High-efficiency control method and system for electrostatic precipitator for industrial waste gas treatment

Abstract

The present invention provides an efficient control method and system for electrostatic precipitator for industrial waste gas treatment, which relates to the field of multi-device collaborative control technology, including: interactive integrated sensing device to obtain waste gas component information of industrial waste gas to be treated; interactive collaborative electrostatic precipitator technology cluster to obtain historical industrial waste gas treatment data; pre-construction of gas component response network; collaborative electrostatic precipitator device positioning to obtain K candidate electrostatic precipitator device groups; pre-construction of waste gas purification simulation model; control parameter optimization and optimization result evaluation to obtain target electrostatic precipitator device group and target control parameter group; obtain target electrostatic precipitator process flow; after the sealed pipeline valve is opened, perform dust removal treatment. The present invention solves the technical problem that the parameter setting of multiple devices involved in industrial waste gas treatment in the prior art is usually based on the historical highest industrial waste gas component treatment, and the operating cost of dust removal equipment is relatively high.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-device coordinated control, and in particular to a high-efficiency control method and system for electrostatic precipitator for industrial waste gas treatment. Background Art

[0002] Industrial waste gas treatment is one of the important links in environmental protection and industrial production. Electrostatic precipitator, as one of the key technologies, is widely used in various industrial production processes. It should be understood that in addition to the use of electrostatic precipitator, the electrostatic precipitator process will also use cyclone dust collector, wet desulfurization system and other equipment. Through the coordinated control of multiple equipment, effective harmless treatment of industrial waste gas can be achieved. There is a defect in the existing technology, that is, the parameter setting of multiple devices involved in industrial waste gas treatment is usually based on the historical highest industrial waste gas component treatment, and there is a defect that the operating cost of the dust removal equipment is relatively high. Summary of the invention

[0003] The present application provides an efficient control method and system for electrostatic precipitator for industrial waste gas treatment, aiming to solve the technical problem that the parameter setting of multiple devices involved in industrial waste gas treatment in the prior art is usually based on the treatment of the highest historical industrial waste gas components, resulting in high operating costs for dust removal equipment.

[0004] The first aspect disclosed in the present application provides an efficient control method for electrostatic precipitators for industrial waste gas treatment, the method comprising: interactively integrating a sensor device to obtain waste gas component information of the industrial waste gas to be treated, wherein the integrated sensor device is pre-deployed in a buffer tank, and the industrial waste gas to be treated is temporarily stored in the buffer tank; interactively coordinating an electrostatic precipitator technology cluster to obtain historical industrial waste gas treatment data, wherein the coordinating electrostatic precipitator technology cluster is connected to the buffer tank via a sealed pipe; pre-constructing a gas component response network, wherein the gas component response network is constructed based on the coordinating electrostatic precipitator technology cluster; synchronizing the waste gas component information to the gas component response network to locate the coordinating electrostatic precipitator device, and obtaining K gas components in the waste gas component information. K candidate electrostatic precipitator groups of body components; pre-constructing an exhaust gas purification simulation model, wherein the exhaust gas purification simulation model uses the historical industrial exhaust gas treatment data to improve simulation accuracy; optimizing control parameters of the K candidate electrostatic precipitator groups through the exhaust gas component information, and evaluating the optimization results through the exhaust gas purification simulation model to obtain a target electrostatic precipitator group and a target control parameter group; activating equipment according to the target electrostatic precipitator group in the collaborative electrostatic precipitator technology cluster mapping to obtain a target electrostatic precipitator process flow; after the sealed pipeline valve between the collaborative electrostatic precipitator technology cluster and the buffer tank is opened, the target control parameter group is used to run the target electrostatic precipitator process flow to perform dust removal treatment of the industrial exhaust gas to be treated.

[0005] The second aspect disclosed in the present application provides an efficient electrostatic precipitator control system for industrial waste gas treatment, the system is used for the above-mentioned efficient electrostatic precipitator control method for industrial waste gas treatment, and the system includes: an exhaust gas component information acquisition module, the exhaust gas component information acquisition module is used to interact with an integrated sensor device to obtain exhaust gas component information of the industrial waste gas to be treated, wherein the integrated sensor device is pre-deployed in a buffer tank, and the industrial waste gas to be treated is temporarily stored in the buffer tank; a historical processing data acquisition module, the historical processing data acquisition module is used to interact with a collaborative electrostatic precipitator technology cluster to obtain historical industrial waste gas treatment data, wherein the collaborative electrostatic precipitator technology cluster is connected to the buffer tank through a sealed pipe; a response network pre-construction module, the response network pre-construction module is used to pre-construct a gas component response network, wherein the gas component response network is obtained according to the collaborative electrostatic precipitator technology cluster; a collaborative electrostatic precipitator device positioning module, the collaborative electrostatic precipitator device positioning module is used to synchronize the exhaust gas component information to the gas component response network for Collaborative electrostatic precipitator positioning, obtaining K candidate electrostatic precipitator groups for K gas components in the exhaust gas component information; a simulation model pre-construction module, the simulation model pre-construction module is used to pre-construct an exhaust gas purification simulation model, wherein the exhaust gas purification simulation model uses the historical industrial exhaust gas treatment data to improve simulation accuracy; a control parameter optimization module, the control parameter optimization module is used to optimize the control parameters of the K candidate electrostatic precipitator groups through the exhaust gas component information, and evaluate the optimization results through the exhaust gas purification simulation model to obtain a target electrostatic precipitator group and a target control parameter group; an equipment activation module, the equipment activation module is used to perform equipment activation according to the target electrostatic precipitator group mapped in the collaborative electrostatic precipitator technology cluster, and obtain a target electrostatic precipitator process flow; a dust removal treatment module, the dust removal treatment module is used to use the target control parameter group to run the target electrostatic precipitator process flow to perform dust removal treatment of the industrial exhaust gas to be treated after the sealed pipe valve between the collaborative electrostatic precipitator technology cluster and the buffer tank is opened.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] Through the integrated sensor device pre-deployed in the buffer tank, the waste gas component information of the industrial waste gas to be treated is obtained in real time. Through real-time data, it can respond to changes in waste gas components immediately to improve treatment efficiency and response speed; the collaborative electrostatic precipitator technology cluster connected to the buffer tank through a sealed pipeline is interactively connected to obtain historical industrial waste gas treatment data, and the waste gas purification simulation model is trained using historical industrial waste gas treatment data, which provides a scientific basis for selecting the optimal electrostatic precipitator device group and control parameter group; a gas component response network is constructed based on the collaborative electrostatic precipitator technology cluster, and the waste gas component information is synchronized to the gas component response network, which can intelligently and accurately locate the components suitable for treating the current waste gas. The electrostatic precipitator can improve the accuracy of decision-making and processing efficiency; the control parameters of the candidate electrostatic precipitator group are optimized through the exhaust gas composition information, and the working parameters of each electrostatic precipitator can be accurately adjusted in actual operation. Combined with the evaluation results of the exhaust gas purification simulation model, the control parameters can be optimized in real time to adapt to the changes in exhaust gas components; according to the optimized target electrostatic precipitator group, the equipment can be accurately activated and the process flow can be executed in the collaborative electrostatic precipitator technology cluster, the sealed pipeline valves can be opened, and the electrostatic precipitator process flow can be operated according to the preset target control parameter group, which improves the efficiency of exhaust gas treatment and energy consumption control, and realizes the intelligence and efficiency of the exhaust gas treatment process.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of a process flow of an efficient control method for electrostatic precipitator for industrial waste gas treatment provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of the structure of an efficient electrostatic precipitator control system for industrial waste gas treatment provided in an embodiment of the present application.

