Electrocatalytic device for breaking down complexes in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics

Abstract

This invention relates to the field of electrocatalytic oxidation devices, and discloses an electrocatalytic device for breaking down complexes in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics. The device includes an oxidation tank and a data acquisition module that collects real-time information on the catalytic solution, including its composition and state data. A catalytic module processes the catalytic solution, using electrocatalytic reactions to break down complexes. A central control module analyzes the complex composition of the catalyst using convolutional neural networks (CNN) and recurrent neural networks (RNN), and dynamically adjusts the electrocatalytic parameters based on a fuzzy control algorithm combined with historical data and real-time information to optimize the catalytic effect. An early warning module monitors changes in the catalytic solution and electrocatalytic parameters, compares them with preset thresholds to determine whether an early warning is needed, and takes appropriate measures based on the risk level. This invention, through real-time data analysis and intelligent control, helps improve the accuracy and efficiency of the catalytic process, ensuring the safety and stability of the treatment process.

Description

Technical Field

[0001] This invention relates to the field of electrocatalytic oxidation device technology, and more specifically, to an electrocatalytic device for breaking down the complexes in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics. Background Technology

[0002] With the advancement of industrialization, the electroplating industry generates a large amount of rinsing wastewater during production, containing significant amounts of metal ions and chemicals. If this wastewater is not effectively treated and discharged into the environment, it will cause serious pollution to water bodies and ecosystems. Therefore, the treatment of electroplating rinsing wastewater has become an urgent environmental problem to be solved.

[0003] Currently, common treatment methods include chemical precipitation, electrolysis, and membrane separation. However, these traditional methods suffer from low efficiency, high cost, and difficulty in handling complex wastewater compositions, necessitating a more efficient and intelligent treatment solution. Furthermore, traditional electrocatalytic complex-breaking technology faces challenges such as low catalytic efficiency and unstable catalytic processes. In particular, catalyst optimization during treatment often relies on manual experience, lacking intelligent adjustment methods for different complex types and concentrations. This prevents the complex-breaking effect of electroplating rinsing wastewater from being fully improved.

[0004] Therefore, there is an urgent need to invent a catalytic device to solve the problems of low efficiency, high cost, unstable catalytic process, and lack of intelligent adjustment means in traditional electroplating rinsing wastewater treatment methods, which result in insufficient improvement of complex breaking effect. Summary of the Invention

[0005] In view of this, the present invention proposes an electrocatalytic device for breaking down complexes in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics, which aims to solve the problems of low efficiency, high cost, unstable catalytic process, and lack of intelligent adjustment means in the current traditional electroplating rinsing wastewater treatment methods, which result in insufficient improvement of complex breaking effect.

[0006] This invention proposes an electrocatalytic device for breaking down complexes in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics, comprising an oxidation tank, and further comprising:

[0007] The acquisition module is located inside the oxidation tank and is configured to collect information about the catalytic solution inside the oxidation tank.

[0008] A catalytic module is configured inside the oxidation tank to catalyze the catalytic liquid inside the oxidation tank.

[0009] The central control module is electrically connected to the acquisition module and the catalytic module respectively. The central control module is configured to analyze the complex composition of the catalyst based on convolutional neural network and recurrent neural network. The central control module is also configured to determine the electrocatalytic parameters of the catalytic module based on fuzzy control algorithm, combined with historical data, the complex composition of the catalyst, and real-time catalytic liquid information.

[0010] The early warning module is electrically connected to both the catalytic module and the central control module. The early warning module is configured to determine whether to issue an early warning based on the relationship between the catalytic liquid information and the electrocatalytic parameters.

[0011] Furthermore, the electrode material of the catalytic module is specifically titanium-based nano-conductive ceramic.

[0012] Furthermore, the data acquisition module includes:

[0013] A spectral sensor is configured to monitor the wastewater composition of the catalytic solution in real time;

[0014] An ultrasonic sensor is configured to detect the flow rate of the catalyst solution in real time;

[0015] An electrochemical sensor is configured to detect the current density and pH value of the catalytic solution in real time.

[0016] Furthermore, when the central control module analyzes the complex composition of the catalyst based on convolutional neural networks and recurrent neural networks, it includes:

[0017] The central control module is also configured to extract features from spectral signals and electrochemical data based on convolutional neural networks, and to extract the spectral absorption features and electrochemical response features of complexes based on convolutional layers;

[0018] The central control module is also configured to reduce data dimensionality based on the pooling layer and extract complex composition information of the catalyst.

[0019] The central control module is also configured to analyze the time series data of complex concentration based on a recurrent neural network, and to determine the dynamic trend of the composition of the complex to be catalyzed based on a long short-term memory network.

[0020] Furthermore, the central control module extracts features from spectral signals and electrochemical data based on convolutional neural networks, and when extracting the spectral absorption features and electrochemical response features of the complex based on the convolutional layers, it includes:

[0021] The central control module is also configured to denoise the spectral signal based on a filtering algorithm and perform baseline correction on the electrochemical data;

[0022] The central control module is also configured to scan the spectral signal based on the convolutional layer and extract spectral absorption features, including absorption peak position and absorbance intensity. The central control module is also configured to perform convolution operations on electrochemical data based on the convolutional layer and extract the change patterns of current and potential.

