Intelligent optimization control system and method for electrochemical reaction process

Abstract

The invention relates to the technical field of electrochemistry, in particular to an intelligent optimization control system and method for an electrochemical reaction process, and the system comprises a data acquisition module, a data preprocessing module, a data modeling module, a model evaluation module and a data analysis module, the data mining module is in communication connection with the model evaluation module and receives the evaluation result sent by the model evaluation module; based on the evaluation result, mining the correlation between the reaction characteristics and the product yield from historical data; identifying key parameters influencing the product yield; the control module is in communication connection with the data mining module and receives the key parameter information sent by the data mining module; adjusting reaction conditions based on the key parameter information; reaction parameters are optimized, an optimal electrochemical reaction parameter combination is found through a trial-and-error method or a heuristic method, a reaction kinetic model can be accurately established, and the nonlinearity and time-varying characteristics of the electrochemical reaction are effectively captured.

Description

Technical Field

[0001] The present invention relates to the field of electrochemistry technology, in particular to an intelligent optimization control system and method for the electrochemistry reaction process. Background Art

[0002] Electrochemical reactions play a crucial role in modern industrial production and are widely used in many fields such as energy, materials, and environmental protection. With the industrial upgrading and technological progress, higher requirements are put forward for the control and optimization of the electrochemical reaction process. Traditional electrochemical reaction control methods mainly rely on empirical models and PID controllers. Although they can achieve basic process control to a certain extent, in the face of complex and changeable reaction environments and increasingly strict production requirements, these methods are inadequate.

[0003] In recent years, with the development of artificial intelligence and big data technologies, some researchers have tried to introduce machine learning algorithms into the field of electrochemical reaction control. For example, some researchers have proposed an electrolytic cell model based on neural networks to predict the concentration of reaction products. Other scholars have explored methods for optimizing electrode materials using genetic algorithms. Although these attempts have made certain progress, there are still many problems and limitations.

[0004] Firstly, existing intelligent control methods often only focus on a single optimization goal, such as product yield or energy efficiency, and it is difficult to balance multiple goals. Secondly, most methods simply apply artificial intelligence algorithms to a specific link, lacking systematic and overall consideration. Moreover, when dealing with high-dimensional and non-linear electrochemical reaction data, existing methods often face problems such as low computational efficiency and poor model generalization ability. In addition, since many intelligent control systems lack interpretability, their decision-making processes are a "black box" to operators, which may cause safety hazards in actual production.

[0005] Finally, existing methods generally lack consideration of optimization goals at different time scales. The electrochemical reaction process usually involves multiple time scales from milliseconds to hours. How to achieve optimal control at these different scales is an urgent problem to be solved. Summary of the Invention

[0006] Facing these challenges, there is an urgent need for a control system and method that can comprehensively, systematically, and intelligently optimize the electrochemical reaction process. The present invention is precisely proposed in view of the above problems, aiming to construct an intelligent optimization control system integrating data acquisition, preprocessing, modeling, evaluation, mining, and control to achieve efficient, stable, and intelligent operation of the electrochemical reaction process.

[0007] The present invention proposes an intelligent optimization control system for the electrochemical reaction process, including:

[0008] Data acquisition module, for:

[0009] Collect characteristic parameter data of the electrochemical reaction process, including voltage, current density, concentrations of reactants and products, reaction temperature;

[0010] Data preprocessing module, communicatively connected to the data acquisition module, for:

[0011] Receive the characteristic parameter data sent by the data acquisition module;

[0012] Clean and preprocess the characteristic parameter data, including removing outliers and filling missing values;

[0013] Data modeling module, communicatively connected to the data preprocessing module, for:

[0014] Receive the preprocessed data sent by the data preprocessing module;

[0015] Based on the preprocessed data, construct an electrochemical reaction kinetics model using deep learning algorithms;

[0016] Model evaluation module, communicatively connected to the data modeling module, for:

[0017] Receive the kinetics model sent by the data modeling module;

[0018] Use the kinetics model to predict the concentrations of reactants and products;

[0019] Compare the prediction results with the actually measured concentration data to evaluate the accuracy of the kinetics model;

[0020] Data mining module, communicatively connected to the model evaluation module, for:

[0021] Receive the evaluation results sent by the model evaluation module;

[0022] Based on the evaluation results, mine the correlation between reaction characteristics and product yield from historical data;

[0023] Identify the key parameters affecting product yield;

[0024] Control module, communicatively connected to the data mining module, for:

[0025] Receive the key parameter information sent by the data mining module;

[0026] Based on the key parameter information, adjust the reaction conditions;

[0027] Optimize the reaction parameters, and find the optimal combination of electrochemical reaction parameters through the trial-and-error method or heuristic method.

[0028] Preferably, the data preprocessing module includes:

[0029] A data cleaning unit for removing useless data, such as parameters irrelevant to the reaction process;

[0030] An outlier processing unit for detecting and removing outliers through statistical methods and data visualization;

[0031] A feature extraction unit for extracting useful features from the original data using deep learning algorithms or statistical methods;

[0032] A normalization processing unit for mapping the feature parameter values to a specific interval to eliminate scale differences.

[0033] Preferably, the deep learning algorithms adopted by the data modeling module include:

[0034] A feature extraction neural network for processing the original data and extracting useful features;

[0035] A time series model for learning the kinetic process of the electrochemical reaction;

[0036] A recurrent neural network for modeling the process of the electrochemical reaction;

[0037] A long short-term memory network for learning the correlation between reaction parameters and product yield.

[0038] Preferably, the data mining module includes:

[0039] An association rule analysis unit for analyzing the association rules between feature parameters and product yield in the data;

[0040] A decision tree analysis unit for constructing a decision tree model to identify the key factors affecting product yield;

[0041] A feature importance evaluation unit for calculating the influence degree of each feature parameter on product yield;

[0042] An optimal condition identification unit for determining the optimal reaction conditions based on the data mining results.

