Intelligent operation and maintenance system for industrial wastewater treatment

Abstract

The invention relates to the technical field of industrial wastewater treatment, in particular to an intelligent operation and maintenance system for industrial wastewater treatment, which comprises a wastewater data acquisition module, a wastewater data preprocessing module, a wastewater complex component identification and classification module and a wastewater treatment process scheduling and optimization module, the wastewater data acquisition module acquires water quality monitoring data in wastewater in real time; the wastewater data preprocessing module is used for preprocessing the collected water quality monitoring data; the wastewater complex component identification and classification module realizes optimized classification of wastewater complex components; and the wastewater treatment process scheduling and optimizing module selects a wastewater treatment process, automatically adjusts parameters of the wastewater treatment process and optimizes an operation strategy of wastewater treatment. According to the invention, different pollutant components in the wastewater and dynamic change trends thereof are accurately identified, and the efficiency and precision of wastewater component analysis are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial wastewater treatment, and particularly to an intelligent operation and maintenance system for industrial wastewater treatment. Background Art

[0002] With the rapid development of industrialization, the discharge of industrial wastewater continues to increase, which contains a large number of complex pollutant components, such as organic matter, inorganic matter, microbial communities, and nanoparticles, etc. The diversity and dynamic change characteristics of these components pose great challenges to wastewater treatment. The industrial wastewater treatment system needs to meet the effluent water quality standards while minimizing operating energy consumption and resource consumption. However, due to the complex and frequently changing characteristics of wastewater, traditional wastewater treatment methods are unable to cope with the requirements of high efficiency, real-time, and dynamic adjustment, and cannot fully meet the needs of modern industrial development.

[0003] Existing industrial wastewater treatment systems usually rely on fixed treatment processes and manual adjustment of process parameters, and it is difficult to respond to changes in wastewater composition in real time, resulting in unstable pollutant removal effects or serious resource waste. In addition, the data processing ability of traditional wastewater monitoring systems is limited, and it is difficult to accurately identify and classify complex components in wastewater, which affects the selection of subsequent treatment processes and parameter adjustment. At the same time, the lack of intelligent process optimization and resource allocation mechanisms makes it difficult to ensure the economy and stability of system operation. There are significant deficiencies in data collection, process control, and intelligent optimization in the existing technology, and there is an urgent need for a more efficient, intelligent, and flexible wastewater treatment system.

[0004] To solve the deficiencies of the existing technology, the present invention proposes an intelligent operation and maintenance system for industrial wastewater treatment, which improves the response ability, stability, and economy of the wastewater treatment system, and provides a more accurate, efficient, and green solution for industrial wastewater treatment. Summary of the Invention

[0005] The present invention provides an intelligent operation and maintenance system for industrial wastewater treatment.

[0006] The intelligent operation and maintenance system for industrial wastewater treatment includes a wastewater data collection module, a wastewater data preprocessing module, a wastewater complex component identification and classification module, and a wastewater treatment process scheduling and optimization module, wherein;

[0007] The wastewater data collection module collects water quality monitoring data in wastewater in real time, including wastewater temperature, pH value, dissolved oxygen, chemical oxygen demand (COD), total suspended solids (TSS);

[0008] The wastewater data preprocessing module preprocesses the collected water quality monitoring data, including denoising, data cleaning, and missing value filling;

[0009] The complex component identification and classification module for wastewater identifies and classifies complex organic substances, inorganic substances, microbial populations, and nanoparticle components in wastewater based on the preprocessed water quality monitoring data, realizes the optimized classification of the complex components of wastewater, and conducts trend analysis on the wastewater components, specifically including:

[0010] Dimensionality reduction and reconstruction of water quality data: Perform dimensionality reduction and reconstruction on the preprocessed water quality monitoring data;

[0011] Identification and classification of wastewater components: Based on the water quality monitoring data after dimensionality reduction and reconstruction, identify and classify the complex components (organic substances, inorganic substances, microbial populations, and nanoparticle components) in wastewater through a convolutional neural network (CNN) model, and automatically identify the types of different components in wastewater;

[0012] Trend analysis of wastewater component changes: Conduct trend analysis on the identified and classified wastewater components to monitor the change rules of wastewater components;

[0013] The wastewater treatment process scheduling and optimization module selects the wastewater treatment process based on the results of the optimized classification of complex wastewater components, combines the wastewater component change trend and treatment objectives, and automatically adjusts the wastewater treatment process parameters to optimize the operation strategy of wastewater treatment.

