A method and system for optimizing and controlling operating parameters of an air supply system in a coal preparation plant

Abstract

The present application proposes a method and system for optimizing the control of the operating parameters of the air supply system of a coal preparation plant, the method comprising: obtaining the filter press operating sets, dust removal system operating parameters, air filter operating sets and terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the coal preparation plant air system during the period to be optimized; correcting the terminal system wind speed to obtain the corrected terminal system wind speed; inputting the filter press operating sets, dust removal system operating parameters, air filter operating sets and the corrected terminal system wind speed, pressure, temperature, and number of air compressor operating sets into a pre-trained operating parameter optimization model to obtain the operating parameter reference value for the period to be optimized; optimizing and controlling the operating parameters of the air supply system based on the operating parameter reference value. The technical solution proposed in the present application can accurately calculate the operating parameters of the air supply system, solve the problem of ineffective consumption of electric energy, and reduce the labor intensity of the staff.

Description

Technical Field

[0001] The present application relates to the field of intelligent technology for coal preparation plants, and in particular to a method and system for optimizing and controlling operating parameters of an air supply system for a coal preparation plant. Background Art

[0002] From the current actual development situation, most coal preparation plants do not calculate the air volume, and run enough air compressors to ensure that the filter press, dust removal and other systems have enough power sources, which causes a waste of electricity and increases the equipment maintenance cost and labor intensity. Some coal preparation plants have made rough predictions. Due to the large fluctuations in data sources, the predicted data fluctuates greatly, causing frequent start and stop of equipment, affecting the life of the equipment, and the corresponding lag of the air compressor, causing trouble. Therefore, it is necessary to develop a set of intelligent control algorithms that can automatically adjust the fan operating parameters according to production needs. Summary of the invention

[0003] The present application provides an operating parameter optimization control method and system for an air supply system of a coal preparation plant, so as to at least solve the technical problems of lack of intelligent control algorithms and safety assurance measures, insufficient system operation reliability, and difficulty in meeting the needs of modern production.

[0004] The first embodiment of the present application provides a method for optimizing and controlling operating parameters of an air supply system in a coal preparation plant, the method comprising:

[0005] Obtain the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets, and terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system of the coal preparation plant during the period to be optimized;

[0006] Correcting the terminal system wind speed of the air supply system during the period to be optimized to obtain a corrected terminal system wind speed;

[0007] Input the filter press operating sets, dust removal system operating parameters, air filter operating sets in the coal preparation plant air system and the calibrated terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system into a pre-trained operating parameter optimization model to obtain reference values ​​of the air supply system operating parameters for the period to be optimized;

[0008] Optimizing and controlling the operating parameters of the air supply system based on the operating parameter reference values ​​of the air supply system;

[0009] Among them, the operating parameter optimization model is a BP neural network model, which is obtained by training the initial BP neural network based on the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and the calibrated terminal system wind speed, pressure, temperature, number of air compressor operating sets in the air supply system of the coal preparation plant in each period within the historical time period, as well as the actual optimized values ​​of the air supply system operating parameters in each period.

[0010] Preferably, the terminal system wind speed is collected by an anemometer;

[0011] The operating parameters include: the number of air compressors started, the operating frequency of the air compressors, and the opening of the air valves.

[0012] Furthermore, the calculation formula of the corrected terminal system wind speed is as follows:

[0013] b

[0014] In the formula, is the corrected terminal system wind speed, are the polynomial coefficients corresponding to the N-order terminal system wind speed values, is the N-order terminal system wind speed value, b is the offset.

[0015] Furthermore, the BP neural network model includes: an input layer, a hidden layer and an output layer;

[0016] Wherein, the input layer comprises 7 neurons;

[0017] The hidden layer comprises 4 neurons;

[0018] The output layer contains 1 neuron.