[0023] The central control module is also configured to perform dimensionality reduction processing on spectral and electrochemical features based on pooling layers;

[0024] The central control module is also configured to form a complete feature vector of the complex based on the fusion of spectral and electrochemical features of the fully connected layer.

[0025] Furthermore, the central control module analyzes the time-series data of the complex concentration based on a recurrent neural network, and determines the dynamic trend of the composition of the catalytic complex based on a long short-term memory network, including:

[0026] The central control module is also configured to acquire time-series data of complex concentration in real time and to acquire concentration changes at different catalytic stages;

[0027] The central control module is also configured to perform noise reduction and normalization on the concentration data based on a data preprocessing algorithm;

[0028] The central control module is also configured to extract time-series features of concentration data based on LSTM network, identify the concentration change pattern over time, and combine an attention mechanism to increase the weight of concentration changes at key time points.

[0029] The central control module is also configured to predict the future trend of complex concentration based on regression analysis and dynamically determine catalytic parameters based on the prediction results.

[0030] Furthermore, the central control module is also configured to determine the electrocatalytic parameters of the catalytic module based on fuzzy control algorithms, combined with historical data, the complex composition of the catalytic compound, and real-time catalytic liquid information, including:

[0031] The central control module is also configured to perform fuzzification processing on variables such as complex concentration, catalytic current, and catalytic time based on fuzzy logic, and map each variable to a fuzzy set based on the membership function;

[0032] The central control module is also configured to combine a fuzzy rule base to reason about catalytic parameters based on preset rules, and to determine the catalytic parameters of complex composition based on fuzzy reasoning;

[0033] The central control module is also configured to model historical catalytic data based on time series analysis and optimize real-time parameter calculations by combining Bayesian inference or Kalman filtering.

[0034] The central control module is also configured to calculate and determine the electrocatalytic parameters of the catalytic module based on real-time parameters.

[0035] Furthermore, when the central control module combines the fuzzy rule base to infer catalytic parameters based on preset rules, and determines the catalytic parameters of the complex composition based on fuzzy inference, it includes:

[0036] The central control module is also configured to construct a mapping relationship between complex components and catalytic parameters based on historical data, so as to form fuzzy rules for catalytic current, catalytic time, and pH value;

[0037] The central control module is also configured to map complex concentration variables, current density variables, and redox potential variables to fuzzy sets;

[0038] The central control module is also configured to perform rule-based reasoning based on the Mamdani reasoning method to determine the fuzzy output of catalytic current and catalytic time;

[0039] The central control module is also configured to use the maximum membership degree method to convert fuzzy inference results into specific numerical values;

[0040] The central control module is also configured to optimize specific values ​​based on historical data and real-time catalyst information, and to use the optimized values ​​as the execution parameters of the catalyst module.

[0041] Furthermore, the early warning module determines whether to issue an early warning based on the relationship between catalytic liquid information and electrocatalytic parameters, including:

[0042] The early warning module is also configured to determine whether to issue an early warning based on the relationship between the execution parameters and the preset execution parameters configured in the catalytic module.

[0043] When the execution parameters are lower than the preset execution parameters, the warning module determines not to issue a warning.

[0044] When the execution parameter is higher than or equal to the preset execution parameter, the early warning module will issue an early warning and determine the early warning level based on the relationship between the execution parameter and the preset execution parameter.

[0045] Furthermore, when determining the warning level based on the relationship between the execution parameters and the preset execution parameters, the early warning module includes:

[0046] The early warning module is also configured to obtain the parameter difference between the execution parameters and the preset execution parameters, and to determine the early warning level based on the relationship between the parameter difference and the first preset parameter difference and the second preset parameter difference configured by the early warning module;

[0047] When the parameter difference is lower than or equal to the first preset parameter difference, the warning level is determined to be a low-risk level.

[0048] When the parameter difference is higher than the first preset parameter difference and lower than or equal to the second preset parameter difference, the warning level is determined to be a medium risk level.

[0049] When the parameter difference is higher than the second preset parameter difference, the warning level is determined to be a high-risk level.

[0050] Among them, the difference between the first preset parameter and the difference between the second preset parameter are less than the difference between the two preset parameters, and the severity of the warning levels are low risk level, medium risk level and high risk level in sequence.

