier processing to clean up noise and outliers, and then uses the Fourier transform, a "spectrum magnifier", to extract hidden frequency features from the equipment vibration data, preparing for subsequent analysis.
[0037] Specifically, the edge computing layer deploys an industrial edge server, equipped with a time series database and a preprocessing module. The preprocessing module uses sliding window filtering and 3σ outlier processing to clean data, and uses Fourier transform to extract spectral features.
[0038] In some preferred embodiments, the AI core layer includes a data storage module, a data preprocessing module, an AI algorithm module and a control strategy generation module; the data storage module stores the collected historical data and the data generated during the AI algorithm training process; the data preprocessing module cleans, filters and normalizes the collected data; the AI algorithm module integrates neural networks, support vector machines, and reinforcement learning algorithms, and establishes a mathematical model of the sewage treatment process through learning and training of historical data; the control strategy generation module generates specific control instructions based on the output results of the AI algorithm module.
[0039] In some preferred embodiments, the mathematical model of the AI algorithm module includes a mechanism model based on the ASM3 activated sludge model, a data-driven model of a deep spatiotemporal network using ConvLSTM combined with Attention, and a real-time optimization engine based on the NSGA-II multi-objective optimization algorithm.
[0040] As a first preferred embodiment of the present invention, the AI algorithm module uses a neural network algorithm to establish a mapping relationship between water quality indicators and process parameters, and predicts the optimal process parameters by inputting real-time water quality data and equipment operating status data; uses a reinforcement learning algorithm to optimize the sewage treatment process in real time, and automatically adjusts the process parameters according to the current operating status and objective function.
[0041] Specifically, the control instructions generated by the control strategy generation module include adjusting the dosage of sewage treatment agents, changing the operating power of aeration equipment, and adjusting the opening of valves.
[0042] In some embodiments, after the execution device of the control execution layer adjusts the operating parameters according to the control instructions, the system monitors the control effect in real time, feeds back the newly collected data to the AI core layer, and optimizes the control strategy through the AI core layer.
[0043] In some preferred embodiments, multimodal data fusion adopts a feature-level fusion strategy to perform tensor fusion on the sensor data matrix X∈R^m×n, the process parameter vector P∈R^k, and the equipment state vector S∈R^l to generate a feature fusion vector:
[0044]
[0045] Where: X is the sensor data matrix, P is the process parameter vector, and S is the equipment state vector;
[0046] The data driven model is constructed as follows:
[0047] dy / dt=f_mechanism(y,u)+f_AI(y,u,ξ)
[0048] Where f_mechanism is the ASM3 dynamic equation, f_AI is the LSTM residual correction term (AI correction term), ξ is the environmental disturbance term, y is the state variable, and u is the control input.
[0049] In other preferred embodiments, the real-time optimization engine of the multi-objective optimization algorithm is constructed as follows:
[0050] min[α·E_air+β·E_chemical];
[0051] Where: α and β are the weight coefficients of the two energy consumptions respectively;
[0052] The constraints are:
[0053] COD_out≤50mg / L;
[0054] NH3-N_out≤15mg / L;
[0055] DO∈[2,4]mg / L.
[0056] CODout (Chemical Oxygen Demand) and NH3-Nout (Ammonia Nitrogen) are core indicators for measuring wastewater treatment effectiveness. Constraints are set at 50 mg / L and 15 mg / L, respectively, to ensure effluent meets standards. DO (Dissolved Oxygen Concentration) must be maintained between 2 and 4 mg / L, a range that reflects the optimal microbial metabolism in common processes such as the activated sludge process. This ensures treatment efficiency while avoiding energy waste caused by excessive aeration.
[0057] The improved NSGA-II algorithm is used to solve the problem, and a constraint processing mechanism is introduced:
[0058] Adaptive crossover probability: P_c = 0.9-0.5*(g / G_max);
[0059] P_c: crossover probability; g: current number of iterations; G_max: maximum number of iterations;
[0060] Elite retention strategy: the top 10% individuals enter the next generation directly.
[0061] Finally, digital twins are used to verify:
[0062] A high-fidelity virtual sewage treatment plant model was built, and real-time data synchronization between the virtual model and the physical sewage treatment plant was achieved through the OPC UA protocol. The virtual model included the same process flow, equipment parameters, and control logic as the physical system. Before deploying optimization strategies to the physical system, they were simulated in the digital twin environment to verify their impact on effluent quality, energy consumption, and equipment operation, ensuring the safety and reliability of the strategies.