[0019] Furthermore, the training process of the operating parameter optimization model includes:

[0020] Step F1: Obtain the filter press operating sets, dust removal system operating parameters, air filter operating sets and corrected terminal system wind speed, pressure, temperature, number of air compressor operating sets in the air supply system of the coal preparation plant in each period of the historical time, and the actual optimized values ​​of the air supply system operating parameters in each period, and construct a training set and a test set, wherein the actual optimized values ​​of the operating parameters include: the number of air compressor startups, the air compressor operating frequency, and the opening of the air valve;

[0021] Step F2: using the filter press operating sets, dust removal system operating parameters, air filter operating sets and calibrated terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the coal preparation plant air system in each period in the training set as input, using the actual optimized values ​​of the air supply system operating parameters in each period as output, and using the genetic algorithm to optimize the initial BP neural network parameters to obtain the optimal initial threshold and weight of the neural network, wherein the mean square error is used as the fitness function of the genetic algorithm;

[0022] Step F3: Input the filter press operating sets, dust removal system operating parameters, air filter operating sets and corrected terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system of the coal preparation plant in each period of the test set into the BP neural network with the optimal initial threshold and weight, and obtain the predicted values ​​of the air supply system operating parameters in each period output by the BP neural network;

[0023] Step F4: determine whether the error between the predicted value of the air supply system operating parameter of each time period output by the BP neural network and the actual optimized value of the air supply system operating parameter of each time period in the test set is less than a first threshold value. If so, use the optimal initial threshold value and weight as the optimal parameter of the BP neural network to obtain the optimized BP neural network, and use the optimized BP neural network as the initial operating parameter optimization model, and enter step F5; otherwise, update the optimal initial threshold value and weight, and return to step F3;

[0024] Step F5: Perform fuzzy correction on the initial operating parameter optimization model to obtain the operating parameter optimization model.

[0025] Furthermore, the fuzzy correction is performed on the initial operation parameter optimization model to obtain the operation parameter optimization model, including:

[0026] Step E1: Determine the Lagrange multiplier of each sample in the training set;

[0027] Step E2: The first step is to remove a preset proportion of sample data in the training set whose Lagrange multiplier is less than the first threshold value, and obtain the The training set after elimination is ;

[0028] Step E3: Based on the The initial operating parameter optimization model is trained with the training set after the first elimination to obtain the The optimization model of operating parameters after the second calibration;

[0029] Step E4: Determination Is it equal to 3? If so, change the The operating parameter optimization model after the correction is used as the operating parameter optimization model, otherwise, let = +1, and return to step E2.

[0030] The second embodiment of the present application proposes an operation parameter optimization control system of a coal preparation plant air supply system, comprising:

[0031] The acquisition module is used to obtain the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets, and the wind speed, pressure, temperature, and number of air compressor operating sets of the terminal system in the air supply system in the coal preparation plant during the period to be optimized;

[0032] A correction module, used to correct the terminal system wind speed of the air supply system in the period to be optimized to obtain a corrected terminal system wind speed;

[0033] A determination module is used to input the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and the corrected terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system of the coal preparation plant into a pre-trained operating parameter optimization model to obtain reference values ​​of the operating parameters of the air supply system in the period to be optimized;

[0034] An optimization control module, used for optimizing and controlling the operating parameters of the air supply system based on the reference values ​​of the operating parameters of the air supply system;

[0035] Among them, the operating parameter optimization model is a BP neural network model, which is obtained by training the initial BP neural network based on the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and the calibrated terminal system wind speed, pressure, temperature, number of air compressor operating sets in the air supply system of the coal preparation plant in each period within the historical time period, as well as the actual optimized values ​​of the air supply system operating parameters in each period.

[0036] Preferably, the terminal system wind speed is collected by an anemometer;

[0037] The operating parameters include: the number of air compressors started, the operating frequency of the air compressors, and the opening of the air valves.

[0038] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect is implemented.

[0039] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect of the present application.