[0014] Optionally, the wastewater data acquisition module includes:

[0015] Wastewater temperature data acquisition: Real-time acquisition of temperature data in wastewater through a temperature sensor;

[0016] Wastewater pH value acquisition: Use a pH sensor to measure the pH value of wastewater in real time to ensure data accuracy;

[0017] Dissolved oxygen data acquisition: Use a dissolved oxygen sensor (polarographic or Clark type dissolved oxygen sensor) to monitor the dissolved oxygen concentration in wastewater in real time;

[0018] Chemical oxygen demand (COD) acquisition: Measure the chemical oxygen demand (COD) in wastewater through a chemical oxygen demand sensor (photometric or electrochemical sensor);

[0019] Total suspended solids (TSS) acquisition: Use a total suspended solids sensor (turbidity sensor or sedimentation method sensor) to detect the total suspended solids concentration in wastewater in real time.

[0020] Optionally, the wastewater data preprocessing module includes:

[0021] Denoising processing: Use the moving average method to perform denoising processing on the collected water quality monitoring data;

[0022] Data cleaning: In the collected water quality monitoring data, detect whether there are outliers, and replace the outliers with the mean of adjacent values;

[0023] Missing value imputation: When there are missing values in the water quality monitoring data, use the K-Nearest Neighbor (KNN) algorithm to impute the missing values.

[0024] Optionally, the water quality data dimensionality reduction and reconstruction include:

[0025] Dimensionality reduction processing: Use the principal component analysis algorithm to reduce the dimensionality of the water quality monitoring data and calculate the principal component data matrix Z of the data;

[0026] Data reconstruction: Reconstruct the water quality monitoring data according to the reduced-dimensional principal component data matrix Z and the projection matrix.

[0027] Optionally, the Convolutional Neural Network (CNN) model includes:

[0028] Multi-scale convolutional feature extraction: Introduce a multi-scale convolutional module to simultaneously extract multi-scale features of the water quality monitoring data through convolutional kernels of different sizes;

[0029] Attention enhancement mechanism: Introduce a variable weight attention mechanism to dynamically adjust the weights of different features;

[0030] Classification prediction: Output the classification results of the wastewater components through the fully connected layer and the Softmax function.

[0031] Optionally, the analysis of the change trend of the wastewater components includes:

[0032] Trend prediction: Use the Autoregressive Integrated Moving Average model (ARIMA) to predict the change trend of the wastewater components at future times and obtain the component values at future times;

[0033] Change trend analysis: By comparing the predicted values of the trend with the current actual values, calculate the change rate of the wastewater components and analyze the trend of the component changes, including rising, falling, and remaining stable.

[0034] Optionally, the wastewater treatment process scheduling and optimization module includes:

[0035] Treatment process selection: Based on the optimized classification results of the complex components of the wastewater, dynamically select the current wastewater treatment process;

[0036] Process parameter adjustment: Automatically adjust the wastewater treatment process parameters according to the change trend of the wastewater components, including the chemical dosage, reaction time, and bubble generation rate;

[0037] Operation strategy optimization: Optimize the operation strategy of the wastewater treatment according to the treatment objectives (effluent water quality standards, energy consumption limits), including equipment start-stop strategies and resource allocation strategies.

[0038] Optionally, the processing process selection includes:

[0039] Wastewater characteristic analysis: According to the optimized classification results of the complex components of the wastewater, extract the pollutant categories and their concentration ratios in the wastewater to generate a wastewater characteristic vector V;

[0040] Processing process scoring: Based on the wastewater characteristic vector V, perform adaptability scoring on different processing processes;

[0041] Process dynamic selection: According to the results of the processing process scoring, select the processing process M with the highest adaptability score selected .

[0042] Optionally, the process parameter adjustment includes:

[0043] Optimized adjustment of chemical dosage: Adjust the chemical dosage according to the pollutant concentration change rate and the target removal efficiency;

[0044] Dynamic optimization of reaction time: Dynamically adjust the residence time of the wastewater reaction tank according to the pollutant removal target and the current concentration;

[0045] Optimization of bubble generation rate: Adjust the bubble generation rate according to the pollutant removal requirements and the oxygen utilization efficiency to optimize the flotation effect.