[0051] Compared with existing technologies, the advantages of this invention are as follows: By combining convolutional neural networks (CNN) and recurrent neural networks (RNN) for intelligent analysis of the complex composition of the catalyst, the accuracy and intelligence level of the electrocatalytic device for breaking complexes in electroplating rinsing wastewater are significantly improved. The central control module accurately identifies the complex composition based on deep learning algorithms and dynamically optimizes electrocatalytic parameters (such as current density and catalytic time) by combining fuzzy control algorithms, historical data, and real-time catalytic liquid information. This achieves adaptive adjustment of the catalytic process, thereby significantly improving catalytic efficiency and system stability. Through this intelligent control mechanism, the device can respond to changes in wastewater composition in real time, avoiding the problems of catalytic instability or low efficiency caused by insufficient experience in traditional methods. Furthermore, the introduction of an early warning module allows abnormal situations in the catalytic process (such as changes in catalytic liquid composition or deviations of catalytic parameters from preset ranges) to be monitored and warned in a timely manner, preventing over-catalysis or catalytic failure, thus ensuring the consistency and stability of the complex-breaking effect. Through intelligent parameter optimization and real-time feedback control, the entire treatment process is not only more efficient and accurate but also reduces the need for human intervention, lowering operational risks and costs. Attached Figure Description

[0052] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0053] Figure 1 This is a functional block diagram of an electrocatalytic device for breaking down the complex in electroplating rinsing wastewater, based on titanium-based nano-conductive ceramics, provided as an embodiment of the present invention. Detailed Implementation

[0054] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0055] like Figure 1 As shown in some embodiments of this application, this embodiment provides an electrocatalytic device for breaking down the complexes in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics, including: an oxidation tank, a collection module, a catalytic module, a central control module, and an early warning module.

[0056] Specifically, the acquisition module is configured inside the oxidation tank to collect information about the catalytic liquid inside the oxidation tank; the catalytic module is configured inside the oxidation tank to catalyze the catalytic liquid inside the oxidation tank; the central control module is electrically connected to both the acquisition module and the catalytic module, and is configured to analyze the complex composition of the catalyst based on convolutional neural networks and recurrent neural networks. The central control module is also configured to determine the electrocatalytic parameters of the catalytic module based on fuzzy control algorithms, combined with historical data, the complex composition of the catalyst, and real-time catalytic liquid information; the early warning module is electrically connected to both the catalytic module and the central control module, and is configured to determine whether to issue an early warning based on the relationship between the catalytic liquid information and the electrocatalytic parameters.

[0057] Understandably, the synergistic effect of the acquisition and catalytic modules, by acquiring real-time information about the catalytic liquid within the oxidation tank and real-time data during the catalytic process, provides comprehensive environmental data support to the central control module. The central control module, through a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN), performs in-depth analysis of the complex composition of the catalyst, automatically identifying the characteristics of different complexes using deep learning technology, and achieving more precise catalytic process control based on these characteristics. Secondly, the central control module incorporates a fuzzy control algorithm, combining historical data, real-time catalytic liquid information, and complex composition to dynamically adjust catalytic parameters, such as current density and catalytic time. The fuzzy control algorithm, by handling fuzzy and uncertain information, can better cope with complex catalytic processes, allowing catalytic parameters to be adaptively optimized according to different wastewater components. This dynamic adjustment mechanism significantly improves catalytic efficiency and ensures the stability of the complex-breaking effect. Finally, the introduction of an early warning module further enhances the system's intelligence and safety. By monitoring the relationship between catalytic liquid information and electrocatalytic parameters, the early warning module can promptly identify abnormalities in the catalytic process and issue warning signals. This can prevent over-catalysis or catalytic failure during the catalytic process, ensuring the stability and efficiency of the catalytic process, and ultimately achieving efficient complex breaking and intelligent treatment of electroplating rinsing wastewater.

[0058] It can be seen that through the synergistic effect of the acquisition module and the catalytic module, the information of the catalytic liquid inside the oxidation tank can be monitored in real time, providing necessary real-time data support for the central control module. The central control module uses convolutional neural networks (CNN) and recurrent neural networks (RNN) to analyze the complex composition of the catalyst, and can accurately identify the types and characteristics of different complexes. This intelligent analysis process enables the system to dynamically optimize electrocatalytic parameters based on different complex compositions, thereby achieving more efficient catalytic reactions and more precise complex-breaking effects. Secondly, by combining the application of fuzzy control algorithms, the central control module, historical data, real-time catalytic liquid information, and complex composition are integrated to make the adjustment of the catalytic process more intelligent and adaptive. Fuzzy control algorithms can handle the uncertainties and complexities in the catalytic process, enabling real-time optimization of catalytic parameters (such as current density, catalytic time, etc.). This adaptive control can significantly improve catalytic efficiency, reduce manual intervention, and ensure the stability and consistency of the complex-breaking effect of electroplating rinsing wastewater. Finally, the introduction of the early warning module enhances the safety and stability of the system. By monitoring the relationship between catalytic liquid information and electrocatalytic parameters, the early warning module can promptly identify potential anomalies during the catalytic process, such as changes in catalytic liquid composition or deviations in electrocatalytic parameters from preset ranges. When the system detects a potential anomaly, the early warning module issues an alarm, thereby preventing over-catalysis or catalytic failure and ensuring that the catalytic process is always in optimal condition. This efficient and intelligent treatment method ensures the high efficiency, stability, and safety of wastewater treatment.

[0059] Specifically, the electrode material of the catalytic module is titanium-based nano-conductive ceramic.