[0011] Explanation of the accompanying drawings: exhaust gas component information acquisition module 10, historical processing data acquisition module 20, response network pre-construction module 30, collaborative electrostatic precipitator positioning module 40, simulation model pre-construction module 50, control parameter optimization module 60, equipment activation module 70, dust removal processing module 80. DETAILED DESCRIPTION

[0012] The embodiments of the present application provide an efficient control method for electrostatic precipitator for industrial waste gas treatment, thereby solving the technical problem that the parameter setting of multiple devices involved in industrial waste gas treatment in the prior art is usually based on the treatment of the highest historical industrial waste gas components, resulting in high operating costs for dust removal equipment.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] like Figure 1 As shown, the embodiment of the present application provides an efficient control method for electrostatic precipitator for industrial waste gas treatment, the method comprising:

[0015] An interactive integrated sensor device is used to obtain waste gas component information of the industrial waste gas to be treated, wherein the integrated sensor device is pre-deployed in a buffer tank, and the industrial waste gas to be treated is temporarily stored in the buffer tank.

[0016] The buffer tank is a container for temporarily storing industrial waste gas to be treated, usually located near the industrial waste gas emission point. The integrated sensing device integrates multiple sensors inside to detect various chemical components and physical parameters in the industrial waste gas, such as gas composition, temperature, humidity, etc. The waste gas component information includes the type, concentration, physical properties, etc. of various chemical components in the waste gas.

[0017] The integrated sensor device is pre-installed in the buffer tank so that the sensor can effectively contact the industrial waste gas flowing through the buffer tank. The integrated sensor device is used to collect data on the industrial waste gas to be treated temporarily stored in the buffer tank. For example, a gas sensor is used to detect the concentration of various waste gas components, etc. The data collected by multiple sensors are summarized to obtain waste gas component information.

[0018] An interactive and collaborative electrostatic precipitator technology cluster is used to obtain historical industrial waste gas treatment data, wherein the collaborative electrostatic precipitator technology cluster is connected to the buffer tank through a sealed pipeline.

[0019] The synergistic electrostatic precipitator technology cluster includes multiple synergistic electrostatic precipitator devices. It should be understood that in addition to the electrostatic precipitator, the electrostatic precipitator process will also use cyclone dust collectors, wet desulfurization systems and other equipment to achieve effective harmless treatment of industrial waste gas through the coordinated control of multiple devices. These devices are connected to the nearby buffer tanks through sealed pipes. These sealed pipes ensure that the waste gas will not leak or be interfered with by external pollutants during the flow from the buffer tank to the electrostatic precipitator technology cluster.

[0020] Interactively collaborate with the electrostatic precipitator technology cluster to obtain historical industrial waste gas treatment data. Historical industrial waste gas treatment data is data recorded in the past about the industrial waste gas treatment process, including waste gas components, treatment effects, equipment operating parameters and other information.

[0021] A gas component response network is pre-constructed, wherein the gas component response network is obtained by constructing the collaborative electrostatic precipitator technology cluster.

[0022] The gas component response network is a network topology constructed based on a collaborative electrostatic precipitator technology cluster. It is used to analyze the treatment effects of different equipment on different exhaust gas components, so as to better respond to complex industrial waste gas treatment needs.

[0023] Further, a gas component response network is pre-constructed, wherein the gas component response network is obtained according to the collaborative electrostatic precipitator technology cluster construction, and the method further includes:

[0024] Perform waste gas component aggregation on the historical industrial waste gas treatment data to obtain a standard waste gas indicator set, wherein the standard waste gas indicator set includes a plurality of standard waste gas indicators; interactively obtain a plurality of waste gas pollutant spectra of a plurality of collaborative electrostatic precipitators in the collaborative electrostatic precipitator technology cluster; use the plurality of standard waste gas indicators and the plurality of collaborative electrostatic precipitators as topological nodes, connect the topological nodes according to the plurality of waste gas pollutant spectra, and generate the gas component response network.

[0025] According to the relevant standards for waste gas treatment, a variety of standard waste gas indicators are determined, which cover the main pollutants in waste gas, such as particulate matter, nitrogen oxides, sulfur dioxide, volatile organic compounds, etc. The historical industrial waste gas treatment data are summarized in time series, such as data summary by time period such as hours, days, weeks, etc., the concentration data of various pollutants in the waste gas are aggregated, and the average value, maximum value, minimum value, standard deviation and other statistics in each time period are calculated. Using the aggregated data, the statistical indicators of each pollutant in different time periods, such as average concentration, peak concentration, etc., are calculated to generate a standard waste gas indicator set.

[0026] The exhaust gas pollutant spectrum refers to the collection of the processing capabilities and efficiencies of different collaborative electrostatic precipitators for various pollutants. The pollutant treatment performance data of the equipment is obtained from the operation logs, performance test reports, etc. of each collaborative electrostatic precipitator, including the equipment model, type of pollutants treated, treatment efficiency, etc. The treatment performance data of multiple collaborative electrostatic precipitators are integrated to form multiple exhaust gas pollutant spectra.

[0027] Use graph theory to construct topological nodes, including standard exhaust gas index nodes, which represent different exhaust gas pollutant components, and coordinated electrostatic precipitator nodes, which represent different electrostatic precipitator equipment. Create a network graph and add the standard exhaust gas index node and the coordinated electrostatic precipitator node to the network.