[0040] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0041] The present application proposes a method and system for optimizing the control of operating parameters of an air supply system of a coal preparation plant, wherein the method comprises: obtaining the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air system of the coal preparation plant during the period to be optimized; correcting the terminal system wind speed of the air supply system during the period to be optimized to obtain the corrected terminal system wind speed; inputting the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and the corrected terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system into In the pre-trained operation parameter optimization model, the reference value of the air supply system operation parameter of the time period to be optimized is obtained; based on the reference value of the air supply system operation parameter, the air supply system operation parameter is optimized and controlled; wherein, the operation parameter optimization model is a BP neural network model, and the BP neural network model is based on the filter press operation sets, dust removal system operation parameters, air compressor filter operation sets and the corrected terminal system wind speed, pressure, temperature, number of air compressor operation sets in the air supply system of the coal preparation plant in each period within the historical time length, and the actual optimization value of the air supply system operation parameters in each period. The technical solution proposed in this application can accurately calculate the operation parameters of the air supply system, solve the problem of ineffective consumption of electric energy, and reduce the labor intensity of the staff.

[0042] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0044] Figure 1 A flow chart of an operating parameter optimization control method for an air supply system of a coal preparation plant provided according to an embodiment of the present application;

[0045] Figure 2 A schematic diagram of wind speed correction results of a terminal system provided according to an embodiment of the present application;

[0046] Figure 3A structural diagram of a BP neural network model provided according to an embodiment of the present application;

[0047] Figure 4 A schematic diagram of the correction effect of the operating parameter optimization model provided according to an embodiment of the present application;

[0048] Figure 5 A first structural diagram of an operating parameter optimization control system for an air supply system of a coal preparation plant provided according to an embodiment of the present application;

[0049] Figure 6 This is a second structural diagram of an operating parameter optimization control system for an air supply system of a coal preparation plant provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0051] The present application proposes a method and system for optimizing the control of operating parameters of an air supply system in a coal preparation plant, wherein the method comprises: obtaining the filter press operating set, dust removal system operating parameters, air compressor filter operating set and terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air system of the coal preparation plant during the period to be optimized; correcting the terminal system wind speed of the air supply system during the period to be optimized to obtain the corrected terminal system wind speed; inputting the filter press operating set, dust removal system operating parameters, air compressor filter operating set and the corrected terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system into the pre-optimized control system. First, the reference value of the operating parameters of the air supply system in the period to be optimized is obtained from the trained operating parameter optimization model; the operating parameters of the air supply system are optimized and controlled based on the reference value of the operating parameters of the air supply system; wherein, the operating parameter optimization model is a BP neural network model, which is obtained by training the initial BP neural network based on the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and corrected terminal system wind speed, pressure, temperature, number of air compressor operating sets in the air system of the coal preparation plant in each period within the historical time, and the actual optimized values ​​of the operating parameters of the air supply system in each period. The technical solution proposed in this application can accurately calculate the operating parameters of the air supply system, solve the problem of ineffective consumption of electric energy, and reduce the labor intensity of the staff.

[0052] The following describes a method and system for optimizing and controlling operating parameters of an air supply system of a coal preparation plant according to an embodiment of the present application with reference to the accompanying drawings.

[0053] Embodiment 1

[0054] Figure 1 The flowchart of a method for optimizing the control of operating parameters of an air supply system in a coal preparation plant according to one embodiment of the present application is as follows: Figure 1 As shown, the method includes:

[0055] Step 1: Obtain the number of filter press operating units, dust removal system operating parameters, air compressor filter operating units, and terminal system wind speed, pressure, temperature, and number of air compressor operating units in the air supply system of the coal preparation plant during the period to be optimized.

[0056] It should be noted that the terminal system wind speed is collected using an anemometer.

[0057] Step 2: Correcting the terminal system wind speed of the air supply system during the period to be optimized to obtain a corrected terminal system wind speed.

[0058] In the embodiment of the present disclosure, the calculation formula of the corrected terminal system wind speed is as follows:

[0059] b

[0060] In the formula, is the corrected terminal system wind speed, are the polynomial coefficients corresponding to the N-order terminal system wind speed values, is the N-order terminal system wind speed value, b is an offset, wherein the offset is obtained based on experiments.