[0063] The model training phase is divided into the following two stages:
[0064] (1) Preprocessing of historical data:
[0065] We collected operational data from the sewage treatment plant over the past three years, including water quality monitoring data, equipment operating parameters, and process operation records. We used the Dynamic Time Warping (DTW) algorithm to align data with different sampling frequencies to eliminate time skew. We extracted 56 characteristic parameters from the raw data (such as COD and ammonia nitrogen concentrations for each treatment unit, and equipment current and speed) to construct a training set. We also identified and labeled data for 12 abnormal operating conditions (such as sludge bulking and abnormal aeration) to train the model's anomaly detection capabilities.
[0066] (2) Model training:
[0067] a: Mechanism model parameter identification: For the ASM3 model, an improved particle swarm optimization algorithm is used to optimize model parameters. During the optimization process, the error between historical water quality monitoring data and model output is used as the objective function. Through iterative search of particles in the parameter space, the optimal parameter combination is found, making the mechanism model more consistent with the actual sewage treatment process.
[0068] b: Deep Network Training: A TCN-LSTM hybrid network was constructed, with a time window of T = 60 minutes. Multimodal fusion data within 60 minutes was used as network input. The TCN (Temporal Convolutional Network) was responsible for extracting local data features, while the LSTM network processed long-term dependencies. The Adam optimizer was used with a learning rate of 0.001 and a mean squared error (MSE) loss function. 100 rounds of iterative training were performed on the training set, with network parameters continuously adjusted to improve model prediction accuracy.
[0069] Online operation stage:
[0070] (1) Real-time reasoning:
[0071] The edge computing layer performs predictions every 5 minutes:
[0072] [y_pred,u_opt]=AI_Model(Z_t,P_curr)
[0073] The output includes: water quality forecast value for the next 30 minutes, optimized control parameters (blower frequency, internal recirculation ratio, valve opening, etc.).
[0074] y_pred: Future output predicted by the model, such as water quality, system energy consumption, etc.
[0075] u_opt: optimized control input, such as aeration volume, dosage of reagents, etc.
[0076] AI_Model: is an intelligent decision-making module (industrial AI model) that combines prediction models and optimization algorithms;
[0077] Z_t: The system state vector at time t. It usually contains the key water quality parameters and operating status at the current moment;
[0078] P_curr: Current control strategy or operating parameters, such as aeration intensity, reagent dosage, reflux ratio, etc.
[0079] (2) Security control mechanism:
[0080] The confidence threshold θ is set to 0.85. When the prediction confidence is less than θ, the key parameters of the expert rule base are switched to double verification (LIME interpretability analysis + mechanism model verification).
[0081] In the present invention, the AI core layer is the "smart center" of the housekeeper, and the various modules inside have clear division of labor: the data storage module is like a "historical database", storing all the collected historical data and data generated during model training; the data preprocessing module is a "data organizer", cleaning, filtering and normalizing the data to make them neat and tidy; the AI algorithm module is a "smart researcher", which uses neural networks, support vector machines, reinforcement learning and other algorithms, combined with historical data to "learn" the laws of sewage treatment and establish accurate mathematical models, such as the mechanism model based on the ASM3 activated sludge model, the deep spatiotemporal network model integrating ConvLSTM and Attention, and the real-time optimization engine based on the NSGA-II algorithm; the control strategy generation module is like a "decision publisher", which generates specific control instructions such as adjusting the dosage of the agent, changing the power of the aeration equipment, and adjusting the valve opening according to the analysis results of the algorithm module.
[0082] The control execution layer is the steward's "action arm." Its executive devices take immediate action upon receiving instructions: adjusting the dosage of chemical reagents is like "precisely feeding" wastewater, changing the power of aeration equipment is like "regulating the wastewater's breathing rhythm," and adjusting valve openings is like "planning the wastewater's flow path." Furthermore, after the action is taken, the system acts as a "performance inspector" to monitor control effectiveness in real time, feeding newly collected data back to the AI core layer, allowing the "intelligent hub" to promptly optimize control strategies, forming a complete closed loop of "perception-analysis-decision-execution-feedback."
[0083] In general, the system of the present invention is like an intelligent housekeeper who can "think", "act" and "review". Through layer-by-layer coordination and continuous optimization, it makes the sewage treatment process both efficient and energy-saving, and ensures that the effluent water quality meets the standards, truly realizing the intelligent management of sewage treatment.