[0046] Optionally, the operation strategy optimization includes:

[0047] Operation status monitoring and deviation calculation: Real-time monitor the pollutant removal rate and energy consumption of wastewater treatment, compare with the treatment target, and calculate the operation deviation;

[0048] Equipment start-stop strategy optimization: According to the operation deviation and the wastewater treatment requirements, calculate the priority score of the equipment, and dynamically adjust the start-stop status of the equipment;

[0049] Resource allocation strategy optimization: Dynamically allocate resources (chemicals, gas flow rate) to meet the treatment target and achieve the optimal utilization of resources within the energy consumption limit.

[0050] Advantages of the present invention:

[0051] In the present invention, through the efficient acquisition, preprocessing of wastewater data, accurate identification and classification of complex components, and through the dimensionality reduction and reconstruction of water quality data, the redundant information and noise of the data are significantly reduced, ensuring the integrity and accuracy of the data. Combining the multi-scale feature extraction and attention enhancement mechanism of the convolutional neural network model, it can adapt to the multi-modal characteristics of wastewater data, accurately identify different pollutant components and their dynamic change trends in the wastewater, provide a scientific basis for the optimization of wastewater treatment processes, and significantly improve the efficiency and accuracy of wastewater component analysis.

[0052] In the present invention, through the wastewater treatment process scheduling and optimization module, the dynamic selection of wastewater treatment processes, the intelligent adjustment of key parameters, and the comprehensive optimization of operation strategies are realized. The treatment process selection accurately matches the wastewater characteristics and treatment processes, ensuring that the process can maximize its adaptation to the wastewater characteristics and pollutant removal requirements. The process parameter adjustment module dynamically optimizes the chemical dosage, reaction time, and bubble generation rate, quickly responds to the fluctuations in wastewater composition, and maximizes the pollutant removal effect. The operation strategy optimization module effectively reduces energy consumption and operation costs and improves the overall economy and reliability of wastewater treatment through the intelligent adjustment of equipment start-stop strategies and resource allocation strategies.

[0053] In the present invention, through the precise control of the entire wastewater treatment process, it can dynamically adapt to the changing trend of wastewater composition, maximize the removal of pollutants, while reducing the waste of chemicals and energy, ensuring that the effluent quality meets the standards. Combining real-time monitoring and optimization mechanisms significantly improves the stability and adaptability of system operation, providing an efficient, green, and intelligent solution for the wastewater treatment industry, and is applicable to various complex wastewater treatment scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the system function module of an embodiment of the present invention;

[0055] Figure 2 It is a schematic diagram of the wastewater complex component identification and classification module of an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The present invention will be described in detail below with reference to the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawing part is only for more specifically describing the embodiments and is not intended to specifically limit the present invention.

[0057] As Figure 1 - Figure 2 shown, the intelligent operation and maintenance system for industrial wastewater treatment includes a wastewater data collection module, a wastewater data preprocessing module, a wastewater complex component identification and classification module, and a wastewater treatment process scheduling and optimization module, wherein;

[0058] The wastewater data collection module collects the water quality monitoring data in the wastewater in real time, including wastewater temperature, pH value, dissolved oxygen, chemical oxygen demand (COD), and total suspended solids (TSS);

[0059] The wastewater data preprocessing module preprocesses the collected water quality monitoring data, including denoising, data cleaning, and missing value filling;

[0060] Based on the pre - processed water quality monitoring data, the complex component identification and classification module for wastewater identifies and classifies the complex organic substances, inorganic substances, microbial populations, and nanoparticle components in wastewater, realizes the optimized classification of the complex components of wastewater, conducts trend analysis on the wastewater components, improves the accuracy and real - time performance of wastewater component analysis, and thus provides a scientific basis for the personalized regulation of wastewater treatment. Specifically, it includes:

[0061] Dimensionality reduction and reconstruction of water quality data: Perform dimensionality reduction and reconstruction on the pre - processed water quality monitoring data, reduce the complexity of the data, remove noise and redundant information, and improve the effectiveness of the data;

[0062] Identification and classification of wastewater components: Based on the water quality monitoring data after dimensionality reduction and reconstruction, use a convolutional neural network (CNN) model to identify and classify the complex components (organic substances, inorganic substances, microbial populations, and nanoparticle components) in wastewater, and automatically identify the types of different components in wastewater;

[0063] Trend analysis of wastewater component changes: Conduct trend analysis on the identified and classified wastewater components to monitor the changing patterns of wastewater components;