[0060] As can be seen, the electrode material of the catalytic module is titanium-based nano-conductive ceramic, a material choice that effectively improves conductivity and catalytic efficiency during the catalytic process. Titanium-based nano-conductive ceramic possesses excellent conductivity and chemical stability, enabling it to provide higher current density in electrocatalytic reactions while maintaining high stability at high currents. This characteristic makes the catalytic process more efficient, achieving effective complex breaking down of electroplating rinsing wastewater in a shorter time, reducing treatment time and energy consumption. Furthermore, the corrosion resistance and oxidation resistance of titanium-based nano-conductive ceramic materials give the catalytic module a longer service life in complex electroplating rinsing wastewater environments. Compared to traditional electrode materials, titanium-based nano-conductive ceramic can better withstand high concentrations of corrosive substances while avoiding rapid electrode wear during catalysis. This not only improves the long-term stability of the system but also reduces the cost of maintenance and electrode replacement, further enhancing the economics of the wastewater treatment system. Finally, the nanostructured surface of titanium-based nano-conductive ceramic provides more reactive sites, which has a significant impact on the efficiency of the catalytic reaction. The nanoscale surface structure can accelerate electrochemical reactions, increase reaction rates, and enhance the affinity and catalytic activity of the catalyst. This enables the catalytic module to quickly and effectively decompose complex complexes when treating electroplating rinsing wastewater, significantly improving the complex-breaking effect and ultimately achieving efficient wastewater treatment.

[0061] Specifically, the acquisition module includes: a spectral sensor configured to monitor the wastewater composition of the catalytic solution in real time; an ultrasonic sensor configured to detect the flow rate of the catalytic solution in real time; and an electrochemical sensor configured to detect the current density and pH value of the catalytic solution in real time.

[0062] Understandably, the acquisition module uses a spectral sensor to monitor the wastewater composition of the catalytic solution in real time, effectively detecting changes in the concentration of various chemical components and pollutants in the wastewater. The spectral sensor can quickly identify harmful substances in the catalytic solution by analyzing the absorption or reflection characteristics of light, providing crucial real-time data support for subsequent catalytic processes. Real-time monitoring of these components allows for timely adjustments to the catalytic strategy during wastewater treatment, ensuring maximum complex-breaking effect. Secondly, an ultrasonic sensor is configured to detect the flow rate of the catalytic solution in real time, effectively monitoring its flow state. Flow rate significantly impacts the efficiency of the catalytic process; excessively low flow rates may prevent reactants from fully contacting the electrodes, while excessively high flow rates may lead to insufficient catalyst reaction. By monitoring the flow rate in real time, the acquisition module ensures the catalytic solution flows through the catalytic module at the optimal speed, thereby optimizing the electrocatalytic reaction and improving treatment efficiency. Finally, an electrochemical sensor is used to detect the current density and pH value of the catalytic solution in real time; these two parameters are crucial for the electrocatalytic reaction. Current density determines the intensity and rate of the electrocatalytic reaction, while pH value affects catalyst activity and the stability of the catalytic process. By monitoring these two key parameters in real time, the acquisition module can promptly detect potential deviations in the catalytic process, such as excessively low current density or abnormal pH value. This provides data support to the central control module, enabling rapid adjustments to the catalytic process and maintaining the high efficiency and stability of the catalytic reaction.

[0063] Specifically, when the central control module analyzes the complex composition of the catalyst based on convolutional neural networks and recurrent neural networks, it includes: the central control module is also configured to extract features from spectral signals and electrochemical data based on convolutional neural networks, and extract the spectral absorption features and electrochemical response features of the complex based on convolutional layers; the central control module is also configured to reduce the data dimensionality based on pooling layers and extract the complex composition information of the catalyst; the central control module is also configured to analyze the time series data of the complex concentration based on recurrent neural networks, and determine the dynamic change trend of the complex composition of the catalyst based on long short-term memory networks.

[0064] Specifically, when the central control module extracts features from spectral signals and electrochemical data based on convolutional neural networks, and extracts spectral absorption features and electrochemical response features of the complex based on convolutional layers, the following steps are included: the central control module is also configured to denoise the spectral signals based on filtering algorithms and perform baseline correction on the electrochemical data; the central control module is also configured to scan the spectral signals based on convolutional layers to extract spectral absorption features, including absorption peak positions and absorbance intensity; the central control module is also configured to perform convolution operations on the electrochemical data based on convolutional layers to extract current and potential change patterns; the central control module is also configured to perform dimensionality reduction processing on spectral features and electrochemical features based on pooling layers; and the central control module is also configured to fuse spectral features and electrochemical features based on fully connected layers to form a complete feature vector of the complex.

[0065] Specifically, the central control module analyzes the time-series data of complex concentration based on a recurrent neural network and determines the dynamic trend of the complex composition of the catalyst based on a long short-term memory network. This includes: the central control module is configured to acquire the time-series data of complex concentration in real time and obtain concentration changes at different catalytic stages; the central control module is also configured to perform noise reduction and normalization on the concentration data based on a data preprocessing algorithm; the central control module is further configured to extract the time-series features of the concentration data based on an LSTM network, identify the concentration change pattern over time, and combine an attention mechanism to increase the weight of concentration changes at key time points; and the central control module is also configured to predict the future trend of complex concentration based on regression analysis and dynamically determine catalytic parameters based on the prediction results.