[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.
Claims
1. A sewage treatment plant process control system based on industrial AI, characterized in that: It includes a data acquisition layer, an edge computing layer, an AI core layer and a control execution layer; the data acquisition layer collects real-time data during the sewage treatment process and adopts a multimodal data fusion mechanism to fuse multi-source heterogeneous data; the edge computing layer cleans and extracts features from the collected data; the AI core layer uses industrial AI algorithms to analyze, process and predict data and generate control instructions; the control execution layer adjusts operating parameters according to the control instructions to achieve automatic control of the sewage treatment process, and feeds back the newly collected data to the AI core layer to form a closed-loop control.
2. The process control system for a sewage treatment plant based on industrial AI according to claim 1 is characterized in that: The real-time data collected by the data acquisition layer include water quality data, water quantity data, and equipment operation status data; the water quality data include pH, DO, ORP, SS, MLS, chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen, total phosphorus, total nitrogen, turbidity index data and sludge status visual perception data; the equipment operation status data include motor current, vibration spectrum, and bearing temperature data; environmental data is also collected, and the environmental data includes water temperature, light intensity, and rainfall data.
3. The process control system for a sewage treatment plant based on industrial AI according to claim 1 is characterized in that: The edge computing layer deploys an industrial edge server, equipped with a time series database and a preprocessing module. The preprocessing module uses sliding window filtering and 3σ outlier processing to clean data, and uses Fourier transform to extract spectral features.
4. The process control system for a sewage treatment plant based on industrial AI according to claim 1 is characterized in that: The AI core layer includes a data storage module, a data preprocessing module, an AI algorithm module and a control strategy generation module; the data storage module stores the collected historical data and the data generated during the AI algorithm training process; the data preprocessing module cleans, filters and normalizes the collected data; the AI algorithm module integrates neural networks, support vector machines, and reinforcement learning algorithms, and establishes a mathematical model of the sewage treatment process through learning and training of historical data; the control strategy generation module generates specific control instructions based on the output results of the AI algorithm module.
5. The process control system for a sewage treatment plant based on industrial AI according to claim 4 is characterized in that: The mathematical model of the AI algorithm module includes a mechanism model based on the ASM3 activated sludge model, a data-driven model using a deep spatiotemporal network combining ConvLSTM and Attention, and a real-time optimization engine based on the NSGA-II multi-objective optimization algorithm.
6. The industrial AI-based sewage treatment plant process control system according to claim 4 is characterized in that: The AI algorithm module uses a neural network algorithm to establish a mapping relationship between water quality indicators and process parameters, and predicts the optimal process parameters by inputting real-time water quality data and equipment operating status data; Reinforcement learning algorithms are used to optimize the sewage treatment process in real time, and process parameters are automatically adjusted according to the current operating status and objective function.
7. The industrial AI-based sewage treatment plant process control system according to claim 4 is characterized in that: The control instructions generated by the control strategy generation module include adjusting the dosage of sewage treatment agents, changing the operating power of aeration equipment, and regulating the opening of valves.
8. The process control system for a sewage treatment plant based on industrial AI according to claim 1 is characterized in that: After the execution devices of the control execution layer adjust the operating parameters according to the control instructions, the system monitors the control effect in real time, feeds back the newly collected data to the AI core layer, and optimizes the control strategy through the AI core layer.
9. The process control system for a sewage treatment plant based on industrial AI according to claim 5 is characterized in that: Multimodal data fusion adopts a feature-level fusion strategy to perform tensor fusion on the sensor data matrix X∈R^m×n, the process parameter vector P∈R^k and the equipment state vector S∈R^l to generate a feature fusion vector: Where: X is the sensor data matrix, P is the process parameter vector, and S is the equipment state vector; The data driven model is constructed as follows: dy / dt=f_mechanism(y,u)+f_AI(y,u,ξ) Where f_mechanism is the ASM3 dynamic equation, f_AI is the LSTM residual correction term (AI correction term), ξ is the environmental disturbance term, y is the state variable, and u is the control input.
10. The industrial AI-based sewage treatment plant process control system according to claim 5, characterized in that: The real-time optimization engine of the multi-objective optimization algorithm is built as: min[α·E_air+β·E_chemical]; Where: α and β are the weight coefficients of the two energy consumptions respectively; The constraints are: COD_out≤50mg / L; NH3-N_out≤15mg / L; DO∈[2,4]mg / L.