[0064] Based on the results of the optimized classification of the complex components of wastewater, combined with the trend of wastewater component changes and treatment objectives, the wastewater treatment process scheduling and optimization module selects the wastewater treatment process, automatically adjusts the wastewater treatment process parameters, and optimizes the operation strategy of wastewater treatment to achieve the optimization of the wastewater treatment process, maximize the removal of pollutants, improve the treatment efficiency, and ensure the stability and reliability of system operation;

[0065] Through the above - mentioned content, efficient collection, pre - processing, identification and classification of complex components of wastewater data, as well as precise treatment process scheduling and optimization are achieved, ensuring the accurate analysis and real - time regulation of wastewater components. Thus, the efficiency and stability of wastewater treatment are effectively improved. By dynamically adjusting the treatment process parameters, the treatment process can be optimized based on different wastewater components and changing trends, maximizing the removal of pollutants, reducing energy consumption, and ensuring the intelligence and automation of the wastewater treatment system.

[0066] The wastewater data collection module includes:

[0067] Collection of wastewater temperature data: Real - time collect the temperature data in wastewater through a temperature sensor;

[0068] Collection of wastewater pH value: Use a pH sensor to measure the pH value of wastewater in real - time to ensure the accuracy of the data;

[0069] Collection of dissolved oxygen data: Use a dissolved oxygen sensor (polarographic or Clark - type dissolved oxygen sensor) to monitor the dissolved oxygen concentration in wastewater in real - time;

[0070] Chemical Oxygen Demand (COD) collection: Measure the Chemical Oxygen Demand (COD) in wastewater through a Chemical Oxygen Demand sensor (photometric or electrochemical sensor);

[0071] Total Suspended Solids (TSS) collection: Use a Total Suspended Solids sensor (turbidity sensor or sedimentation method sensor) to detect the concentration of total suspended solids in wastewater in real time;

[0072] Through the above content, the key water quality parameters in wastewater are monitored in real time, ensuring the accuracy and timeliness of data collection during the wastewater treatment process. Each water quality parameter is independently monitored by professional equipment, avoiding cross-interference and improving the reliability and accuracy of the data.

[0073] The wastewater data preprocessing module includes:

[0074] Denoising processing: Use the moving average method to perform denoising processing on the collected water quality monitoring data, expressed as:

[0075]

[0076] where, is the data after denoising, x(t - i) is the i-th data point in the window of the original signal, and N is the size of the window;

[0077] Data cleaning: In the collected water quality monitoring data, detect whether there are outliers, and replace the outliers with the mean of adjacent values to ensure the stability and accuracy of the data, expressed as:

[0078]

[0079] where, x i-1 and x i+1 are the adjacent data before and after the outlier x i respectively, x i is the value of the current data point. When the data exceeds [x min , x max , it is considered an outlier. x min is the minimum reasonable range value of the data, and x max is the maximum reasonable range value of the data;

[0080] Missing value filling: When there are missing values in the water quality monitoring data, use the K-Nearest Neighbor (KNN) algorithm to fill in the missing values, expressed as:

[0081]

[0082] where, x missing is the missing value, x k is the data of the k-th sample most similar to the missing value, wk is the weight, calculated as the reciprocal of the distance, d k is the Euclidean distance between the missing value and the k-th sample, n is the feature dimension of the data, x k,j is the j-th feature of the k-th sample, x missing,j is the j-th feature of the missing value;

[0083] Through the above content, the quality of water quality monitoring data has been improved. The denoising process effectively removes random noise in the data, highlighting the main features of the signal. Data cleaning ensures the stability and reliability of the data by detecting outliers and making reasonable replacements. The filling of missing values uses intelligent algorithms to accurately complete the data, avoiding analysis biases caused by incomplete data.

[0084] Dimensionality reduction and reconstruction of water quality data include:

[0085] Dimensionality reduction processing: The principal component analysis algorithm is used to reduce the dimensionality of water quality monitoring data, calculate the principal component data matrix Z of the data, and retain the main information, expressed as:

[0086] Z = X · W;

[0087] Among them, Z is the data matrix after dimensionality reduction, X is the water quality monitoring data matrix after preprocessing (each row is a sample, and the columns are features, such as temperature, pH value, etc.), and W is the projection matrix of the principal component, calculated based on the covariance matrix Σ of the data, that is n is the number of samples;

[0088] Data reconstruction: According to the principal component data matrix Z after dimensionality reduction and the projection matrix, the water quality monitoring data is reconstructed to restore it to a form close to the original data, removing noise and redundant information, expressed as:

[0089]

[0090] Among them, is the data matrix after reconstruction, Z is the principal component data matrix after dimensionality reduction, is the transpose of the projection matrix;

[0091] Through the above content, the processing of high-dimensional water quality monitoring data significantly reduces the redundant information and noise in the data, reduces the computational complexity, while retaining the main features of the data, ensuring the integrity and accuracy of the information. The dimensionality reduction processing simplifies the data structure, helping to improve the efficiency of subsequent analysis. Reconstruction restores the data to a form close to the original on the basis of dimensionality reduction, avoiding the impact of information loss on subsequent wastewater component identification and treatment optimization.