[0066] Understandably, by using convolutional layers to scan the input spectral signals and electrochemical data, spectral absorption features (such as absorption peak positions and absorbance intensity) and electrochemical response features (such as current and potential change patterns) are extracted. This process, through the filtering effect of convolutional layers, can effectively identify useful information in the data while removing noise, making subsequent analysis more accurate. Feature extraction from spectral signals can reveal the chemical composition of the catalyst, while feature extraction from electrochemical data helps to reveal the dynamic process of the catalytic reaction. Secondly, the application of pooling layers in convolutional neural networks further reduces the dimensionality of the data, extracting the most representative features. By downsampling the feature maps output by convolutional layers, pooling layers can effectively reduce the computational complexity of the data while retaining key information. This dimensionality reduction process can reduce the interference of redundant features, making the model more efficient and more conducive to capturing the main features of the complex. Spectral and electrochemical features are effectively fused in this way, providing high-quality input data for subsequent analysis by the central control module. Next, the central control module also fuses spectral and electrochemical features through fully connected layers to form a complete feature vector of the complex. Fully connected layers, by weighted connections of multiple neurons, comprehensively consider all input features and generate a feature vector that comprehensively represents the composition of the complex. This feature vector includes spectral absorption features, electrochemical response features, and their interrelationships, providing comprehensive information support for subsequent analysis. When analyzing time-series data of complex concentration, the central control module employs recurrent neural network (RNN) technology, particularly Long Short-Term Memory (LSTM) networks. LSTM, through its unique memory units, effectively captures time-series features in concentration data, identifying patterns of concentration change over time. LSTM networks overcome the gradient vanishing problem commonly encountered by traditional neural networks when processing time-series data, enabling the model to better understand and predict complex temporal change patterns. Finally, the central control module combines attention mechanisms and regression analysis to predict the dynamic trend of complex concentration changes. The attention mechanism assigns different weights to key time points in the time series, allowing the model to focus more on concentration changes at critical moments, thereby improving prediction accuracy. Based on the time-series features extracted by the LSTM network and the predicted concentration change trends, the central control module can dynamically adjust catalytic parameters to ensure the efficient and stable catalytic process, thereby optimizing the catalytic reaction effect.

[0067] It can be seen that CNNs can extract important features from spectral signals and electrochemical data, especially spectral absorption features (such as absorption peak positions and absorbance intensity) and electrochemical response features (such as current and potential change patterns). This technique makes the identification of complex components more accurate, providing a reliable data foundation for subsequent catalytic analysis. Secondly, by using pooling layers to reduce data dimensionality, CNNs reduce information redundancy and computational burden, while extracting the most representative features. This not only improves the efficiency of the model but also enhances the stability of feature extraction. Pooling operations can effectively compress the scale of input data, reduce computational resource consumption, and retain key information, further improving the analytical capabilities of spectral and electrochemical features. Furthermore, the combination of CNNs and pooling layers can significantly enhance the performance of the central control module when processing complex data, especially in the compression and information extraction of high-dimensional data. When spectral signals and electrochemical data features are fused into fully connected layers, the central control module can form a comprehensive complex feature vector that fully describes the chemical properties of the catalyst. The use of fully connected layers allows for the integration of different types of features, resulting in more accurate and comprehensive component analysis results. Furthermore, recurrent neural networks based on Long Short-Term Memory (LSTM) can analyze time-series data of complex concentrations and identify patterns in concentration changes. Combined with an attention mechanism, LSTM can more accurately focus on key change points in the time series, improving the model's responsiveness to dynamic changes. This technique provides significant performance improvements when processing complex time-varying data, especially in identifying and predicting concentration change trends. Finally, by predicting future trends in complex concentration changes through regression analysis, and combining real-time concentration data and catalytic stage information, the central control module can dynamically adjust catalytic parameters based on the prediction results. This prediction-based adjustment strategy not only optimizes the catalytic process but also ensures the stability and efficiency of the catalytic reaction, thereby maximizing treatment effectiveness and catalytic efficiency.

[0068] Specifically, the central control module is also configured to determine the electrocatalytic parameters of the catalytic module based on fuzzy control algorithms, combined with historical data, the complex composition of the catalyst, and real-time catalytic liquid information. This includes: the central control module is also configured to perform fuzzification processing on variables such as complex concentration, catalytic current, and catalytic time based on fuzzy logic, and map each variable to a fuzzy set based on membership functions; the central control module is also configured to infer the catalytic parameters based on preset rules using a fuzzy rule base, and determine the catalytic parameters of the complex composition based on fuzzy inference; the central control module is also configured to model historical catalytic data based on time series analysis, and optimize real-time parameter calculations using Bayesian inference or Kalman filtering; and the central control module is also configured to determine the electrocatalytic parameters of the catalytic module based on real-time parameter calculations.