[0028] Based on the exhaust gas pollutant spectrum, the standard exhaust gas index node of a certain pollutant is connected to the cooperative electrostatic precipitator node of the equipment that can treat the pollutant. The weight of the connection can be set according to the equipment's treatment efficiency of the pollutant. A weight is assigned to the connection between each device and the pollutant, and the topological nodes are connected to generate the gas component response network, in which the standard exhaust gas index node and the cooperative electrostatic precipitator node are connected by edges. The weight of each edge represents the efficiency of the equipment in treating the pollutant. The gas component response network finally generated can intuitively display the treatment capacity and efficiency of each device for different pollutants. Through this network, it is possible to quickly locate which device is most effective in treating a certain pollutant, thereby optimizing the selection of equipment.

[0029] The exhaust gas component information is synchronized to the gas component response network to coordinate the positioning of the electrostatic precipitator, and K candidate electrostatic precipitator groups for the K gas components in the exhaust gas component information are obtained.

[0030] The exhaust gas composition information is synchronized to the gas composition response network. The gas composition response network evaluates the treatment effect of each electrostatic precipitator on different gas components based on the input exhaust gas composition information, and recommends an alternative electrostatic precipitator device group for each gas component. These alternative device groups include single electrostatic precipitators, such as electrostatic precipitators, and combinations of multiple electrostatic precipitators to maximize the efficiency of exhaust gas treatment and reduce treatment costs.

[0031] A waste gas purification simulation model is pre-constructed, wherein the waste gas purification simulation model uses the historical industrial waste gas treatment data to improve simulation accuracy.

[0032] The waste gas purification simulation model is used to simulate and predict the treatment effect of industrial waste gas under different treatment conditions. For example, it predicts the removal efficiency of various waste gas components under different treatment conditions and evaluates the performance of different electrostatic precipitator combinations to guide decision-making in actual operations.

[0033] Furthermore, a waste gas purification simulation model is pre-constructed, wherein the waste gas purification simulation model uses the historical industrial waste gas treatment data to improve simulation accuracy, and the method further includes:

[0034] A first collaborative electrostatic precipitator is extracted based on the collaborative electrostatic precipitator technology cluster, and a first exhaust gas pollutant is extracted based on the first collaborative electrostatic precipitator in the multiple exhaust gas pollutant spectrum mapping, wherein the first collaborative electrostatic precipitator is any one of the multiple collaborative electrostatic precipitators, and the first exhaust gas pollutant includes multiple exhaust gas pollutants; the first industrial exhaust gas treatment data of the first collaborative electrostatic precipitator is obtained by calling the historical industrial exhaust gas treatment data; the first industrial exhaust gas treatment data is divided based on the multiple exhaust gas pollutants in the first exhaust gas pollutant as a constraint to obtain multiple single-component exhaust gas treatment data sets, wherein each single-component exhaust gas treatment data set includes multiple Sample intake component data, multiple sample outlet component data and multiple sample device control parameters; construct a standard exhaust gas purification simulation branch based on a back propagation neural network; use the multiple single-component exhaust gas treatment data sets as training data to improve the simulation accuracy of the standard exhaust gas purification simulation branch, and obtain multiple single-component purification simulation branches; after using the multiple exhaust gas pollutants to map and identify the multiple single-component purification simulation branches, connect the multiple single-component purification simulation branches in parallel to obtain a first exhaust gas purification simulation unit; and so on, construct multiple exhaust gas purification simulation units to obtain the multiple cooperative electrostatic precipitators; connect the multiple exhaust gas purification simulation units in parallel to generate the exhaust gas purification simulation model.

[0035] One electrostatic precipitator is randomly selected from multiple synergistic electrostatic precipitator devices in a synergistic electrostatic precipitator technology cluster as a first synergistic electrostatic precipitator, and a first exhaust gas pollutant spectrum of the first synergistic electrostatic precipitator is mapped and obtained. According to the first exhaust gas pollutant spectrum, a plurality of exhaust gas pollutants that can be treated by the first synergistic electrostatic precipitator are obtained as first exhaust gas pollutants. For example, the first exhaust gas pollutants may include sulfur dioxide, nitrogen oxides, etc.

[0036] Data related to the first collaborative electrostatic precipitator is extracted from the historical industrial waste gas treatment data, including detailed records of the first collaborative electrostatic precipitator treating waste gas under different operating conditions, including the intake pollutant concentration, outlet pollutant concentration, treatment efficiency, equipment operating parameters, etc. of the waste gas treatment by the first collaborative electrostatic precipitator, to form the first industrial waste gas treatment data.

[0037] Taking the multiple waste gas pollutants in the first waste gas pollutants as constraints, the first industrial waste gas treatment data are classified according to the type of pollutants, and a separate data set is established for each pollutant. For each pollutant, relevant data are extracted to form a single-component waste gas treatment data set, each data set includes multiple sample intake component data, multiple sample outlet component data and multiple sample device control parameters, wherein the sample intake component data are the pollutant composition and concentration before entering the first collaborative electrostatic precipitator, the sample outlet component data are the pollutant composition and concentration after being treated by the first collaborative electrostatic precipitator, and the sample device control parameters are the control parameters of the first collaborative electrostatic precipitator during the treatment process, such as voltage, current, flow rate, temperature, etc.

[0038] Build the initial neural network model and construct the back-propagation mechanism, including defining the loss function to measure the error between the network output and the real data, using the back-propagation algorithm to calculate the gradient of the loss function with respect to the network parameters, updating the network weights and biases layer by layer, and selecting an optimization algorithm, such as stochastic gradient descent, to improve the training efficiency and effect, and obtain the standard exhaust gas purification simulation branch, providing a basis for subsequent accuracy improvement.

[0039] Each single-component exhaust gas treatment data set is divided into a training set, a validation set, and a test set. The training set is used to train the standard exhaust gas purification simulation branch, and the model parameters are gradually adjusted. The model weights and biases are continuously updated through back propagation and optimization algorithms. During the training process, the performance on the validation set is monitored to prevent overfitting. The performance of each single-component purification simulation branch is evaluated using the test set. The performance of the model on actual data is evaluated through the loss function value. Based on the evaluation results, the model structure and parameters are further adjusted to improve the simulation accuracy of the model. After training and tuning, high-precision single-component purification simulation branches are obtained for each exhaust gas pollutant. These branch models can accurately simulate the behavior of different exhaust gas components during the treatment process, providing a basis for subsequent optimization.

[0040] A plurality of single-component purification simulation branches obtained through training are mapped and identified using a variety of exhaust gas pollutants. Each branch corresponds to a specific exhaust gas pollutant. The input of each branch is the intake component data and the expected exhaust component data of the pollutant, and the output is the device control parameter. All single-component purification simulation branches are connected in parallel to form a comprehensive first exhaust gas purification simulation unit. The first exhaust gas purification simulation unit can route the data to the corresponding single-component purification simulation branch for processing according to the identification of each pollutant. After each single-component purification simulation branch processes its corresponding pollutant, the results are summarized.