[0092] The convolutional neural network (CNN) model includes:

[0093] Multi-scale Convolution Feature Extraction: Introduce a multi-scale convolution module to simultaneously extract multi-scale features of water quality monitoring data through convolution kernels of different sizes, expressed as:

[0094] F k = ReLU(W i,k * X + b k );

[0095] F = Concat(F 1 , F 3 , F 5 );

[0096] where F k is the feature extracted using a k×k convolution kernel, W i,k is the convolution kernel parameter of k×k, b k is the bias term, X ∈ R n×m is the input data (where n is the number of samples and m is the feature dimension), ReLU(x) = max(0, x) is the activation function, F is the output of multi-scale feature fusion, F 1 , F 3 , F 5 are the features extracted by convolution kernels of different sizes respectively;

[0097] Attention Enhancement Mechanism: Introduce a variable-weight attention mechanism to dynamically adjust the weights of different features and highlight the importance of key features, expressed as:

[0098] α = Softmax(W a · F + b a );

[0099] F' = α · F;

[0100] where W a and b a are the parameters of the attention mechanism, α ∈ R 1×m is the weight of each feature, x i is the i-th eigenvalue of the input, x j is the j-th eigenvalue of the input, F' is the weighted feature matrix, and α is the weight vector;

[0101] Classification Prediction: Output the classification result of the wastewater composition through a fully connected layer and the Softmax function, expressed as:

[0102] E = ReLU(W f · F' + b f );

[0103] Y = Softmax(E);

[0104]

[0105] Among them, W f is the weight matrix of the fully connected layer, b f is the bias term, E ∈ R n×C is the intermediate result of classification, C is the number of categories, Y ∈ R n×C is the predicted probability distribution of the wastewater component categories, is the final predicted category;

[0106] Through the above content, by introducing the multi-scale convolutional feature extraction and attention enhancement mechanism, it can adapt to the multi-modal characteristics of wastewater data, effectively extract different features of organic matter, inorganic matter, microbial population and nanoparticles in wastewater. At the same time, the attention mechanism highlights the importance of key features according to the dynamic changes of water quality parameters, improves the classification accuracy of the model for complex components, combines the processing requirements of real-time data, not only improves the recognition efficiency, but also has higher robustness and real-time performance, providing strong technical support for the accurate analysis and classification of wastewater components, thereby optimizing the intelligent operation and maintenance effect of wastewater treatment.

[0107] The analysis of the change trend of wastewater components includes:

[0108] Trend prediction: Using the autoregressive integrated moving average model (ARIMA) to predict the change trend of wastewater components at future times, and obtaining the component values at future times, which is expressed as:

[0109]

[0110] Among them, is the wastewater component value at the predicted time t + k, c is the constant term, is the autoregressive coefficient, θ j is the moving average coefficient, is the prediction error term, p is the autoregressive order, and q is the moving average order;

[0111] Change trend analysis: By comparing the predicted value with the current actual value, calculate the change rate of wastewater components and analyze the change trend of components, including rising, falling, and remaining stable, which is expressed as:

[0112]

[0113] Among them, x t is the actual value at the current time t, is the value at the predicted time t + 1, r t is the change rate of the current component. When r t > 0, it means that the component concentration is rising. When r t < 0, it means that the component concentration is falling. When r tWhen it is equal to 0, it means that the component concentration remains stable;

[0114] Through the above, the real-time performance and operation efficiency of the system are improved. By calculating the component change rate and classification trend, it is possible to quickly capture the increase and decrease changes and rates of each component in the wastewater, providing accurate real-time optimization basis for wastewater treatment, being able to identify potential abnormal changes in advance, ensuring the stability and adaptability of the wastewater treatment system, and at the same time enhancing the intelligent level of the treatment process, providing important support for realizing efficient and precise wastewater management.