[0043] Preferably, the control module includes:

[0044] A parameter optimization unit for searching for the optimal reaction parameter combination using intelligent optimization algorithms, such as the Monte Carlo method or the particle swarm optimization algorithm;

[0045] A multi-objective optimization unit for considering constraint conditions such as energy consumption and production cost while increasing product yield;

[0046] An online adaptive optimization unit for dynamically adjusting control parameters according to real-time collected process data;

[0047] A reinforcement learning unit for continuously learning and optimizing control strategies through interaction with the environment.

[0048] Preferably, it further includes:

[0049] A multi-sensor data fusion module, communicatively connected to the data acquisition module, for:

[0050] Receiving data of multiple electrochemical sensors sent by the data acquisition module;

[0051] Performing fusion analysis on the data of the multiple electrochemical sensors;

[0052] Assigning weights to each sensor to improve the robustness and accuracy of the model.

[0053] Preferably, the data modeling module further includes:

[0054] An inverse problem optimization unit for:

[0055] Constructing an inverse problem model of the electrochemistry reaction kinetics model;

[0056] Combining the data mining results with the error of the actual reaction process to adjust the model parameters;

[0057] Continuously optimizing the model to improve the prediction accuracy.

[0058] Preferably, the control module further includes:

[0059] A multi-time scale optimization unit for:

[0060] Considering optimization objectives at different time scales, including real-time control and long-term optimization;

[0061] Coordinating the balance between short-term benefits and long-term performance;

[0062] Adjusting the control strategy according to the optimization results at different time scales.

[0063] Preferably, it further includes:

[0064] An interpretability analysis module, communicatively connected to the data mining module and the control module, for:

[0065] Receiving key parameter information sent by the data mining module;

[0066] Receiving optimization strategy information sent by the control module;

[0067] Analyzing the model decision-making process and providing interpretable optimization suggestions;

[0068] Generate a visual report on the optimization of the reaction process.

[0069] The optimization control method based on the intelligent optimization control system for the electrochemical reaction process includes the following steps:

[0070] Step 1: Collect the characteristic parameter data of the electrochemical reaction process;

[0071] Step 2: Preprocess the characteristic parameter data;

[0072] Step 3: Based on the preprocessed data, construct an electrochemical reaction kinetics model using a deep learning algorithm;

[0073] Step 4: Use the kinetics model for prediction and evaluate the model accuracy;

[0074] Step 5: Conduct data mining to identify the key parameters affecting the product yield;

[0075] Step 6: Optimize the reaction conditions and parameters based on the key parameters;

[0076] Step 7: Perform multi-sensor data fusion to improve the model robustness;

[0077] Step 8: Conduct inverse problem optimization to continuously adjust and optimize the model;

[0078] Step 9: Implement multi-time scale optimization to balance short-term and long-term benefits;

[0079] Step 10: Generate interpretable optimization suggestions and a visual report.

[0080] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0081] Firstly, the present invention realizes the comprehensive perception and accurate modeling of the electrochemical reaction process. Through the multi-sensor data fusion technology, the system can comprehensively capture various parameter changes in the reaction process. Combining with deep learning algorithms, especially the long short-term memory network (LSTM), the system can accurately establish the reaction kinetics model and effectively capture the non-linear and time-varying characteristics of the electrochemical reaction. This greatly improves the prediction accuracy of the model and lays a solid foundation for subsequent optimization control.

[0082] Secondly, the present invention adopts a multi-objective optimization method, which can take into account multiple objectives such as energy consumption and cost while improving the product yield. By introducing advanced multi-objective optimization algorithms such as NSGA-II, the system can find the best balance point among multiple objectives and achieve the comprehensive optimization of the electrochemical reaction process. This not only improves the production efficiency but also significantly reduces the energy consumption and production cost.

[0083] Furthermore, the present invention introduces reinforcement learning technology, especially the Deep Deterministic Policy Gradient (DDPG) algorithm, enabling the system to have the ability of autonomous learning and continuous optimization. This allows the control system to continuously adapt to changing reaction conditions and continuously improve control performance. Compared with traditional fixed control strategies, this adaptive learning method greatly improves the robustness and long-term performance of the system.

[0084] In addition, the present invention also innovatively introduces a multi-time scale optimization mechanism. Through a hierarchical reinforcement learning method, the system can simultaneously optimize objectives at different time scales, from millisecond-level current control to hour-level production optimization, achieving all-round and multi-scale optimization of the electrochemical reaction process. This multi-scale optimization method significantly improves the overall performance and adaptability of the system.

[0085] Finally, by introducing an interpretability analysis module, the present invention greatly improves the transparency and credibility of system decisions. Using advanced model interpretation methods such as SHAP values, the system can provide clear and intuitive decision explanations and optimization suggestions for operators. This not only enhances the credibility of the system but also provides valuable insights for further optimization.

[0086] Generally speaking, the intelligent optimization control system and method for the electrochemical reaction process proposed by the present invention achieve intelligent and refined control of the complex electrochemical reaction process through the organic combination and synergistic effect of multiple innovative modules. Such a system can not only significantly improve reaction efficiency and product quality, but also effectively reduce energy consumption and costs, while ensuring the interpretability and credibility of the control process. This provides a new technical path for the intelligent upgrading of the electrochemical industry and is expected to promote the entire industry to develop in a more efficient, more environmentally friendly, and more intelligent direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is a logical block diagram of the overall system of the present invention.

[0088] Figure 2 It is a logical block diagram of the data preprocessing module of the present invention.