[0061] It should be noted that there is an error between the collected terminal system wind speed and the actual wind speed. The relationship between them can be fitted by a power series polynomial as the mathematical model of the correction link, as shown in the formula b , is the corrected terminal system wind speed, i.e. the actual wind speed, The wind speed of the terminal system is collected. Obviously, the degree of the polynomial is related to the fitting accuracy. If the degree of the polynomial is higher, the model accuracy is higher and the correction effect is better. The wind speed data is brought in for verification. Finally, the signal stability is significantly improved and the data processing effect is obvious. Figure 2 shown.

[0062] Step 3: Input the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets in the coal preparation plant's air system and the calibrated terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system into a pre-trained operating parameter optimization model to obtain reference values ​​for the air supply system operating parameters for the period to be optimized.

[0063] It should be noted that the operating parameters include: the number of air compressors started, the operating frequency of the air compressors, and the opening of the air valves.

[0064] The operating parameter optimization model is a BP (back propagation) neural network model, which is obtained by training the initial BP neural network based on the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and corrected terminal system wind speed, pressure, temperature, number of air compressor operating sets in the air supply system of the coal preparation plant in each period within the historical time period, and the actual optimized values ​​of the air supply system operating parameters in each period.

[0065] In the embodiments of the present disclosure, Figure 3 As shown, the BP neural network model includes: an input layer, a hidden layer and an output layer;

[0066] Wherein, the input layer comprises 7 neurons;

[0067] The hidden layer comprises 4 neurons;

[0068] The output layer contains 1 neuron.

[0069] The input layer of the BP neural network model has a total of e (e=7) nodes, the output layer has a total of f (f=1) nodes, and the number of nodes in the hidden layer is m (m=4). W ij For the input layer i The connection weight between the hidden layer j and is the hidden layer j and the output layer k The connection rights between θ j is the threshold of the hidden layer nodes, θ k is the threshold of the output layer node. First, the threshold θ and weight W are randomly assigned. Secondly, the input vector X =( x 1 ,x 2 ,…x e ) and its corresponding output vector Y =( y 1 ， y 2 ,...yf ), Will X The values ​​in are input to each node of the input layer and calculated forward in sequence:

[0070] Input Layer: ;

[0071] Output layer: .

[0072] Furthermore, the training process of the operating parameter optimization model includes:

[0073] Step F1: Obtain the filter press operating sets, dust removal system operating parameters, air filter operating sets and corrected terminal system wind speed, pressure, temperature, number of air compressor operating sets in the air supply system of the coal preparation plant in each period of the historical time, and the actual optimized values ​​of the air supply system operating parameters in each period, and construct a training set and a test set, wherein the actual optimized values ​​of the operating parameters include: the number of air compressor startups, the air compressor operating frequency, and the opening of the air valve;

[0074] Step F2: using the filter press operating sets, dust removal system operating parameters, air filter operating sets and calibrated terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the coal preparation plant air system in each period in the training set as input, using the actual optimized values ​​of the air supply system operating parameters in each period as output, and using the genetic algorithm to optimize the initial BP neural network parameters to obtain the optimal initial threshold and weight of the neural network, wherein the mean square error is used as the fitness function of the genetic algorithm;

[0075] Step F3: Input the filter press operating sets, dust removal system operating parameters, air filter operating sets and corrected terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system of the coal preparation plant in each period of the test set into the BP neural network with the optimal initial threshold and weight, and obtain the predicted values ​​of the air supply system operating parameters in each period output by the BP neural network;

[0076] Step F4: determine whether the error between the predicted value of the air supply system operating parameter of each time period output by the BP neural network and the actual optimized value of the air supply system operating parameter of each time period in the test set is less than a first threshold value. If so, use the optimal initial threshold value and weight as the optimal parameter of the BP neural network to obtain the optimized BP neural network, and use the optimized BP neural network as the initial operating parameter optimization model, and enter step F5; otherwise, update the optimal initial threshold value and weight, and return to step F3;

[0077] Step F5: Perform fuzzy correction on the initial operating parameter optimization model to obtain the operating parameter optimization model.

[0078] It should be noted that the initial optimized radial basis parameters of the operating parameter optimization model can be obtained through the training of steps F1-F4. and the regularization factor ;

[0079] Then, the initial optimized radial basis parameters are and the regularization factor The operating parameter optimization model is corrected to obtain the final radial basis parameters and the regularization factor , and then obtain the optimal operating parameter optimization model.