[0115] The wastewater treatment process scheduling and optimization module includes:

[0116] Treatment process selection: Based on the results of the optimized classification of the complex components of the wastewater, dynamically select the current wastewater treatment process to ensure the maximization of wastewater treatment effect and resource utilization efficiency;

[0117] Process parameter adjustment: Automatically adjust the wastewater treatment process parameters according to the change trend of the wastewater components, including chemical dosage, reaction time, and bubble generation rate;

[0118] Operation strategy optimization: Optimize the operation strategy of wastewater treatment according to the treatment objectives (effluent water quality standard, energy consumption limit), including equipment start-stop strategy and resource allocation strategy;

[0119] Through the above, dynamically select the most suitable treatment process for the current wastewater characteristics, automatically adjust the key process parameters, and optimize the equipment start-stop and resource allocation strategies to achieve the high efficiency and intelligence of wastewater treatment. It can not only maximize the removal of pollutants and improve the treatment efficiency, but also dynamically adapt to the change trend of wastewater components, ensure the effluent water quality meets the standards, reduce energy consumption and operation costs, and enhance the stability and reliability of the system.

[0120] Treatment process selection includes:

[0121] Wastewater characteristic analysis: According to the results of the optimized classification of the complex components of the wastewater, extract the pollutant categories and their concentration ratios in the wastewater, and generate a wastewater characteristic vector V, expressed as:

[0122] V = {D 1 , D 2 ,..., D q};

[0123] Among them, D 1 , D 2 ,..., D q are the concentrations of the 1st, 2nd,..., qth types of pollutants in the wastewater, and q is the number of pollutant types;

[0124] Treatment process scoring: Based on the wastewater characteristic vector V, perform an adaptability score on different treatment processes, expressed as:

[0125]

[0126] Among them, S i is the adaptability score of the i-th treatment process, w j is the importance weight of the j-th type of pollutant, and E ij is the removal efficiency of the i-th treatment process for the j-th type of pollutant;

[0127] Process dynamic selection: According to the results of the treatment process scoring, select the treatment process M selected with the highest adaptability score, which is expressed as:

[0128]

[0129] Among them, M selected is the finally selected wastewater treatment process, and M i is the i-th alternative treatment process;

[0130] Through the above content, accurately match the wastewater characteristics with the treatment process to ensure that the selected process can maximize the adaptation to the current wastewater characteristics and pollutant removal requirements, improve the treatment effect and resource utilization efficiency, avoid treatment non-compliance or resource waste caused by process mismatch, and significantly improve the intelligent level and operation stability of the system.

[0131] Process parameter adjustment includes:

[0132] Optimization and adjustment of chemical dosage: According to the pollutant concentration change rate and the target removal efficiency, adjust the chemical dosage, which is expressed as:

[0133] G dose,i = k i ·D i (t)·(1 + r i (t));

[0134]

[0135] Among them, G dose,i is the chemical demand for the i-th type of pollutant, k i is the reaction coefficient between the chemical and the pollutant, D i (t) is the current concentration of the i-th type of pollutant, r i (t) is the pollutant concentration change rate, and G total is the total chemical dosage, and q is the number of pollutant types;

[0136] Dynamic optimization of reaction time: According to the pollutant removal target and the current concentration, dynamically adjust the residence time of the wastewater reaction tank, which is expressed as:

[0137]

[0138] T final = T react ·(1 + β·r i (t));

[0139] Among them, T react is the reaction time, D target is the concentration of pollutants to be removed, D input is the current concentration of pollutants in the wastewater, k react is the reaction rate constant, T final is the adjusted reaction time, and β is the influence coefficient of the change trend;

[0140] Bubble generation rate optimization: According to the removal requirements of pollutants and the oxygen utilization efficiency, adjust the bubble generation rate to optimize the flotation effect, expressed as:

[0141]

[0142] R final = R bubble ·(1 + δ·r i (t));

[0143] Among them, R bubble is the bubble generation rate, C dissolved is the current dissolved oxygen concentration in the wastewater, C required is the target dissolved oxygen concentration, γ is the adjustment coefficient, R final is the optimized bubble generation rate, and δ is the influence coefficient of the trend change;

[0144] Through the above content, ensure the high efficiency and flexibility of the wastewater treatment process, be able to quickly respond to the fluctuations of the wastewater composition, dynamically adjust the process parameters, maximize the pollutant removal effect, avoid the waste of chemicals and energy at the same time, improve the precise control ability and resource utilization efficiency of the wastewater treatment system, reduce the operation cost while ensuring the stability and treatment quality of the system, and provide strong support for the intelligent and sustainable development of wastewater treatment.