[0089] Figure 3 It is a logical block diagram of the data modeling module of the present invention.

[0090] Figure 4 It is a logical block diagram of the data mining module of the present invention.

[0091] Figure 5 It is a logical block diagram of the control module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0092] Refer to Figures 1-5, The present invention relates to an intelligent optimization control system and method for the electro - chemical reaction process. The system aims to achieve the automated control and optimization of the electro - chemical reaction process through intelligent algorithms and data - driven methods, thereby improving reaction efficiency, product quality, and economic benefits.

[0093] Specifically, the intelligent optimization control system for the electro - chemical reaction process of the present invention includes a data acquisition module 1, a data pre - processing module 2, a data modeling module 3, a model evaluation module 4, a data mining module 5, and a control module 6. These modules cooperate organically to form a complete intelligent control closed - loop system.

[0094] The data acquisition module 1 is used to collect the characteristic parameter data of the electro - chemical reaction process. These characteristic parameters usually include voltage, current density, concentrations of reactants and products, reaction temperature, etc. Preferably, in an embodiment of the present invention, the data acquisition module 1 can also collect parameters such as pH value, pressure, stirring speed, etc., to comprehensively reflect the reaction state. The data acquisition frequency can be adjusted according to actual needs. For example, for rapidly changing parameters (such as voltage and current), a higher sampling frequency (such as 10 times per second) can be adopted, while for slower - changing parameters (such as temperature), a lower sampling frequency (such as 1 time per minute) can be used.

[0095] The data pre - processing module 2 is communicatively connected to the data acquisition module 1 and is used to receive the characteristic parameter data sent by the data acquisition module 1 and clean and pre - process this data. The main purpose of pre - processing is to improve data quality and provide reliable input for subsequent modeling. Specifically, the pre - processing process includes removing outliers and filling in missing values. In a preferred embodiment of the present invention, the determination of outliers can be based on statistical methods, such as using the 3σ principle, that is, data outside the range of the mean ± 3 times the standard deviation is regarded as an outlier. For missing values, various filling methods can be adopted, such as mean filling, median filling, or advanced filling methods based on machine learning (such as the K - nearest neighbor algorithm).

[0096] The data modeling module 3 is communicatively connected to the data pre - processing module 2 and is used to receive the pre - processed data and construct an electro - chemical reaction kinetics model based on this data using deep - learning algorithms. The advantage of using deep - learning algorithms in the present invention is that it can automatically learn complex non - linear relationships from a large amount of data without the need for manual feature design or prior assumption of the reaction mechanism. In an embodiment of the present invention, a long short - term memory network (LSTM) can be used to establish the kinetics model.

[0097] The model evaluation module 4 is communicatively connected to the data modeling module 3, and is used to receive the kinetic model sent by the data modeling module 3, and use this model to predict the reactant concentration and product concentration. Then, the model evaluation module 4 compares the prediction result with the actually measured concentration data to evaluate the accuracy of the kinetic model. In an embodiment of the present invention, the root mean square error (RMSE) can be used as an evaluation index:

[0098]

[0099] where y i represents the actually measured value, represents the model prediction value, and n represents the number of samples. Preferably, when the RMSE is less than a preset threshold (for example, 5% of the product concentration), the model is considered to have sufficient accuracy.

[0100] The data mining module 5 is communicatively connected to the model evaluation module 4, and is used to receive the evaluation results sent by the model evaluation module 4. Based on these results, the data mining module 5 mines the correlation between reaction characteristics and product yield from historical data, and identifies the key parameters affecting the product yield. In a preferred embodiment of the present invention, a feature importance analysis method based on random forest can be used to identify key parameters. The basic idea of this method is to evaluate the importance of a feature by randomly permuting the values of a certain feature and observing the degree of decline in model performance.

[0101] The control module 6 is communicatively connected to the data mining module 5, and is used to receive the key parameter information sent by the data mining module 5. Based on this information, the control module 6 adjusts the reaction conditions, optimizes the reaction parameters, and searches for the optimal combination of electrochemical reaction parameters through a trial-and-error method or a heuristic method. In an embodiment of the present invention, a particle swarm optimization algorithm (PSO) can be used to search for the optimal parameter combination. The basic update formula of the PSO algorithm is as follows:

[0102]

[0103] where and represent the velocity and position of the i-th particle at time t respectively, represents the historical best position of this particle, and g t represents the global best position, w, c1, and c2 are algorithm parameters, and r1 and r2 are random numbers between [0,1].

[0104] The data preprocessing module 2 of the present invention includes a data cleaning unit 21, an outlier processing unit 22, a feature extraction unit 23, and a normalization processing unit 24. The collaborative work of these units ensures the high quality of the data and lays a foundation for subsequent modeling and analysis.

[0105] The data cleaning unit 21 is used to remove useless data, such as parameters irrelevant to the reaction process. In practical applications, some redundant or irrelevant data may be collected. For example, in the process of producing hydrogen by electrolysis of water, parameters such as ambient humidity that are not directly related to the reaction may be mistakenly collected. The data cleaning unit 21 can remove these irrelevant data through pre-set rules or screening algorithms based on expert knowledge, thereby improving the efficiency and accuracy of subsequent processing.

[0106] The outlier processing unit 22 is used to detect and remove outliers through statistical methods and data visualization. The existence of outliers may seriously affect the accuracy of the model. In a preferred embodiment of the present invention, the box plot and Z-score method can be combined to detect outliers. Specifically, the Z-score of each feature can be calculated:

[0107]

[0108] Where X is the original data, μ is the mean, and σ is the standard deviation. When |Z|>3, the data point can be regarded as a potential outlier. Then, a box plot is used for visual verification to ensure that the judgment of the outlier is accurate.