[0041] For multiple synergistic electrostatic precipitator devices in the synergistic electrostatic precipitator technology cluster, the above process is repeated to construct multiple exhaust gas purification simulation units. Each device corresponds to an exhaust gas purification simulation unit and independently processes its corresponding exhaust gas pollutants.

[0042] The purification simulation units of each electrostatic precipitator device are connected in parallel to form a comprehensive multi-device exhaust gas purification simulation model. This parallel structure can ensure that the various devices of the entire collaborative electrostatic precipitator technology cluster can work together to optimize the exhaust gas treatment effect.

[0043] The control parameters of the K candidate electrostatic precipitator groups are optimized through the exhaust gas component information, and the optimization results are evaluated through the exhaust gas purification simulation model to obtain the target electrostatic precipitator group and the target control parameter group.

[0044] K alternative electrostatic precipitator device groups are randomly combined to obtain an alternative collaborative control device group. Based on the exhaust gas composition information, the parameters of the alternative collaborative control device group are optimized through the exhaust gas purification simulation model to obtain a collaborative control parameter group. A control fitness function is introduced to evaluate the control fitness of multiple collaborative control parameter groups. The best target electrostatic precipitator device group is determined by comparing the simulation effects of each device group, that is, the device combination that can provide the best purification effect under given exhaust gas composition conditions, and the parameter group corresponding to the target electrostatic precipitator device group is obtained as the target control parameter group.

[0045] Furthermore, the control parameters of the K candidate electrostatic precipitator groups are optimized by using the exhaust gas component information, and the optimization results are evaluated by using the exhaust gas purification simulation model to obtain a target electrostatic precipitator group and a target control parameter group. The method also includes:

[0046] The K candidate electrostatic precipitator groups are combined and enumerated to obtain a plurality of candidate cooperative control device groups; a first candidate cooperative control device group is extracted based on the plurality of candidate cooperative control device groups, wherein the first candidate cooperative control device group is any one of the plurality of candidate cooperative control device groups, and the first candidate cooperative control device group includes K available electrostatic precipitators for treating the exhaust gas of the K gas components; K exhaust gas purification simulation units are positioned in the exhaust gas purification simulation model according to the K available electrostatic precipitators; and K exhaust gas purification simulation units are positioned in the exhaust gas purification simulation model according to the K gas components. Map and activate K single-component purification simulation branches with the same identification in the pseudo-unit; interactively obtain K qualified outlet gas components of the K gas components, and input the K gas components and the K qualified outlet gas components into the K single-component purification simulation branches, and analyze to obtain a first collaborative control parameter group, wherein the first collaborative control parameter group includes K first control parameters; and so on, obtain multiple collaborative control parameter groups of the multiple alternative collaborative control device groups; introduce a control fitness function to evaluate the control fitness of the multiple collaborative control parameter groups, and locate the target electrostatic precipitator device group and the target control parameter group.

[0047] Using the combination enumeration algorithm, K alternative electrostatic precipitator groups are combined and enumerated, and all possible combinations are traversed to form multiple alternative cooperative control device groups, each combination representing a possible exhaust gas treatment configuration.

[0048] A group is randomly selected from a plurality of candidate cooperative control device groups as a first candidate cooperative control device group, and the first candidate cooperative control device group includes K available electrostatic precipitators for performing exhaust gas treatment of the K gas components.

[0049] According to the construction process of the aforementioned exhaust gas purification simulation model, the exhaust gas purification simulation model includes multiple exhaust gas purification simulation units that cooperate with multiple electrostatic precipitators, each exhaust gas purification simulation unit corresponds to an electrostatic precipitator, and each exhaust gas purification simulation unit includes multiple single-component purification simulation branches, each single-component purification simulation branch corresponds to a single-component exhaust gas treatment data set. For the determined K available electrostatic precipitators, map them to the corresponding K exhaust gas purification simulation units in the exhaust gas purification simulation model, and each simulation unit corresponds to an electrostatic precipitator. For the determined K gas components, map each component to the corresponding single-component purification simulation branch in the K exhaust gas purification simulation units, and activate the corresponding K single-component purification simulation branches according to the mapping results.

[0050] K qualified outlet gas components of K gas components are obtained. These data reflect the expected component concentration of each exhaust gas component after being treated by the electrostatic precipitator, which can be determined by the interactive dust removal requirements.

[0051] Each gas component and the corresponding qualified outlet gas component are input into the corresponding single-component purification simulation branch in the exhaust gas purification simulation model. Each single-component purification simulation branch is analyzed according to the input data and model training results to generate corresponding first control parameters. K single-component purification simulation branches output K first control parameters to form the first collaborative control parameter group.

[0052] Similar to the first candidate cooperative control device group, the above steps are repeated for the multiple candidate cooperative control device groups to obtain multiple cooperative control parameter groups for the multiple candidate cooperative control device groups.

[0053] The control fitness function is used to evaluate the control fitness of each collaborative control parameter group, and evaluates the advantages and disadvantages of each combination based on the following key indicators, including unit power consumption of the device, unit exhaust gas treatment time, device operation and maintenance cycle, continuous operation time, and operation recovery time. The function will numerically score each collaborative control parameter group based on key indicators to reflect its adaptability in actual operation. According to the results of the control fitness function evaluation, the combination with the best score is selected as the target electrostatic precipitator device group and target control parameter group.

[0054] Furthermore, a control fitness function is introduced to evaluate the control fitness of the multiple coordinated control parameter groups, and the target electrostatic precipitator group and the target control parameter group are located. The method further includes:

[0055] Interact the historical industrial waste gas treatment data to obtain K unit power consumptions and K unit waste gas treatment durations of the K available electrostatic precipitators when operating the K first control parameters; interact with the collaborative electrostatic precipitator technology cluster to obtain K device operation and maintenance cycles, K continuous operation durations and K operation recovery durations of the K available electrostatic precipitators; synchronize the K device unit power consumptions, K unit waste gas treatment durations, K device operation and maintenance cycles, K continuous operation durations and K operation recovery durations to the control fitness function to obtain a first control fitness; and so on, perform fitness evaluation on the multiple collaborative control parameter groups to obtain multiple control fitnesses; serialize the multiple control fitnesses, and locate the target control parameter group in the multiple collaborative control parameter groups based on the extreme values ​​of the sorting results, and locate the target electrostatic precipitator device group in the multiple alternative collaborative control device groups according to the target control parameter group.