[0069] Specifically, when the central control module combines a fuzzy rule base to infer catalytic parameters based on preset rules and determines the catalytic parameters of complex components based on fuzzy reasoning, the following steps are included: the central control module is also configured to construct a mapping relationship between complex components and catalytic parameters based on historical data to form fuzzy rules for catalytic current, catalytic time, and pH value; the central control module is also configured to map complex concentration variables, current density variables, and redox potential variables to fuzzy sets; the central control module is also configured to perform rule reasoning based on the Mamdani reasoning method to determine the fuzzy output of catalytic current and catalytic time; the central control module is also configured to convert the fuzzy reasoning results into specific values ​​using the maximum membership method; the central control module is also configured to optimize the specific values ​​based on historical data and real-time catalytic liquid information, and use the optimized specific values ​​as the execution parameters of the catalytic module.

[0070] Understandably, by transforming these variables into fuzzy sets and mapping them to specific fuzzy sets using membership functions, the shortcomings of traditional control methods in handling uncertainty and fuzziness can be addressed. Fuzzification enables the system to better handle complex variable relationships and incomplete input data, ensuring more flexible and precise adjustment of catalytic parameters. Next, the central control module infers catalytic parameters by combining a fuzzy rule base with preset rules. In this process, historical data is used to construct a mapping relationship between complex components and catalytic parameters, thereby generating fuzzy rules related to complex components, such as catalytic current, catalytic time, and pH value. The fuzzy rule base combines actual catalytic conditions with theoretical reasoning to achieve intelligent adjustment of catalytic parameters, effectively improving the stability and reaction efficiency of the catalytic process. Furthermore, the fuzzy inference system uses the Mamdani inference method to reason about the fuzzy rules and derive outputs such as catalytic current and catalytic time. This inference method generates fuzzy output values ​​by matching input fuzzy variables with fuzzy rules in the rule base. These fuzzy output values ​​accurately reflect the variables that need to be adjusted during the catalytic process, but still require further processing to be converted into specific execution parameters. To transform fuzzy inference results into concrete numerical values, the central control module employs the Max Membership Principle, selecting the most suitable output value based on this principle. This method ensures that the parameter best suited to the actual catalytic process is chosen from multiple possible outputs derived from fuzzy inference, thereby improving the accuracy of the catalytic process. Finally, by optimizing real-time and historical catalytic data using Bayesian inference or Kalman filtering, the central control module can dynamically adjust the electrocatalytic parameters. Through Bayesian inference and Kalman filtering techniques, the central control module can optimize catalytic parameters based on real-time input data, ensuring that the electrocatalytic process maintains optimal operating conditions under different circumstances. This optimization calculation method adaptively adjusts catalytic parameters, thereby improving catalytic efficiency and system stability.

[0071] It can be seen that by using fuzzy control algorithms to fuzzify key variables such as complex concentration, catalytic current, and catalytic time, the system can handle various uncertainties and fuzzy factors. In actual production, the properties of the catalytic solution are often affected by multiple factors. Using fuzzy logic to transform them into fuzzy sets helps avoid the problem that traditional control methods cannot accurately reflect system changes, thus ensuring that the system can make accurate decisions in real time. Secondly, combining fuzzy rule bases to reason about catalytic parameters helps to intelligently adjust key parameters in the catalytic process. By establishing a mapping relationship between historical data and catalytic parameters, fuzzy rules can reflect the optimal catalytic current, catalytic time, and pH value under different complex compositions, providing stronger adaptability and flexibility to the catalytic process. This process can make precise adjustments for different situations, improving catalytic efficiency and reaction rate. Thirdly, using the Mamdani inference method to reason about parameters such as catalytic current and catalytic time helps the system comprehensively consider multiple factors and arrive at more appropriate control decisions when facing complex catalytic reactions. When dealing with problems with uncertainty and fuzziness, the Mamdani inference method can effectively incorporate multiple factors into its consideration, improving the accuracy of the catalytic reaction and ensuring its stability and efficiency. Furthermore, by converting the fuzzy inference results into specific numerical values ​​using the maximum membership method, accurate numerical output of control variables in the catalytic process can be ensured. This method effectively solves the uncertainty problem in fuzzy inference, automates and refines the catalytic process, avoids human error, and ensures the efficient operation of the catalytic module. Finally, optimizing real-time data based on Bayesian inference or Kalman filtering techniques helps the central control module more accurately adjust the electrocatalytic parameters of the catalytic module. Real-time optimization can cope with fluctuations in the composition of the catalytic solution and complex reactions, further improving the adaptability of the catalytic process and ensuring that the catalytic module's performance remains at its best. In addition, the combination of historical data and real-time information provides the central control module with more comprehensive decision support, making the catalytic process more efficient, stable, and precise.

[0072] Specifically, when the early warning module determines whether to issue an early warning based on the relationship between the catalytic liquid information and the electrocatalytic parameters, the following is included: The early warning module is also configured to determine whether to issue an early warning based on the relationship between the execution parameters and the preset execution parameters configured in the catalytic module: when the execution parameters are lower than the preset execution parameters, the early warning module determines not to issue an early warning; when the execution parameters are higher than or equal to the preset execution parameters, the early warning module determines to issue an early warning and determines the early warning level based on the relationship between the execution parameters and the preset execution parameters.