[0109] The feature extraction unit 23 is used to extract useful features from the raw data using a deep learning algorithm or a statistical method. During the electrochemical reaction process, the raw data may contain a large amount of redundant information or features that are difficult to use directly for modeling. The purpose of feature extraction is to reduce the data dimension and extract the most representative features. In one embodiment of the present invention, the principal component analysis (PCA) method can be used for feature extraction. The mathematical principle of PCA is as follows: 1. Calculate the data covariance matrix: Where X is the centered data matrix and n is the number of samples. 2. Calculate the eigenvalues ​​and eigenvectors of the covariance matrix: Cv = λv, where λ is the eigenvalue and v is the corresponding eigenvector. 3. Select the eigenvectors corresponding to the first k largest eigenvalues ​​to form the projection matrix P. 4. Project the original data to the new feature space: Y = XP.

[0110] Through PCA, high-dimensional raw data can be reduced to a lower-dimensional feature space while retaining the main information of the data.

[0111] The normalization processing unit 24 is used to map the characteristic parameter value to a specific interval to eliminate the scale difference. In the electrochemical reaction process, the dimensions and value ranges of different parameters may vary greatly. For example, the voltage may be in the range of 0-10V, while the current density may be in the range of 0-1000A / m 2Within a certain range. Normalization can eliminate these differences, enabling different features to have the same weight in subsequent modeling. In a preferred embodiment of the present invention, the Min - Max normalization method can be used:

[0112]

[0113] where X is the original data, X min and X max are the minimum and maximum values of this feature respectively. Through this processing, all features are mapped into the interval [0, 1].

[0114] The deep - learning algorithms adopted by the data modeling module 3 of the present invention include a feature extraction neural network 31, a time - series model 32, a recurrent neural network 33, and a long short - term memory network 34. These different types of neural network models have their own characteristics, and appropriate models can be selected or combined according to the specific characteristics of the electrochemical reaction process.

[0115] The feature extraction neural network 31 is used to process the original data and extract useful features. In the electrochemical reaction process, the original data may contain a large amount of redundant information or features that are difficult to directly use for modeling. The feature extraction neural network can automatically learn the important features in the data without the need for manual design of feature extraction rules. In an embodiment of the present invention, a convolutional neural network (CNN) can be used as the feature extraction network. The basic structure of the CNN includes a convolutional layer, a pooling layer, and a fully - connected layer. The convolution operation can be expressed as:

[0116]

[0117] where f is the input signal and g is the convolution kernel. Through multiple layers of convolution and pooling operations, the CNN can automatically learn the local features and high - level features in the data.

[0118] The time - series model 32 is used to learn the kinetic process of the electrochemical reaction. The electrochemical reaction is usually a dynamic process, and the system state changes over time. The time - series model can capture this time - dependence, thereby more accurately describing the reaction kinetics. In a preferred embodiment of the present invention, an autoregressive integrated moving average (ARIMA) model can be used as the time - series model. The general form of the ARIMA model is:

[0119] φ(B)(1 - B) d X t =θ(B)ε t ,

[0120] where B is the lag operator, φ(B) and θ(B) are the autoregressive and moving average polynomials respectively, d is the order of differencing, ε tis a white noise sequence. By adjusting the model parameters, ARIMA can adapt to different types of time series data.

[0121] The recurrent neural network 33 is used to model the process of the electrochemical reaction. The characteristic of the RNN is that it can process sequence data and maintain an internal state, which makes it very suitable for modeling the electrochemical reaction process with time dependence. The basic structure of the RNN can be expressed as:

[0122] h t = f(W hh h t-1 + W xh x t + b h ),

[0123] where h t is the hidden state at time t, x t is the input, W xh and W xh are weight matrices, b h is the bias term, and f is the activation function.

[0124] The long short-term memory network 34 is used to learn the correlation between the reaction parameters and the product yield. The LSTM is a variant of the RNN, which can solve the problem of gradient vanishing in long sequence training, so it is particularly suitable for modeling long-term dependence relationships. The core of the LSTM is its cell structure, including an input gate, a forget gate, and an output gate, and its mathematical expressions have been given above.

[0125] Through the combined application of these deep learning algorithms, the data modeling module 3 of the present invention can comprehensively and accurately describe the kinetic characteristics of the electrochemical reaction process, providing a reliable model basis for subsequent optimization control.

[0126] The system of the present invention realizes the intelligent optimization control of the electrochemical reaction process through the collaborative work of the above modules. To further improve the performance and adaptability of the system, the present invention also includes some innovative functional modules and algorithms.

[0127] The data mining module 5 of the present invention includes an association rule analysis unit 51, a decision tree analysis unit 52, a feature importance evaluation unit 53, and an optimal condition identification unit 54. These units work together to extract valuable information from a large amount of reaction data, providing decision support for optimization control.

[0128] The association rule analysis unit 51 is used to analyze the association rules between the characteristic parameters and the product yield in the data. During the electrochemical reaction process, there may be complex interaction relationships between various parameters. Association rule analysis can help discover these hidden relationships. In a preferred embodiment of the present invention, the Apriori algorithm can be used for association rule mining. The core idea of this algorithm is to utilize the prior knowledge of frequent item sets, generate candidate item sets through an iterative manner, and calculate their support and confidence. Specifically, the calculation formulas for support and confidence are as follows:

[0129]

[0130] where A and B respectively represent different parameters or parameter combinations. By setting appropriate support and confidence thresholds (for example, support > 0.1, confidence > 0.8), strong correlation rules can be screened out, providing guidance for subsequent optimization control.