[0056] Match the K first control parameters with the control parameters of the electrostatic precipitator in the historical industrial waste gas treatment data to obtain the actual effect of the device in treating industrial waste gas under the corresponding parameters, including the unit power consumption of the device and the unit waste gas treatment time, where the unit power consumption of the device is the amount of electricity consumed for each unit of waste gas treated, and the unit waste gas treatment time is the time required to treat each unit of waste gas.

[0057] The interactive and collaborative electrostatic precipitator technology cluster obtains K device operation and maintenance cycles, K continuous operation times and K operation recovery times of K available electrostatic precipitators, where the device operation and maintenance cycle refers to the operation cycle of the electrostatic precipitator under normal operation, usually calculated in days or hours; the continuous operation time refers to the longest time that the electrostatic precipitator can operate continuously; the operation recovery time refers to the time it takes for the electrostatic precipitator to return to normal working condition after stopping operation or maintenance.

[0058] The unit power consumption of K devices, the unit exhaust gas treatment time of K devices, the operation and maintenance cycles of K devices, the continuous operation time and the operation recovery time of K devices are synchronized to the control fitness function, and the control fitness calculation is performed to obtain the first control fitness, which represents the adaptability of the first collaborative control parameter group in actual operation.

[0059] The same steps as those for obtaining the first control fitness are adopted to repeat the above calculation process for the multiple cooperative control parameter groups to obtain corresponding multiple control fitnesses.

[0060] Arrange multiple control fitnesses in order from small to large, and determine the control fitness ranked first, that is, the minimum control fitness, according to the sorted fitness values, and obtain the collaborative control parameter group corresponding to the minimum control fitness as the target control parameter group. For the determined target control parameter group, further determine the corresponding electrostatic precipitator device combination from multiple alternative collaborative control device groups as the target electrostatic precipitator device group.

[0061] Furthermore, the control fitness function is as follows:

[0062]

[0063] Among them, R is the control fitness, K is the total number of electrostatic precipitators, E i is the unit power consumption of the i-th electrostatic precipitator, T op,i is the unit exhaust gas treatment time of the i-th electrostatic precipitator, M cyc,i is the operation and maintenance period of the ith electrostatic precipitator, T con,i is the continuous operation time of the ith electrostatic precipitator, T rec,i is the operation recovery time of the i-th electrostatic precipitator device.

[0064] Specifically, the control fitness function is as follows:

[0065]

[0066] The fitness function is composed of multiple energy efficiency parameters of electrostatic precipitators, including unit power consumption, exhaust gas treatment time, device operation and maintenance cycle, and consideration of operation continuity and recovery capability. These factors comprehensively reflect the performance of the electrostatic precipitator in actual operation. The calculation result of the control fitness R depends on the energy efficiency parameters of each electrostatic precipitator. The goal of the function is to minimize R. The smaller R is, the higher the overall energy efficiency is, and the better the performance and efficiency of the electrostatic precipitator is.

[0067] Equipment activation is performed according to the target electrostatic precipitator device group in the collaborative electrostatic precipitator technology cluster mapping to obtain the target electrostatic precipitator process flow.

[0068] The target electrostatic precipitator device group is mapped in the collaborative electrostatic precipitator technology cluster. Based on the mapping results, the selected equipment is activated. Through the activation of the equipment, the target electrostatic precipitator process flow under the current exhaust gas component conditions is obtained, including detailed information such as the operation sequence, parameter settings, and processing efficiency of each device.

[0069] After the sealed pipeline valve between the collaborative electrostatic precipitator technology cluster and the buffer tank is opened, the target control parameter group is used to run the target electrostatic precipitator process flow to perform dust removal treatment on the industrial waste gas to be treated.

[0070] Open the sealed pipe valve, adjust the specific operating parameters of the target electrostatic precipitator group according to the target control parameter group, including electric field strength, voltage, current, nozzle spacing, etc., and remove particulate matter and other pollutants in industrial waste gas through electrostatic precipitator technology during the process. These waste gases are transmitted to the electrostatic precipitator through sealed pipes. After treatment, the waste gas at the outlet should meet environmental protection requirements and emission standards of relevant regulations. Through the above steps, the electrostatic precipitator treatment of industrial waste gas is realized to ensure stable and reliable waste gas purification operation.

[0071] Furthermore, the interactive integrated sensor device obtains waste gas component information of the industrial waste gas to be treated, and the method further includes:

[0072] Based on the standard exhaust gas index set, a retrieval instruction is generated to perform an online call to the integrated sensor device to obtain the integrated sensor device; the gas flow characteristics of the buffer tank are analyzed to locate reliable deployment nodes, and the integrated sensor device is deployed at the reliable deployment nodes; a data collection cycle is preset, and the data collection cycle is used as a constraint to start the integrated sensor device at intervals to collect environmental data from the buffer tank to obtain the exhaust gas component information.

[0073] Based on the standard exhaust gas indicator set, a retrieval instruction is generated, and this retrieval instruction is used to inform the integrated sensor system which specific exhaust gas component information needs to be collected. The generated retrieval instruction is transmitted to the sensor system. After the sensor system receives the retrieval instruction, it matches the corresponding multiple sensors to form the integrated sensor device.

[0074] The gas flow characteristics of the buffer tank are analyzed. The gas flow characteristics analysis includes analyzing the flow pattern of the exhaust gas inside the buffer tank, the distribution of the airflow velocity, the spatial distribution of the gas concentration, etc. This information is used to determine the appropriate sensor device layout nodes, because the layout position should be able to fully represent the overall exhaust gas composition. According to the gas flow characteristics analysis results, reliable layout nodes are located. These nodes are usually at locations where the flow pattern is stable, the exhaust gas components are evenly distributed, or are representative. The integrated sensor devices are arranged at these nodes.

[0075] The data collection cycle is set according to the system design and specific needs. The data collection cycle is a fixed data collection time interval, such as every hour or every day. The preset data collection cycle is used as a constraint condition so that the integrated sensor device performs data collection within the predetermined time interval to achieve regular sampling. When each data collection cycle arrives, the integrated sensor device is automatically started to collect environmental data on the buffer tank, including the concentration, temperature, humidity, etc. of various pollutants, and the data is integrated to obtain exhaust gas component information.