[0145] Operation strategy optimization includes:

[0146] Operation status monitoring and deviation calculation: Real-time monitor the pollutant removal rate and energy consumption of wastewater treatment, compare with the treatment target, and calculate the operation deviation, expressed as:

[0147]

[0148] Among them, ΔR is the pollutant removal deviation, R target is the target removal rate, R current is the current pollutant removal rate;

[0149]

[0150] Among them, ΔP is the energy consumption deviation, and P current is the current system energy consumption, and P limit is the target energy consumption limit value;

[0151] Optimization of equipment start-stop strategy: According to the operation deviation and wastewater treatment requirements, calculate the priority score of the equipment, and dynamically adjust the start-stop state of the equipment to ensure that the treatment target is met and the system energy consumption is reduced, which is expressed as:

[0152]

[0153] Among them, α k is the operation importance coefficient corresponding to the equipment, E k is the treatment efficiency of the equipment, P k is the energy consumption of the equipment, S k is the priority score of the kth equipment. When ΔP>0 (energy consumption exceeds the limit), the equipment is turned off in ascending order of the priority score S k . When ΔR>0 (the treatment effect does not meet the standard), the standby equipment is enabled in descending order of the priority;

[0154] Optimization of resource allocation strategy: Dynamically allocate resources (chemical agents, gas flow rate) to meet the treatment target and achieve the optimal utilization of resources within the energy consumption limit, which is expressed as:

[0155] R j =R base,j ·(1 + w 1 ·ΔR - w 2 ·ΔP);

[0156] Among them, w 1 is the removal deviation influence coefficient, w 2 is the energy consumption deviation influence coefficient, R j is the allocation amount of the jth type of resource. If R j >R limit,j , then resource allocation restriction is carried out, that is, R j =R limit,j , R limit,j is the maximum allocation amount of the jth type of resource;

[0157] Through the above, dynamically adjusting the equipment start-stop strategy and resource allocation plan can strictly control energy consumption while meeting the effluent water quality standard, achieve the efficient operation of the system, quickly respond to the deviation of pollutant removal effect and energy consumption, reasonably allocate resources, avoid unnecessary waste, improve the economy and stability of equipment operation at the same time, effectively reduce the operation cost, ensure the up-to-standard discharge and resource optimization of the wastewater treatment system, and provide important support for the intelligent and green wastewater treatment.

[0158] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. Intelligent operation and maintenance system for industrial wastewater treatment, characterized by: It includes wastewater data acquisition module, wastewater data preprocessing module, wastewater complex component identification and classification module and wastewater treatment process scheduling and optimization module, among which; The wastewater data acquisition module collects water quality monitoring data in the wastewater in real time, including wastewater temperature, pH value, dissolved oxygen, chemical oxygen demand, and total suspended solids; The wastewater data preprocessing module preprocesses the collected water quality monitoring data, including denoising, data cleaning and missing value filling; The wastewater complex component identification and classification module identifies and classifies the complex organic matter, inorganic matter, microbial community and nanoparticle components in the wastewater based on the pre-treated water quality monitoring data, realizes the optimized classification of the complex components of the wastewater, and performs trend analysis on the wastewater components, specifically including: Water quality data dimensionality reduction and reconstruction: Dimensionality reduction and reconstruction of pre-processed water quality monitoring data; Wastewater component identification and classification: Based on the water quality monitoring data after dimensionality reduction and reconstruction, the convolutional neural network model is used to identify and classify the complex components in the wastewater, and automatically identify the types of different components in the wastewater; Wastewater composition change trend analysis: Conduct trend analysis on the identified and classified wastewater components to monitor the changing patterns of wastewater components; The wastewater treatment process scheduling and optimization module selects the wastewater treatment process based on the results of the optimized classification of the complex components of the wastewater, combined with the trend of wastewater component changes and the treatment objectives, and automatically adjusts the wastewater treatment process parameters to optimize the operation strategy of the wastewater treatment.