[0131] The decision tree analysis unit 52 is used to construct a decision tree model to identify the key factors affecting the product yield. The decision tree model has the characteristics of being intuitive and easy to interpret, and is very suitable for analyzing causal relationships in complex systems. In an embodiment of the present invention, the C4.5 algorithm can be used to construct a decision tree. This algorithm uses the information gain ratio as the criterion for feature selection, and its calculation formula is as follows:

[0132]

[0133] where Gain(A) is the information gain and Split Info(A) is the split information. By recursively selecting the best splitting feature, the C4.5 algorithm can construct a decision tree reflecting the importance of each parameter in the electrochemical reaction process.

[0134] The feature importance evaluation unit 53 is used to calculate the influence degree of each characteristic parameter on the product yield. During the electrochemical reaction process, the influence degrees of different parameters on the reaction result may have significant differences. Identifying the most critical parameters can help optimize the control strategy and improve the system efficiency. In a preferred embodiment of the present invention, a feature importance evaluation method based on random forest can be adopted. The basic idea of this method is to observe the degree of decline in model performance by randomly permuting the values of a certain feature to evaluate the importance of this feature. The specific calculation formula is as follows:

[0135]

[0136] where N tree is the number of trees in the random forest, is the out-of-bag error of the t-th tree, Out-of-bag error after random permutation as a feature. Through this method, a ranking reflecting the importance of each parameter can be obtained, providing an important reference for subsequent optimization control.

[0137] The optimal condition identification unit 54 is used to determine the optimal reaction conditions based on the data mining results. After obtaining various data analysis results, multiple factors need to be comprehensively considered to determine the optimal reaction conditions. In an embodiment of the present invention, a multi-objective optimization method can be used to achieve this goal. Specifically, the non-dominated sorting genetic algorithm II (NSGA-II) can be used to find the optimal conditions. The core idea of NSGA-II is to maintain the diversity of the population through non-dominated sorting and crowding distance calculation, while advancing towards the Pareto front. The main steps of the algorithm include:

[0138] 1. Initialize the population;

[0139] 2. Non-dominated sorting;

[0140] 3. Crowding distance calculation;

[0141] 4. Selection, crossover, and mutation;

[0142] 5. Combine the parent and offspring populations;

[0143] 6. Repeat steps 2-5 until the termination condition is met;

[0144] Through this method, a set of optimal reaction conditions can be found considering multiple objectives (such as product yield, energy consumption, cost, etc.).

[0145] The control module 6 of the present invention includes a parameter optimization unit 61, a multi-objective optimization unit 62, an online adaptive optimization unit 63, and a reinforcement learning unit 64. The collaborative work of these units ensures that the system can be dynamically adjusted according to real-time situations, realizing the intelligent optimization control of the electrochemical reaction process.

[0146] The parameter optimization unit 61 is used to search for the optimal reaction parameter combination using intelligent optimization algorithms, such as the Monte Carlo method or the particle swarm optimization algorithm. In the electrochemical reaction process, there are a large number of parameters that need to be adjusted, such as voltage, current density, temperature, etc. Finding the optimal combination of these parameters is a complex optimization problem. In a preferred embodiment of the present invention, an improved particle swarm optimization algorithm (IPSO) can be used to solve this problem. IPSO introduces an adaptive inertia weight and a convergence factor on the basis of the standard PSO, and its update formula is as follows:

[0147]

[0148] Among them, w(t) is the adaptive inertia weight, and c1(t) and c2(t) are the adaptive learning factors. This improvement can enhance the convergence speed of the algorithm and its global search ability, and find the optimal parameter combination more quickly.

[0149] The multi-objective optimization unit 62 is used to consider constraint conditions such as energy consumption and production cost while increasing the product yield. In the actual electrochemical production process, it is often necessary to balance multiple objectives, such as maximizing the product yield, minimizing the energy consumption, and reducing the production cost. In an embodiment of the present invention, a multi-objective evolutionary algorithm (MOEA / D) can be used to handle such problems. The core idea of MOEA / D is to decompose the multi-objective problem into a series of single-objective optimization problems and utilize neighborhood information to improve the algorithm efficiency. Specifically, the Chebyshev decomposition method can be used:

[0150]

[0151] where w is the weight vector and z * is the ideal point. Through this method, a set of Pareto optimal solutions can be found while considering multiple objectives, providing multiple optional solutions for decision-makers.

[0152] The online adaptive optimization unit 63 is used to dynamically adjust the control parameters according to the process data collected in real time. The electrochemical reaction process is often non-linear and time-varying, and a fixed control strategy is difficult to cope with the complex and changeable production environment. In a preferred embodiment of the present invention, model predictive control (MPC) combined with an adaptive mechanism can be used to achieve online optimization. The basic idea of MPC is to solve the optimization problem within a rolling time domain, and its objective function can be expressed as:

[0153]

[0154] where N p is the prediction time domain, N c is the control time domain, y is the system output, r is the reference trajectory, u is the control input, and Q and R are the weight matrices. By solving this optimization problem in each control period, MPC can generate an optimal control sequence according to the current system state and the prediction model.

[0155] The reinforcement learning unit 64 is used to continuously learn and optimize the control strategy through interaction with the environment. Reinforcement learning is a method that can autonomously learn the optimal strategy and is particularly suitable for dealing with complex control problems. In an embodiment of the present invention, the deep deterministic policy gradient (DDPG) algorithm can be used to implement reinforcement learning control. DDPG combines the advantages of the deep Q-network (DQN) and the deterministic policy gradient (DPG), and the update formulas of its actor network and critic network are respectively:

[0156]

[0157] Among them, θ μ and θ Q are the parameters of the actor network and the critic network respectively, and y i is the target Q value. By continuously interacting with the environment and updating the network parameters, DDPG can gradually learn the optimal control strategy.