[0076] In summary, the electrostatic precipitator high-efficiency control method for industrial waste gas treatment provided in the embodiments of the present application has the following technical effects:

[0077] Through the integrated sensor device pre-deployed in the buffer tank, the waste gas component information of the industrial waste gas to be treated is obtained in real time. Through real-time data, it can respond to changes in waste gas components immediately to improve treatment efficiency and response speed; the collaborative electrostatic precipitator technology cluster connected to the buffer tank through a sealed pipeline is interactively connected to obtain historical industrial waste gas treatment data, and the waste gas purification simulation model is trained using historical industrial waste gas treatment data, which provides a scientific basis for selecting the optimal electrostatic precipitator device group and control parameter group; a gas component response network is constructed based on the collaborative electrostatic precipitator technology cluster, and the waste gas component information is synchronized to the gas component response network, which can intelligently and accurately locate the components suitable for treating the current waste gas. The electrostatic precipitator can improve the accuracy of decision-making and processing efficiency; the control parameters of the candidate electrostatic precipitator group are optimized through the exhaust gas composition information, and the working parameters of each electrostatic precipitator can be accurately adjusted in actual operation. Combined with the evaluation results of the exhaust gas purification simulation model, the control parameters can be optimized in real time to adapt to the changes in exhaust gas components; according to the optimized target electrostatic precipitator group, the equipment can be accurately activated and the process flow can be executed in the collaborative electrostatic precipitator technology cluster, the sealed pipeline valves can be opened, and the electrostatic precipitator process flow can be operated according to the preset target control parameter group, which improves the efficiency of exhaust gas treatment and energy consumption control, and realizes the intelligence and efficiency of the exhaust gas treatment process.

[0078] Based on the same inventive concept as the high-efficiency control method for electrostatic precipitator used for industrial waste gas treatment in the above-mentioned embodiment, Figure 2 As shown, the embodiment of the present application provides an efficient control system for electrostatic precipitator for industrial waste gas treatment, the system comprising:

[0079] An exhaust gas component information acquisition module 10, the exhaust gas component information acquisition module 10 is used to interact with the integrated sensor device to obtain the exhaust gas component information of the industrial exhaust gas to be treated, wherein the integrated sensor device is pre-deployed in the buffer tank, and the industrial exhaust gas to be treated is temporarily stored in the buffer tank; a historical processing data acquisition module 20, the historical processing data acquisition module 20 is used to interact with the collaborative electrostatic precipitator technology cluster to obtain historical industrial exhaust gas treatment data, wherein the collaborative electrostatic precipitator technology cluster is connected to the buffer tank through a sealed pipe; a response network pre-construction module 30, the response network pre-construction module 30 is used to pre-construct a gas component response network, wherein the gas component response network is obtained according to the collaborative electrostatic precipitator technology cluster; a collaborative electrostatic precipitator positioning module 40, the collaborative electrostatic precipitator positioning module 40 is used to synchronize the exhaust gas component information to the gas component response network to perform collaborative electrostatic precipitator positioning, and obtain K candidate electrostatic precipitators for K gas components in the exhaust gas component information. dust device group; a simulation model pre-construction module 50, the simulation model pre-construction module 50 is used to pre-construct an exhaust gas purification simulation model, wherein the exhaust gas purification simulation model uses the historical industrial exhaust gas treatment data to improve the simulation accuracy; a control parameter optimization module 60, the control parameter optimization module 60 is used to optimize the control parameters of the K candidate electrostatic precipitator groups through the exhaust gas component information, and evaluate the optimization results through the exhaust gas purification simulation model to obtain a target electrostatic precipitator group and a target control parameter group; an equipment activation module 70, the equipment activation module 70 is used to perform equipment activation according to the target electrostatic precipitator group in the collaborative electrostatic precipitator technology cluster mapping to obtain a target electrostatic precipitator process flow; a dust removal treatment module 80, the dust removal treatment module 80 is used to use the target control parameter group to run the target electrostatic precipitator process flow to perform dust removal treatment on the industrial exhaust gas to be treated after the sealed pipe valve between the collaborative electrostatic precipitator technology cluster and the buffer tank is opened.

[0080] Furthermore, the system further comprises a gas component response network generation module to perform the following operation steps:

[0081] Perform waste gas component aggregation on the historical industrial waste gas treatment data to obtain a standard waste gas indicator set, wherein the standard waste gas indicator set includes a plurality of standard waste gas indicators; interactively obtain a plurality of waste gas pollutant spectra of a plurality of collaborative electrostatic precipitators in the collaborative electrostatic precipitator technology cluster; use the plurality of standard waste gas indicators and the plurality of collaborative electrostatic precipitators as topological nodes, connect the topological nodes according to the plurality of waste gas pollutant spectra, and generate the gas component response network.

[0082] Furthermore, the system further includes an exhaust gas component information acquisition module to perform the following operation steps:

[0083] Based on the standard exhaust gas index set, a retrieval instruction is generated to perform an online call to the integrated sensor device to obtain the integrated sensor device; the gas flow characteristics of the buffer tank are analyzed to locate reliable deployment nodes, and the integrated sensor device is deployed at the reliable deployment nodes; a data collection cycle is preset, and the data collection cycle is used as a constraint to start the integrated sensor device at intervals to collect environmental data from the buffer tank to obtain the exhaust gas component information.

[0084] Furthermore, the system further includes an exhaust gas purification simulation model generation module to perform the following operation steps:

[0085] A first collaborative electrostatic precipitator is extracted based on the collaborative electrostatic precipitator technology cluster, and a first exhaust gas pollutant is extracted based on the first collaborative electrostatic precipitator in the multiple exhaust gas pollutant spectrum mapping, wherein the first collaborative electrostatic precipitator is any one of the multiple collaborative electrostatic precipitators, and the first exhaust gas pollutant includes multiple exhaust gas pollutants; the first industrial exhaust gas treatment data of the first collaborative electrostatic precipitator is obtained by calling the historical industrial exhaust gas treatment data; the first industrial exhaust gas treatment data is divided based on the multiple exhaust gas pollutants in the first exhaust gas pollutant as a constraint to obtain multiple single-component exhaust gas treatment data sets, wherein each single-component exhaust gas treatment data set includes multiple Sample intake component data, multiple sample outlet component data and multiple sample device control parameters; construct a standard exhaust gas purification simulation branch based on a back propagation neural network; use the multiple single-component exhaust gas treatment data sets as training data to improve the simulation accuracy of the standard exhaust gas purification simulation branch, and obtain multiple single-component purification simulation branches; after using the multiple exhaust gas pollutants to map and identify the multiple single-component purification simulation branches, connect the multiple single-component purification simulation branches in parallel to obtain a first exhaust gas purification simulation unit; and so on, construct multiple exhaust gas purification simulation units to obtain the multiple cooperative electrostatic precipitators; connect the multiple exhaust gas purification simulation units in parallel to generate the exhaust gas purification simulation model.