2. The intelligent operation and maintenance system for industrial wastewater treatment according to claim 1 is characterized in that: The wastewater data acquisition module comprises: Wastewater temperature data collection: Real-time collection of wastewater temperature data through temperature sensors; Wastewater pH value collection: Use pH sensor to measure the pH value of wastewater in real time; Dissolved oxygen data collection: Use dissolved oxygen sensors to monitor the dissolved oxygen concentration in wastewater in real time; Chemical oxygen demand collection: The chemical oxygen demand in wastewater is measured by a chemical oxygen demand sensor; Total suspended solids collection: Use the total suspended solids sensor to detect the total suspended solids concentration in the wastewater in real time.

3. The intelligent operation and maintenance system for industrial wastewater treatment according to claim 1 is characterized in that: The wastewater data preprocessing module comprises: De-noising: The moving average method is used to denoise the collected water quality monitoring data; Data cleaning: In the collected water quality monitoring data, detect whether there are outliers and replace them with the mean of adjacent values; Missing value filling: When there are missing values ​​in the water quality monitoring data, the K nearest neighbor algorithm is used to fill the missing values.

4. The intelligent operation and maintenance system for industrial wastewater treatment according to claim 1 is characterized in that: The water quality data dimensionality reduction and reconstruction include: Dimensionality reduction processing: Use the principal component analysis algorithm to reduce the dimension of water quality monitoring data and calculate the principal component data matrix Z of the data; Data reconstruction: Reconstruct the water quality monitoring data based on the principal component data matrix Z and projection matrix after dimensionality reduction.

5. The intelligent operation and maintenance system for industrial wastewater treatment according to claim 4 is characterized in that: The convolutional neural network model includes: Multi-scale convolution feature extraction: Introduce a multi-scale convolution module to simultaneously extract multi-scale features of water quality monitoring data through convolution kernels of different sizes; Attention enhancement mechanism: Introduce a variable weight attention mechanism to dynamically adjust the weights of different features; Classification prediction: The classification results of wastewater components are output through the fully connected layer and Softmax function.

6. The intelligent operation and maintenance system for industrial wastewater treatment according to claim 5 is characterized in that: The wastewater composition change trend analysis includes: Trend prediction: The autoregressive integrated moving average model is used to predict the changing trend of wastewater components in the future and obtain the component values ​​in the future; Change trend analysis: By comparing the trend prediction value with the current actual value, the change rate of the wastewater composition is calculated, and the trend of the composition change is analyzed, including increase, decrease, and stability.

7. The intelligent operation and maintenance system for industrial wastewater treatment according to claim 6 is characterized in that: The wastewater treatment process scheduling and optimization module includes: Treatment process selection: The result of optimized classification of complex components of wastewater, dynamic selection of current wastewater treatment process; Process parameter adjustment: Automatically adjust wastewater treatment process parameters according to the changing trend of wastewater composition, including reagent dosage, reaction time, and bubble generation rate; Operation strategy optimization: According to the treatment objectives, optimize the operation strategy of wastewater treatment, including equipment start and stop strategy and resource allocation strategy.

8. The intelligent operation and maintenance system for industrial wastewater treatment according to claim 7 is characterized in that: The treatment process options include: Wastewater characteristic analysis: According to the optimized classification results of complex components in wastewater, the pollutant categories and their concentration ratios in wastewater are extracted to generate the wastewater characteristic vector V; Treatment process scoring: Based on the wastewater characteristic vector V, the adaptability of different treatment processes is scored; Dynamic process selection: According to the results of the treatment process scoring, select the treatment process M with the highest adaptability score selected .

9. The intelligent operation and maintenance system for industrial wastewater treatment according to claim 8 is characterized in that: The process parameter adjustment includes: Optimization and adjustment of reagent dosage: adjust the dosage of reagent according to the change rate of pollutant concentration and target removal efficiency; Dynamic optimization of reaction time: Dynamically adjust the residence time of the wastewater reaction tank according to the pollutant removal target and current concentration; Bubble generation rate optimization: According to the pollutant removal requirements and oxygen utilization efficiency, the bubble generation rate is adjusted to optimize the flotation effect.

10. The intelligent operation and maintenance system for industrial wastewater treatment according to claim 9 is characterized in that: The operation strategy optimization includes: Operation status monitoring and deviation calculation: real-time monitoring of pollutant removal rate and energy consumption of wastewater treatment, and comparison with treatment targets to calculate operation deviation; Equipment start-stop strategy optimization: Calculate the equipment priority score based on operation deviation and wastewater treatment requirements, and dynamically adjust the equipment start-stop status; Resource allocation strategy optimization: Dynamically allocate resources to meet processing goals and achieve optimal resource utilization within energy consumption constraints.

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