[0158] The present invention further includes a multi-sensor data fusion module 7, which is communicatively connected to the data acquisition module 1. The introduction of this module greatly improves the data processing ability and information utilization efficiency of the system.

[0159] The multi-sensor data fusion module 7 is used to receive the data of multiple electrochemical sensors sent by the data acquisition module 1, perform fusion analysis on these data, and assign weights to each sensor to improve the robustness and accuracy of the model. In a complex electrochemical reaction system, multiple sensors often need to work together to comprehensively monitor the reaction state. However, the accuracy, reliability, and importance of different sensors may vary. In a preferred embodiment of the present invention, a data fusion method based on Dempster-Shafer evidence theory can be adopted. The core of this method is to calculate the basic probability assignment (BPA) of different sensor data, and then use the Dempster combination rule for fusion. The specific fusion formula is as follows:

[0160]

[0161] Among them, m(A) is the fused BPA, and m1 and m2 are the BPAs of two sensors respectively.. Through this method, the information of multiple sensors can be effectively integrated to improve the perception ability and decision-making accuracy of the system.

[0162] The data modeling module 3 of the present invention further includes an inverse problem optimization unit 35. The introduction of this unit greatly improves the adaptive ability and prediction accuracy of the model.

[0163] The inverse problem optimization unit 35 is used to construct an inverse problem model of the electro-chemical reaction kinetics model. By combining the data mining results with the error of the actual reaction process, it adjusts the model parameters, continuously optimizes the model, and improves the prediction accuracy. In the modeling of the electro-chemical reaction process, the direct problem is to solve the system response with known model parameters, while the inverse problem is to estimate the model parameters based on the observed system response. In an embodiment of the present invention, the Bayesian inference method can be used to solve this inverse problem. Specifically, the Markov Chain Monte Carlo (MCMC) algorithm can be used to estimate the posterior distribution of the model parameters. The core idea of MCMC is to construct a Markov chain whose stationary distribution is the desired posterior distribution. The metropolis-Hastings algorithm is a commonly used implementation of MCMC, and its acceptance probability calculation formula is as follows:

[0164]

[0165] where p(θ|D) is the posterior probability of parameter θ, and q(θ′|θ) is the proposal distribution. Through this method, the model parameters can be continuously optimized to make the model better fit the actual reaction process.

[0166] Through the collaborative work of the above-mentioned modules and units, the intelligent optimization control system for the electro-chemical reaction process of the present invention can comprehensively and accurately describe and optimize the complex electro-chemical reaction process, providing strong technical support for improving reaction efficiency, product quality and economic benefits. The intelligent optimization control system for the electro-chemical reaction process of the present invention has realized the comprehensive monitoring and optimization of the reaction process through the collaborative work of the foregoing modules and units. However, in order to further improve the performance and adaptability of the system, the present invention also introduces some innovative functions and algorithms.

[0167] The control module 6 of the present invention further includes a multi-time scale optimization unit 65. The introduction of this unit enables the system to optimize at different time scales, thereby better balancing short-term benefits and long-term performance.

[0168] The multi-time scale optimization unit 65 is used to consider the optimization objectives at different time scales, including real-time control and long-term optimization, coordinate the balance between short-term benefits and long-term performance, and adjust the control strategy according to the optimization results at different time scales. In the actual electro-chemical production process, there are often objectives at multiple time scales, such as millisecond-level current control, minute-level temperature regulation, hour-level output optimization, etc. In a preferred embodiment of the present invention, the hierarchical reinforcement learning (HRL) method can be used to handle this multi-time scale optimization problem.

[0169] The core idea of HRL is to decompose complex long-term tasks into multiple levels of subtasks, with each level responsible for decision-making at different time scales. Specifically, the Options framework can be used to implement HRL. In the Options framework, an option o consists of three parts: the initial set I o , the termination condition β o , and the internal policy π o . The high-level policy is responsible for selecting options, and the low-level policy is responsible for executing specific actions. Its value function can be expressed as:

[0170]

[0171] where Ω represents the set of options, γ is the discount factor, and k is the execution time of option o. Through this hierarchical structure, the system can optimize goals at different time scales simultaneously, achieving more flexible and efficient control.

[0172] The present invention also includes an interpretability analysis module 8, which is communicatively connected to the data mining module 5 and the control module 6. The introduction of this module greatly improves the transparency and credibility of system decision-making.

[0173] The interpretability analysis module 8 is used to receive the key parameter information sent by the data mining module 5 and the optimization strategy information sent by the control module 6, analyze the model decision-making process, provide interpretable optimization suggestions, and generate a visual report reflecting process optimization. In a complex electrochemical reaction system, relying solely on a black-box model for decision-making may pose risks, so a mechanism is needed to explain the model's decision-making process. In one embodiment of the present invention, the SHAP (SHapley Additive exPlanations) value method can be used to achieve the interpretability analysis of the model.

[0174] The SHAP value is based on the Shapley value concept in game theory and is used to explain the contribution of each feature to the model prediction. For a feature i, its SHAP value can be expressed as:

[0175]

[0176] where F is the set of all features, and f S is the model that only uses the feature subset S. By calculating the SHAP value of each feature, an intuitive explanation can be obtained to show how each parameter affects the final decision.

[0177] In addition, the interpretability analysis module 8 can also generate visual reports, such as SHAP summary plots, SHAP dependence plots, etc., to help operators better understand the system's decision-making process. This not only improves the credibility of the system but also provides valuable insights for further optimization.

[0178] Finally, the present invention also proposes an optimization control method based on the above intelligent optimization control system for the electro-chemical reaction process. This method systematically integrates the functions of the foregoing various modules and units to form a complete optimization control process.