[0086] Furthermore, the system further includes a target electrostatic precipitator group and a target control parameter group acquisition module to perform the following operation steps:

[0087] The K candidate electrostatic precipitator groups are combined and enumerated to obtain a plurality of candidate cooperative control device groups; a first candidate cooperative control device group is extracted based on the plurality of candidate cooperative control device groups, wherein the first candidate cooperative control device group is any one of the plurality of candidate cooperative control device groups, and the first candidate cooperative control device group includes K available electrostatic precipitators for treating the exhaust gas of the K gas components; K exhaust gas purification simulation units are positioned in the exhaust gas purification simulation model according to the K available electrostatic precipitators; and K exhaust gas purification simulation units are positioned in the exhaust gas purification simulation model according to the K gas components. Map and activate K single-component purification simulation branches with the same identification in the pseudo-unit; interactively obtain K qualified outlet gas components of the K gas components, and input the K gas components and the K qualified outlet gas components into the K single-component purification simulation branches, and analyze to obtain a first collaborative control parameter group, wherein the first collaborative control parameter group includes K first control parameters; and so on, obtain multiple collaborative control parameter groups of the multiple alternative collaborative control device groups; introduce a control fitness function to evaluate the control fitness of the multiple collaborative control parameter groups, and locate the target electrostatic precipitator device group and the target control parameter group.

[0088] Furthermore, the system further includes a target electrostatic precipitator group positioning module to perform the following operation steps:

[0089] Interact the historical industrial waste gas treatment data to obtain K unit power consumptions and K unit waste gas treatment durations of the K available electrostatic precipitators when operating the K first control parameters; interact with the collaborative electrostatic precipitator technology cluster to obtain K device operation and maintenance cycles, K continuous operation durations and K operation recovery durations of the K available electrostatic precipitators; synchronize the K device unit power consumptions, K unit waste gas treatment durations, K device operation and maintenance cycles, K continuous operation durations and K operation recovery durations to the control fitness function to obtain a first control fitness; and so on, perform fitness evaluation on the multiple collaborative control parameter groups to obtain multiple control fitnesses; serialize the multiple control fitnesses, and locate the target control parameter group in the multiple collaborative control parameter groups based on the extreme values ​​of the sorting results, and locate the target electrostatic precipitator device group in the multiple alternative collaborative control device groups according to the target control parameter group.

[0090] Furthermore, the control fitness function is as follows:

[0091]

[0092] Among them, R is the control fitness, K is the total number of electrostatic precipitators, E i is the unit power consumption of the i-th electrostatic precipitator, T op,i is the unit exhaust gas treatment time of the i-th electrostatic precipitator, Mcyc,i is the operation and maintenance period of the ith electrostatic precipitator, T con,i is the continuous operation time of the ith electrostatic precipitator, T rec,i is the operation recovery time of the i-th electrostatic precipitator device.

[0093] Through the above-mentioned detailed description of the efficient control method of electrostatic precipitator for industrial waste gas treatment in this specification, those skilled in the art can clearly understand the efficient control system of electrostatic precipitator for industrial waste gas treatment in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0094] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An efficient control method for electrostatic precipitator for industrial waste gas treatment, characterized in that: The method comprises: An interactive integrated sensor device is used to obtain waste gas component information of the industrial waste gas to be treated, wherein the integrated sensor device is pre-deployed in a buffer tank, and the industrial waste gas to be treated is temporarily stored in the buffer tank; Interactively coordinate an electrostatic precipitator technology cluster to obtain historical industrial waste gas treatment data, wherein the coordinated electrostatic precipitator technology cluster is connected to the buffer tank through a sealed pipeline; Pre-constructing a gas component response network, wherein the gas component response network is obtained according to the collaborative electrostatic precipitator technology cluster; By synchronizing the exhaust gas component information to the gas component response network to coordinate the positioning of the electrostatic precipitator, K candidate electrostatic precipitator groups of K gas components in the exhaust gas component information are obtained; Pre-constructing an exhaust gas purification simulation model, wherein the exhaust gas purification simulation model uses the historical industrial exhaust gas treatment data to improve simulation accuracy; Optimizing the control parameters of the K candidate electrostatic precipitator groups through the exhaust gas component information, and evaluating the optimization results through the exhaust gas purification simulation model to obtain a target electrostatic precipitator group and a target control parameter group; Activate equipment according to the target electrostatic precipitator device group mapped in the collaborative electrostatic precipitator technology cluster to obtain a target electrostatic precipitator process flow; After the sealed pipeline valve between the collaborative electrostatic precipitator technology cluster and the buffer tank is opened, the target control parameter group is used to run the target electrostatic precipitator process flow to perform dust removal treatment on the industrial waste gas to be treated.

2. The high-efficiency control method for electrostatic precipitator for industrial waste gas treatment according to claim 1, characterized in that: Pre-constructing a gas component response network, wherein the gas component response network is obtained according to the collaborative electrostatic precipitator technology cluster construction, and the method further includes: Aggregating the waste gas components of the historical industrial waste gas treatment data to obtain a standard waste gas index set, wherein the standard waste gas index set includes a plurality of standard waste gas indexes; Interactively obtaining multiple exhaust gas pollutant spectra of multiple cooperative electrostatic precipitators in the cooperative electrostatic precipitator technology cluster; The multiple standard exhaust gas indicators and multiple cooperative electrostatic precipitators are used as topological nodes, and the topological nodes are connected according to the multiple exhaust gas pollutant spectra to generate the gas component response network.

3. The high-efficiency control method for electrostatic precipitator for industrial waste gas treatment according to claim 2, characterized in that: Interactive integrated sensor device to obtain waste gas component information of the industrial waste gas to be treated, the method also includes: Generate a search instruction based on the standard exhaust gas index set to perform a network call on the integrated sensor device to obtain the integrated sensor device; Analyze the gas flow characteristics of the buffer tank, locate reliable deployment nodes, and deploy the integrated sensor device at the reliable deployment nodes; A data collection cycle is preset, and based on the data collection cycle as a constraint, the integrated sensor device is started at intervals to collect environmental data from the buffer tank to obtain the exhaust gas component information.