[0080] The step of performing fuzzy correction on the initial operating parameter optimization model to obtain the operating parameter optimization model includes:

[0081] Step E1: Determine the Lagrange multiplier of each sample in the training set;

[0082] Step E2: The first step is to remove a preset proportion of sample data in the training set whose Lagrange multiplier is less than the first threshold value, and obtain the The training set after elimination is , wherein the preset ratio may be 10%;

[0083] Step E3: Based on the The initial operating parameter optimization model is trained with the training set after the first elimination to obtain the The optimization model of operating parameters after the second calibration;

[0084] Step E4: Determination Is it equal to 3? If so, The operating parameter optimization model after the correction is used as the operating parameter optimization model, otherwise, let = +1, and return to step E2.

[0085] It should be noted that the reference value of the operating parameters of the air supply system obtained based on the corrected operating parameter optimization model can obtain the optimized air volume, that is, the predicted air volume, and its correction effect can be shown as follows: Figure 4 As shown, the results predicted by the calibrated operating parameter optimization model are closer to the actual operating parameters.

[0086] Step 4: Optimizing and controlling the operating parameters of the air supply system based on the operating parameter reference values ​​of the air supply system.

[0087] In summary, the method for optimizing and controlling the operating parameters of the air supply system of a coal preparation plant proposed in this embodiment can accurately calculate the operating parameters of the air supply system, solve the problem of ineffective consumption of electric energy, and reduce the labor intensity of the staff.

[0088] Embodiment 2

[0089] Figure 5 The structure diagram of an operation parameter optimization control system of a coal preparation plant air supply system provided according to one embodiment of the present application is as follows: Figure 5 As shown, the system comprises:

[0090] The acquisition module 100 is used to obtain the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system of the coal preparation plant during the period to be optimized;

[0091] The terminal system wind speed is collected by an anemometer;

[0092] The operating parameters include: the number of air compressors started, the operating frequency of the air compressors, and the opening of the air valves.

[0093] The correction module 200 is used to correct the terminal system wind speed of the air supply system in the period to be optimized to obtain the corrected terminal system wind speed;

[0094] The calculation formula of the corrected terminal system wind speed is as follows:

[0095] b

[0096] In the formula, is the corrected terminal system wind speed, are the polynomial coefficients corresponding to the N-order terminal system wind speed values, is the N-order terminal system wind speed value, b is the offset.

[0097] The determination module 300 is used to input the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and the corrected terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system of the coal preparation plant into a pre-trained operating parameter optimization model to obtain reference values ​​of the air supply system operating parameters for the period to be optimized;

[0098] Among them, the operating parameter optimization model is a BP neural network model, which is obtained by training the initial BP neural network based on the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and the calibrated terminal system wind speed, pressure, temperature, number of air compressor operating sets in the air supply system of the coal preparation plant in each period within the historical time period, as well as the actual optimized values ​​of the air supply system operating parameters in each period.

[0099] It should be noted that the BP neural network model includes: an input layer, a hidden layer and an output layer;

[0100] Wherein, the input layer comprises 7 neurons;

[0101] The hidden layer comprises 4 neurons;

[0102] The output layer contains 1 neuron.

[0103] The optimization control module 400 is used to optimize the operating parameters of the air supply system based on the reference values ​​of the operating parameters of the air supply system.