[0179] Specifically, this method includes the following steps:

[0180] Step 1: Collect characteristic parameter data of the electro-chemical reaction process. This step is completed by the data acquisition module 1, collecting various parameter data including voltage, current density, concentrations of reactants and products, reaction temperature, etc.

[0181] Step 2: Preprocess the characteristic parameter data. This step is completed by the data preprocessing module 2, including operations such as data cleaning, outlier processing, feature extraction, and normalization.

[0182] Step 3: Based on the preprocessed data, construct an electro-chemical reaction kinetics model using a deep learning algorithm. This step is completed by the data modeling module 3, and deep learning models such as LSTM can be used to capture the dynamic characteristics of the reaction process.

[0183] Step 4: Use the kinetics model for prediction and evaluate the model accuracy. This step is completed by the model evaluation module 4, and the performance of the model is evaluated by comparing the model prediction results with the actual measurement data.

[0184] Step 5: Conduct data mining to identify the key parameters affecting the product yield. This step is completed by the data mining module 5, and methods such as association rule analysis and decision tree analysis can be used to mine valuable information.

[0185] Step 6: Optimize the reaction conditions and parameters based on the key parameters. This step is completed by the control module 6, and algorithms such as particle swarm optimization and multi-objective optimization can be used to find the optimal reaction conditions.

[0186] Step 7: Perform multi-sensor data fusion to improve the robustness of the model. This step is completed by the multi-sensor data fusion module 7, and the data of multiple sensors are fused to improve the system's perception ability and decision-making accuracy.

[0187] Step 8: Conduct inverse problem optimization to continuously adjust and optimize the model. This step is completed by the inverse problem optimization unit 35 in the data modeling module 3, and the model parameters are optimized through inverse problem solving to improve the prediction accuracy of the model.

[0188] Step Nine: Implement multi-time-scale optimization to balance short-term and long-term benefits. This step is completed by the multi-time-scale optimization unit 65 in the control module 6, and methods such as hierarchical reinforcement learning are used to coordinate the optimization goals at different time scales.

[0189] Step Ten: Generate interpretable optimization suggestions and visualization reports. This step is completed by the interpretability analysis module 8, and methods such as SHAP value analysis are used to explain the decision-making process of the model and generate intuitive visualization reports.

[0190] Through the organic combination of these ten steps, the optimization control method of the present invention can comprehensively and systematically optimize the electrochemical reaction process, significantly improving the reaction efficiency, product quality, and economic benefits. It should be noted that these steps are not a strictly linear sequence but a closed-loop optimization process. The system will continuously execute these steps to continuously optimize the reaction process.

[0191] Preferably, in practical applications, the execution order and specific implementation methods of each step can be flexibly adjusted according to the specific type of electrochemical reaction and production requirements. For example, for rapidly changing parameters, the frequency of data collection and preprocessing can be increased; for relatively stable large-scale production systems, the weight of long-term optimization can be increased.

[0192] In addition, the method of the present invention can also be combined with other advanced process control technologies, such as fuzzy control, adaptive control, etc., to further improve the performance and adaptability of the system. For example, a fuzzy logic controller can be introduced into the control module to handle uncertainties and nonlinear problems in the system.

[0193] Generally speaking, the intelligent optimization control system and method for electrochemical reaction processes proposed by the present invention, through the comprehensive application of advanced algorithms such as deep learning, multi-objective optimization, and reinforcement learning, realize the intelligent control and optimization of complex electrochemical reaction processes. This not only greatly improves production efficiency and product quality but also provides a new technical path for the intelligent upgrading of the electrochemical industry.

[0194] In use, first, in terms of material selection, the special requirements of the electrochemical reaction and material properties are fully considered. For the electrode materials, noble metals such as platinum, titanium, ruthenium or their alloys with excellent corrosion resistance and electrical conductivity are selected as the anode to ensure stable and efficient electron transfer during the reaction; while carbon-based materials, copper, nickel, etc. with good reduction performance and low cost are selected as the cathode to achieve a balance between economy and performance. The electrolyte is selected based on the reaction requirements, and acid, alkali, salt solutions or ionic liquids are used. These electrolytes have good ionic conductivity and chemical stability, providing an ideal medium environment for the electrochemical reaction. In addition, the diaphragm materials such as ion exchange membranes and porous ceramic membranes with good ion selectivity, mechanical strength and chemical stability are used to effectively separate the anode and the cathode, prevent the reactants from directly contacting, and at the same time allow ions to pass through freely. The reactor body is made of stainless steel, titanium alloy or special plastics with corrosion resistance, high temperature resistance and high mechanical strength to ensure the long-term stable operation of the reactor in a harsh chemical reaction environment.

[0195] In terms of system composition, the electrochemical reactor, as the core component, integrates electrodes, electrolytes, diaphragms and the reactor body, realizing the efficient progress of the electrochemical reaction and the stable generation of target products. The power supply system provides a stable and adjustable DC power supply to meet the precise voltage and current control required for the electrochemical reaction. The control system includes data acquisition, preprocessing, modeling, evaluation, mining and control modules. These modules cooperate organically to form a complete intelligent control closed-loop system, realizing the automatic control and optimization of the electrochemical reaction process. The auxiliary system includes a cooling system, a stirring system, a gas supply system, etc., ensuring the stability of the reaction conditions and the improvement of the reaction efficiency.

[0196] In terms of the advantages of market application, this electrochemical reaction system shows remarkable features. First, through intelligent optimization control, the system realizes the efficient operation of the electrochemical reaction process, significantly reduces energy consumption, increases the product yield, and achieves the goal of high efficiency and energy saving. Second, the high degree of automation of the system reduces manual intervention, improves production efficiency, and reduces operating costs. In addition, the system has strong scalability and can be flexibly customized and expanded according to different electrochemical reaction requirements, adapting to diverse market application needs. In terms of environmental friendliness, the system uses environmentally friendly materials and processes, reduces pollutant emissions, and meets the requirements of sustainable development. Finally, the system introduces an interpretability analysis module, providing intuitive optimization suggestions and visual reports, enhancing the credibility of the system, and providing better decision-making support for operators.