[0073] Specifically, when the early warning module determines the early warning level based on the relationship between the execution parameters and preset execution parameters, it includes: the early warning module is further configured to obtain the parameter difference between the execution parameters and the preset execution parameters, and determine the early warning level according to the relationship between the parameter difference and the first preset parameter difference and the second preset parameter difference configured by the early warning module; when the parameter difference is lower than or equal to the first preset parameter difference, the early warning level is determined to be low risk; when the parameter difference is higher than the first preset parameter difference and lower than or equal to the second preset parameter difference, the early warning level is determined to be medium risk; when the parameter difference is higher than the second preset parameter difference, the early warning level is determined to be high risk; wherein, the first preset parameter difference is less than the second preset parameter difference, and the severity of the early warning level is in the order of low risk, medium risk, and high risk.

[0074] Understandably, the early warning module can detect anomalies in the catalytic process in real time by monitoring the differences between the executed parameters and preset executed parameters. When the executed parameters are lower than the preset parameters, the early warning module determines not to issue an early warning, meaning the catalytic process is within the normal range and no further intervention is needed. However, when the executed parameters are higher than or equal to the preset parameters, the early warning module issues an early warning, indicating a potential risk in the catalytic process. Secondly, by analyzing the difference between the executed parameters and the preset executed parameters, the early warning module can determine different levels of risk and thus establish different early warning levels. This technical principle classifies potential risks into low-risk, medium-risk, and high-risk levels by measuring the parameter differences, thereby effectively managing potential risks in a tiered manner. In this way, the system can take corresponding countermeasures based on different risk levels to ensure the safety of the catalytic process. Finally, by setting a first preset parameter difference and a second preset parameter difference, the early warning module can further refine the risk assessment mechanism. The magnitude of the parameter difference and the relationship between these preset differences directly affect the determination of the early warning level, making the risk assessment process more accurate. For example, when the parameter difference is lower than or equal to the first preset difference, the system determines it to be at a low risk level, while when the difference exceeds this range, it enters a medium or high risk state. This hierarchical risk assessment method enhances the flexibility and accuracy of the early warning system, and helps to identify and deal with potential safety issues in a timely manner during the catalysis process.

[0075] In the above embodiments, by combining convolutional neural networks (CNN) and recurrent neural networks (RNN) for intelligent analysis of the complex composition of the catalyst, the accuracy and intelligence level of the electrocatalytic device for breaking complexes in electroplating rinsing wastewater are significantly improved. The central control module accurately identifies the complex composition based on deep learning algorithms and dynamically optimizes electrocatalytic parameters (such as current density and catalytic time) by combining fuzzy control algorithms, historical data, and real-time catalytic liquid information. This achieves adaptive adjustment of the catalytic process, thereby significantly improving catalytic efficiency and system stability. Through this intelligent control mechanism, the device can respond to changes in wastewater composition in real time, avoiding the problems of catalytic instability or low efficiency caused by insufficient experience in traditional methods. Furthermore, the introduction of an early warning module allows abnormal situations in the catalytic process (such as changes in catalytic liquid composition or deviations of catalytic parameters from preset ranges) to be monitored and warned in a timely manner, preventing over-catalysis or catalytic failure, thus ensuring the consistency and stability of the complex-breaking effect. Through intelligent parameter optimization and real-time feedback control, the entire treatment process is not only more efficient and accurate but also reduces the need for human intervention, lowering operational risks and costs.

[0076] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A complex-breaking electrocatalytic device for electroplating rinsing wastewater based on titanium-based nano-conductive ceramics, comprising an oxidation tank, characterized in that, include: The acquisition module is located inside the oxidation tank and is configured to collect information about the catalytic solution inside the oxidation tank. A catalytic module is configured inside the oxidation tank to catalyze the catalytic liquid inside the oxidation tank. The central control module is electrically connected to the acquisition module and the catalytic module respectively. The central control module is configured to analyze the complex composition of the catalyst based on convolutional neural network and recurrent neural network. The central control module is also configured to determine the electrocatalytic parameters of the catalytic module based on fuzzy control algorithm, combined with historical data, the complex composition of the catalyst, and real-time catalytic liquid information. The early warning module is electrically connected to both the catalytic module and the central control module. The early warning module is configured to determine whether to issue an early warning based on the relationship between catalytic liquid information and electrocatalytic parameters. The acquisition module includes a wastewater composition spectral sensor configured to monitor the catalytic solution in real time, an ultrasonic sensor configured to detect the flow rate of the catalytic solution in real time, and an electrochemical sensor configured to detect the current density and pH value of the catalytic solution in real time. The central control module analyzes the complex composition of the catalyst based on convolutional neural networks and recurrent neural networks by extracting features from spectral signals and electrochemical data using convolutional neural networks, extracting spectral absorption features and electrochemical response features of the complex based on convolutional layers, reducing data dimensionality based on pooling layers, and extracting complex composition information of the catalyst. The time series data of complex concentration were analyzed based on recurrent neural networks, and the dynamic trend of the complex composition of the catalyst was determined based on long short-term memory networks. The central control module determines the electrocatalytic parameters of the catalytic module based on fuzzy control algorithms, combined with historical data, the complex composition of the catalytic compound, and real-time catalytic liquid information. The steps include: fuzzifying variables such as complex concentration, catalytic current, and catalytic time using fuzzy logic, and mapping each variable to a fuzzy set based on a membership function; inferring catalytic parameters based on preset rules using a fuzzy rule base, and determining the catalytic parameters of the complex composition based on fuzzy inference; modeling historical catalytic data based on time series analysis, and optimizing real-time parameter calculations using Bayesian inference or Kalman filtering; and determining the electrocatalytic parameters of the catalytic module based on the real-time parameter calculations.