[0104] In the embodiments of the present disclosure, Figure 6 As shown, the operation parameter optimization control system of the coal preparation plant air supply system further includes: a training module 500;

[0105] The training module 500 is used to train the operating parameter optimization model;

[0106] The training process of the operating parameter optimization model includes:

[0107] Step T1: Obtain the filter press operating sets, dust removal system operating parameters, air filter operating sets and corrected terminal system wind speed, pressure, temperature, number of air compressor operating sets in the air supply system of the coal preparation plant in each period of the historical time, and the actual optimized values ​​of the air supply system operating parameters in each period, and construct a training set and a test set, wherein the actual optimized values ​​of the operating parameters include: the number of air compressor starts, the air compressor operating frequency, and the opening of the air valve;

[0108] Step T2: using the filter press operating sets, dust removal system operating parameters, air filter operating sets and calibrated terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the coal preparation plant air system in each period in the training set as input, using the actual optimized values ​​of the air supply system operating parameters in each period as output, and using the genetic algorithm to optimize the initial BP neural network parameters to obtain the optimal initial threshold and weight of the neural network, wherein the mean square error is used as the fitness function of the genetic algorithm;

[0109] Step T3: input the filter press operating sets, dust removal system operating parameters, air filter operating sets and corrected terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the coal preparation plant air system in each period of the test set into the BP neural network with the optimal initial threshold and weight, and obtain the predicted values ​​of the air supply system operating parameters in each period output by the BP neural network;

[0110] Step T4: determine whether the error between the predicted value of the air supply system operating parameter of each time period output by the BP neural network and the actual optimized value of the air supply system operating parameter of each time period in the test set is less than a first threshold value; if so, use the optimal initial threshold value and weight as the optimal parameter of the BP neural network to obtain the optimized BP neural network, and use the optimized BP neural network as the initial operating parameter optimization model, and enter step T5; otherwise, update the optimal initial threshold value and weight, and return to step T3;

[0111] Step T5: Perform fuzzy correction on the initial operating parameter optimization model to obtain the operating parameter optimization model.

[0112] The step T5 comprises:

[0113] Step Q1: Determine the Lagrange multiplier of each sample in the training set;

[0114] Step Q2: The first step is to remove a preset proportion of sample data in the training set whose Lagrange multiplier is less than the first threshold value, and obtain the The training set after elimination is ;

[0115] Step Q3: Based on the The initial operating parameter optimization model is trained with the training set after the first elimination to obtain the The optimization model of operating parameters after the second calibration;

[0116] Step Q4: Judgment Is it equal to 3? If so, The operating parameter optimization model after the correction is used as the operating parameter optimization model, otherwise, let = +1, and return to step Q2.

[0117] In summary, the operating parameter optimization control system of the air supply system of a coal preparation plant proposed in this embodiment can accurately calculate the operating parameters of the air supply system, solve the problem of ineffective consumption of electric energy, and reduce the labor intensity of the staff.

[0118] Embodiment 3

[0119] In order to implement the above embodiments, the present disclosure further proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in Embodiment 1 is implemented.

[0120] Embodiment 4

[0121] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the first embodiment is implemented.

[0122] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0123] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0124] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in the field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for optimizing and controlling operating parameters of an air supply system in a coal preparation plant, characterized in that: The method comprises: Obtain the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets, and terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system of the coal preparation plant during the period to be optimized; Correcting the terminal system wind speed of the air supply system during the period to be optimized to obtain a corrected terminal system wind speed; Input the filter press operating sets, dust removal system operating parameters, air filter operating sets in the coal preparation plant air system and the calibrated terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system into a pre-trained operating parameter optimization model to obtain reference values ​​of the air supply system operating parameters for the period to be optimized; Optimizing and controlling the operating parameters of the air supply system based on the operating parameter reference values ​​of the air supply system; Among them, the operating parameter optimization model is a BP neural network model, which is obtained by training the initial BP neural network based on the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and the calibrated terminal system wind speed, pressure, temperature, number of air compressor operating sets in the air supply system of the coal preparation plant in each period within the historical time period, as well as the actual optimized values ​​of the air supply system operating parameters in each period.

2. The method according to claim 1, characterized in that The terminal system wind speed is collected by using an anemometer; The operating parameters include: the number of air compressors started, the operating frequency of the air compressors, and the opening of the air valves.

3. The method according to claim 2, characterized in that The calculation formula of the corrected terminal system wind speed is as follows: b In the formula, is the corrected terminal system wind speed, are the polynomial coefficients corresponding to the N-order terminal system wind speed values, is the N-order terminal system wind speed value, b is the offset.