[0197] It should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. Intelligent optimization control system for electrochemical reaction process, characterized in that: include: Data acquisition module for: Collect characteristic parameter data of the electrochemical reaction process, including voltage, current density, concentration of reactants and products, and reaction temperature; A data preprocessing module is connected to the data acquisition module for: Receiving characteristic parameter data sent by the data acquisition module; Cleaning and preprocessing the characteristic parameter data, including removing outliers and filling missing values; A data modeling module is communicatively connected with the data preprocessing module and is used to: Receiving the preprocessed data sent by the data preprocessing module; Based on the preprocessed data, a deep learning algorithm is used to construct an electrochemical reaction kinetics model; A model evaluation module, in communication with the data modeling module, is used to: Receiving the dynamic model sent by the data modeling module; Predicting reactant concentrations and product concentrations using the kinetic model; Comparing the predicted results with the actual measured concentration data to evaluate the accuracy of the kinetic model; A data mining module is connected in communication with the model evaluation module and is used to: Receiving the evaluation result sent by the model evaluation module; Based on the evaluation results, mining the correlation between reaction characteristics and product yield from historical data; Identify key parameters that affect product yield; A control module, in communication with the data mining module, is used to: Receiving key parameter information sent by the data mining module; Adjusting reaction conditions based on the key parameter information; Optimize reaction parameters and find the optimal combination of electrochemical reaction parameters through trial and error or heuristic methods.

2. The electrochemical reaction process intelligent optimization control system according to claim 1 is characterized in that: The data preprocessing module comprises: A data cleaning unit is used to remove useless data, such as parameters irrelevant to the reaction process; An outlier processing unit, used to detect and remove outliers through statistical methods and data visualization; A feature extraction unit is used to extract useful features from raw data using a deep learning algorithm or a statistical method; The normalization processing unit is used to map the feature parameter values ​​to a specific interval to eliminate scale differences.

3. The electrochemical reaction process intelligent optimization control system according to claim 1, characterized in that: The deep learning algorithms used in the data modeling module include: Feature extraction neural network, used to process raw data and extract useful features; Time series models, used to study the kinetics of electrochemical reactions; Recurrent neural networks, used to model the process of electrochemical reactions; Long short-term memory network is used to learn the relationship between reaction parameters and product yield.

4. The electrochemical reaction process intelligent optimization control system according to claim 1, characterized in that: The data mining module includes: An association rule analysis unit, used to analyze the association rules between characteristic parameters and product yields in the data; A decision tree analysis unit is used to construct a decision tree model and identify key factors affecting product yield; A characteristic importance evaluation unit is used to calculate the influence of each characteristic parameter on the product yield; The optimal condition identification unit is used to determine the optimal reaction condition based on the data mining result.

5. The electrochemical reaction process intelligent optimization control system according to claim 1, characterized in that: The control module comprises: A parameter optimization unit for searching for an optimal reaction parameter combination using an intelligent optimization algorithm, such as a Monte Carlo method or a particle swarm optimization algorithm; Multi-objective optimization unit, used to improve product yield while taking into account constraints such as energy consumption and production cost; Online adaptive optimization unit, used to dynamically adjust control parameters based on real-time collected process data; Reinforcement learning unit, used to continuously learn and optimize control strategies through interaction with the environment.

6. The electrochemical reaction process intelligent optimization control system according to claim 1, characterized in that: Also includes: A multi-sensor data fusion module is communicatively connected with the data acquisition module and is used for: Receiving data of multiple electrochemical sensors sent by the data acquisition module; Performing fusion analysis on the data of the multiple electrochemical sensors; Assign weights to each sensor to improve the robustness and accuracy of the model.

7. The electrochemical reaction process intelligent optimization control system according to claim 1, characterized in that: The data modeling module also includes: Inverse Optimization Unit for: Construct inverse problem models of electrochemical reaction kinetics models; Combine the data mining results with the errors of the actual reaction process to adjust the model parameters; Continuously optimize the model to improve prediction accuracy.

8. The electrochemical reaction process intelligent optimization control system according to claim 1, characterized in that: The control module also includes: Multi-timescale optimization unit for: Consider optimization objectives at different time scales, including real-time control and long-term optimization; Balance short-term benefits and long-term performance; Adjust the control strategy according to the optimization results at different time scales.

9. The electrochemical reaction process intelligent optimization control system according to claim 1, characterized in that: Also includes: An interpretability analysis module, which is in communication with the data mining module and the control module, is used to: Receiving key parameter information sent by the data mining module; Receiving optimization strategy information sent by the control module; Analyze the model decision-making process and provide explainable optimization suggestions; Generate visual reports of reaction process optimization.

10. An optimization control method for an intelligent optimization control system for an electrochemical reaction process according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect characteristic parameter data of the electrochemical reaction process; Step 2: preprocessing the characteristic parameter data; Step 3: Based on the preprocessed data, a deep learning algorithm is used to construct an electrochemical reaction kinetic model; Step 4: using the kinetic model to make predictions and evaluate the accuracy of the model; Step 5: Perform data mining to identify key parameters that affect product yield; Step 6: Optimizing reaction conditions and parameters based on the key parameters; Step 7: Perform multi-sensor data fusion to improve model robustness; Step 8: Perform inverse problem optimization and continuously adjust and optimize the model; Step 9: Implement multi-time scale optimization to balance short-term and long-term benefits; Step 10: Generate explainable optimization recommendations and visualization reports.

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