4. The high-efficiency control method for electrostatic precipitator for industrial waste gas treatment according to claim 2, characterized in that: Pre-constructing an exhaust gas purification simulation model, wherein the exhaust gas purification simulation model uses the historical industrial exhaust gas treatment data to improve simulation accuracy, and the method further includes: Extracting a first cooperative electrostatic precipitator based on the cooperative electrostatic precipitator technology cluster, and extracting a first exhaust gas pollutant based on the first cooperative electrostatic precipitator in the plurality of exhaust gas pollutant spectrum mapping, wherein the first cooperative electrostatic precipitator is any one of the plurality of cooperative electrostatic precipitators, and the first exhaust gas pollutant includes a plurality of exhaust gas pollutants; Obtaining the first industrial waste gas treatment data of the first coordinated electrostatic precipitator from the historical industrial waste gas treatment data; Dividing the first industrial waste gas treatment data by taking the plurality of waste gas pollutants in the first waste gas pollutants as constraints to obtain a plurality of single-component waste gas treatment data sets, wherein each single-component waste gas treatment data set includes a plurality of sample inlet component data, a plurality of sample outlet component data and a plurality of sample device control parameters; Construct a standard exhaust gas purification simulation branch based on back-propagation neural network; Using the multiple single-component exhaust gas treatment data sets as training data to improve the simulation accuracy of the standard exhaust gas purification simulation branch, to obtain multiple single-component purification simulation branches; After using the multiple exhaust gas pollutants to map and identify the multiple single-component purification simulation branches, the multiple single-component purification simulation branches are connected in parallel to obtain a first exhaust gas purification simulation unit; By analogy, multiple exhaust gas purification simulation units are constructed to obtain the multiple cooperative electrostatic precipitators; The plurality of exhaust gas purification simulation units are connected in parallel to generate the exhaust gas purification simulation model.

5. The high-efficiency control method for electrostatic precipitator for industrial waste gas treatment according to claim 4, characterized in that: The control parameters of the K candidate electrostatic precipitator groups are optimized by using the exhaust gas component information, and the optimization results are evaluated by using the exhaust gas purification simulation model to obtain a target electrostatic precipitator group and a target control parameter group. The method further includes: Combining and enumerating the K candidate electrostatic precipitator groups to obtain a plurality of candidate coordinated control device groups; Extracting and obtaining a first candidate coordinated control device group based on the multiple candidate coordinated control device groups, wherein the first candidate coordinated control device group is any one of the multiple candidate coordinated control device groups, and the first candidate coordinated control device group includes K available electrostatic precipitators for performing exhaust gas treatment of the K gas components; Positioning K exhaust gas purification simulation units in the exhaust gas purification simulation model according to the K available electrostatic precipitators; According to the K gas components, K single-component purification simulation branches with the same identification are mapped and activated in the K exhaust gas purification simulation units; Interactively obtain K qualified outgas components of the K gas components, and input the K gas components and the K qualified outgas components into the K single-component purification simulation branches, and analyze to obtain a first coordinated control parameter group, wherein the first coordinated control parameter group includes K first control parameters; By analogy, a plurality of cooperative control parameter groups of the plurality of candidate cooperative control device groups are obtained; A control fitness function is introduced to evaluate the control fitness of the multiple coordinated control parameter groups, and the target electrostatic precipitator group and the target control parameter group are located.

6. The high-efficiency control method for electrostatic precipitator for industrial waste gas treatment according to claim 5, characterized in that: A control fitness function is introduced to evaluate the control fitness of the multiple coordinated control parameter groups, and the target electrostatic precipitator group and the target control parameter group are located. The method further includes: Interacting the historical industrial waste gas treatment data to obtain K unit power consumptions and K unit waste gas treatment durations of the K available electrostatic precipitators when operating the K available electrostatic precipitators using the K first control parameters; Interacting with the collaborative electrostatic precipitator technology cluster to obtain K device operation and maintenance cycles, K continuous operation durations, and K operation recovery durations of the K available electrostatic precipitator devices; Synchronizing the K unit power consumption of the devices, the K unit exhaust gas treatment durations, the K device operation and maintenance cycles, the K continuous operation durations, and the K operation recovery durations to the control fitness function to obtain a first control fitness; Similarly, the fitness of the multiple collaborative control parameter groups is evaluated to obtain multiple control fitnesses; The multiple control fitnesses are sequenced, and the target control parameter group is positioned in the multiple cooperative control parameter groups based on the extreme value of the sorting result, and the target electrostatic precipitator device group is positioned in the multiple alternative cooperative control device groups according to the target control parameter group.

7. The high-efficiency control method for electrostatic precipitator for industrial waste gas treatment according to claim 6, characterized in that: The control fitness function is as follows: Among them, R is the control fitness, K is the total number of electrostatic precipitators, E i is the unit power consumption of the i-th electrostatic precipitator, T op,i is the unit exhaust gas treatment time of the i-th electrostatic precipitator, M cyc,i is the operation and maintenance period of the ith electrostatic precipitator, T con,i is the continuous operation time of the ith electrostatic precipitator, T rec,i is the operation recovery time of the i-th electrostatic precipitator device.

8. An efficient control system for electrostatic precipitator used for industrial waste gas treatment, characterized in that: For implementing the high-efficiency control method for electrostatic precipitator for industrial waste gas treatment according to any one of claims 1 to 7, the system comprises: An exhaust gas component information acquisition module, the exhaust gas component information acquisition module is used to interact with the integrated sensor device to obtain exhaust gas component information of the industrial exhaust gas to be treated, wherein the integrated sensor device is pre-deployed in a buffer tank, and the industrial exhaust gas to be treated is temporarily stored in the buffer tank; A historical processing data acquisition module, wherein the historical processing data acquisition module is used to interactively coordinate the electrostatic precipitator technology cluster to obtain historical industrial waste gas treatment data, wherein the coordinated electrostatic precipitator technology cluster is connected to the buffer tank through a sealed pipeline; A response network pre-construction module, wherein the response network pre-construction module is used to pre-construct a gas component response network, wherein the gas component response network is obtained according to the collaborative electrostatic precipitator technology cluster construction; A collaborative electrostatic precipitator positioning module, the collaborative electrostatic precipitator positioning module is used to synchronize the exhaust gas component information to the gas component response network to perform collaborative electrostatic precipitator positioning, and obtain K candidate electrostatic precipitator groups for K gas components in the exhaust gas component information; A simulation model pre-construction module, wherein the simulation model pre-construction module is used to pre-construct an exhaust gas purification simulation model, wherein the exhaust gas purification simulation model uses the historical industrial exhaust gas treatment data to improve simulation accuracy; A control parameter optimization module, wherein the control parameter optimization module is used to optimize the control parameters of the K candidate electrostatic precipitator groups according to the exhaust gas component information, and evaluate the optimization results through the exhaust gas purification simulation model to obtain a target electrostatic precipitator group and a target control parameter group; An equipment activation module, the equipment activation module is used to perform equipment activation according to the target electrostatic precipitator device group in the collaborative electrostatic precipitator technology cluster mapping to obtain a target electrostatic precipitator process flow; A dust removal processing module is used to, after the sealed pipeline valve between the collaborative electrostatic precipitator technology cluster and the buffer tank is opened, use the target control parameter group to run the target electrostatic precipitator process flow to perform dust removal processing on the industrial waste gas to be treated.

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