4. The method according to claim 3, characterized in that The BP neural network model includes: an input layer, a hidden layer and an output layer; Wherein, the input layer comprises 7 neurons; The hidden layer comprises 4 neurons; The output layer contains 1 neuron.

5. The method according to claim 4, characterized in that The training process of the operating parameter optimization model includes: Step F1: Obtain the filter press operating sets, dust removal system operating parameters, air filter operating sets and corrected terminal system wind speed, pressure, temperature, number of air compressor operating sets in the air supply system of the coal preparation plant in each period of the historical time, and the actual optimized values ​​of the air supply system operating parameters in each period, and construct a training set and a test set, wherein the actual optimized values ​​of the operating parameters include: the number of air compressor startups, the air compressor operating frequency, and the opening of the air valve; Step F2: using the filter press operating sets, dust removal system operating parameters, air filter operating sets and calibrated terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the coal preparation plant air system in each period in the training set as input, using the actual optimized values ​​of the air supply system operating parameters in each period as output, and using the genetic algorithm to optimize the initial BP neural network parameters to obtain the optimal initial threshold and weight of the neural network, wherein the mean square error is used as the fitness function of the genetic algorithm; Step F3: Input the filter press operating sets, dust removal system operating parameters, air filter operating sets and corrected terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system of the coal preparation plant in each period of the test set into the BP neural network with the optimal initial threshold and weight, and obtain the predicted values ​​of the air supply system operating parameters in each period output by the BP neural network; Step F4: determine whether the error between the predicted value of the air supply system operating parameter of each time period output by the BP neural network and the actual optimized value of the air supply system operating parameter of each time period in the test set is less than a first threshold value. If so, use the optimal initial threshold value and weight as the optimal parameter of the BP neural network to obtain the optimized BP neural network, and use the optimized BP neural network as the initial operating parameter optimization model, and enter step F5; otherwise, update the optimal initial threshold value and weight, and return to step F3; Step F5: Perform fuzzy correction on the initial operating parameter optimization model to obtain the operating parameter optimization model.

6. The method according to claim 5, characterized in that The fuzzy correction is performed on the initial operation parameter optimization model to obtain the operation parameter optimization model, including: Step E1: Determine the Lagrange multiplier of each sample in the training set; Step E2: The first step is to remove a preset proportion of sample data in the training set whose Lagrange multiplier is less than the first threshold value, and obtain the The training set after elimination is ; Step E3: Based on the The initial operating parameter optimization model is trained with the training set after the first elimination to obtain the The optimization model of operating parameters after the second calibration; Step E4: Determination Is it equal to 3? If so, The operating parameter optimization model after the correction is used as the operating parameter optimization model, otherwise, let = +1, and return to step E2.

7. An operating parameter optimization control system for an air supply system in a coal preparation plant, characterized in that: The system comprises: The acquisition module is used to obtain the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets, and the wind speed, pressure, temperature, and number of air compressor operating sets of the terminal system in the air supply system in the coal preparation plant during the period to be optimized; A correction module, used to correct the terminal system wind speed of the air supply system in the period to be optimized to obtain a corrected terminal system wind speed; A determination module is used to input the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and the corrected terminal system wind speed, pressure, temperature, and number of air compressor operating sets in the air supply system of the coal preparation plant into a pre-trained operating parameter optimization model to obtain reference values ​​of the operating parameters of the air supply system in the period to be optimized; An optimization control module, used for optimizing and controlling the operating parameters of the air supply system based on the reference values ​​of the operating parameters of the air supply system; Among them, the operating parameter optimization model is a BP neural network model, which is obtained by training the initial BP neural network based on the filter press operating sets, dust removal system operating parameters, air compressor filter operating sets and the calibrated terminal system wind speed, pressure, temperature, number of air compressor operating sets in the air supply system of the coal preparation plant in each period within the historical time period, as well as the actual optimized values ​​of the air supply system operating parameters in each period.

8. The system according to claim 7, characterized in that The terminal system wind speed is collected by using an anemometer; The operating parameters include: the number of air compressors started, the operating frequency of the air compressors, and the opening of the air valves.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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