2. The electrocatalytic device for breaking down the complex in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics as described in claim 1, characterized in that, The electrode material of the catalytic module is specifically titanium-based nano-conductive ceramic.

3. The electrocatalytic device for breaking down the complex in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics as described in claim 1, characterized in that, The central control module extracts features from spectral signals and electrochemical data based on convolutional neural networks, and extracts the spectral absorption features and electrochemical response features of complexes based on convolutional layers, including: The central control module is also configured to denoise the spectral signal based on a filtering algorithm and perform baseline correction on the electrochemical data; The central control module is also configured to scan the spectral signal based on the convolutional layer and extract spectral absorption features, including absorption peak position and absorbance intensity. The central control module is also configured to perform convolution operations on electrochemical data based on the convolutional layer and extract the change patterns of current and potential. The central control module is also configured to perform dimensionality reduction processing on spectral features and electrochemical features based on the pooling layer. The central control module is also configured to form a complete feature vector of the complex based on the fusion of spectral and electrochemical features of the fully connected layer.

4. The electrocatalytic device for breaking down the complex in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics as described in claim 3, characterized in that, The central control module analyzes the time-series data of complex concentration based on a recurrent neural network, and determines the dynamic trend of the composition of the catalytic complex based on a long short-term memory network, including: The central control module is also configured to acquire time-series data of complex concentration in real time and to acquire concentration changes at different catalytic stages; The central control module is also configured to perform noise reduction and normalization on the concentration data based on a data preprocessing algorithm; The central control module is also configured to extract time-series features of concentration data based on LSTM network, identify the concentration change pattern over time, and combine an attention mechanism to increase the weight of concentration changes at key time points. The central control module is also configured to predict the future trend of complex concentration based on regression analysis and dynamically determine catalytic parameters based on the prediction results.

5. The electrocatalytic device for breaking down the complex in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics as described in claim 1, characterized in that, When the central control module combines a fuzzy rule base to infer catalytic parameters based on preset rules, and determines the catalytic parameters for complex composition based on fuzzy inference, it includes: The central control module is also configured to construct a mapping relationship between complex components and catalytic parameters based on historical data, so as to form fuzzy rules for catalytic current, catalytic time, and pH value; The central control module is also configured to map complex concentration variables, current density variables, and redox potential variables to fuzzy sets; The central control module is also configured to perform rule-based reasoning based on the Mamdani reasoning method to determine the fuzzy output of catalytic current and catalytic time; The central control module is also configured to use the maximum membership degree method to convert fuzzy inference results into specific numerical values; The central control module is also configured to optimize specific values ​​based on historical data and real-time catalyst information, and to use the optimized values ​​as the execution parameters of the catalyst module.

6. The electrocatalytic device for breaking the complex in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics as described in claim 1, characterized in that, The early warning module determines whether to issue an early warning based on the relationship between catalytic liquid information and electrocatalytic parameters, including: The warning module is also configured to determine whether to issue a warning based on the relationship between the execution parameters and the preset execution parameters configured in the catalytic module: when the execution parameters are lower than the preset execution parameters, the warning module determines not to issue a warning. When the execution parameter is higher than or equal to the preset execution parameter, the early warning module will issue an early warning and determine the early warning level based on the relationship between the execution parameter and the preset execution parameter.

7. The electrocatalytic device for breaking down the complex in electroplating rinsing wastewater based on titanium-based nano-conductive ceramics as described in claim 6, characterized in that, When determining the warning level based on the relationship between the execution parameters and preset execution parameters, the early warning module includes: The early warning module is also configured to obtain the parameter difference between the execution parameters and the preset execution parameters, and to determine the early warning level based on the relationship between the parameter difference and the first preset parameter difference and the second preset parameter difference configured by the early warning module; When the parameter difference is lower than or equal to the first preset parameter difference, the warning level is determined to be a low-risk level. When the parameter difference is higher than the first preset parameter difference and lower than or equal to the second preset parameter difference, the warning level is determined to be a medium risk level. When the parameter difference is higher than the second preset parameter difference, the warning level is determined to be a high-risk level. Among them, the difference between the first preset parameter and the difference between the second preset parameter are less than the difference between the two preset parameters, and the severity of the warning levels are low risk level, medium risk level and high risk level in sequence.

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