Limestone slurry density prediction method and device, storage medium and electronic equipment

Abstract

The invention relates to a limestone slurry density prediction method and device, a storage medium and electronic equipment, and relates to the technical field of wet desulphurization, and the method comprises the following steps: firstly, obtaining characteristic data related to limestone slurry, the characteristic data comprises the liquid level of a limestone slurry box, the current value of a limestone slurry pump, the instantaneous flow rate of limestone slurry and the instantaneous flow rate of limestone powder; and on the basis of the characteristic data, predicting the density of the limestone slurry by using a support vector machine model of particle swarm optimization. According to the technical scheme, on the basis of the characteristic data related to the limestone slurry, the density of the limestone slurry is predicted in real time through the particle swarm optimization support vector machine algorithm, the accuracy of measuring the density of the limestone slurry is improved, a basis is provided for production, and therefore the density of the limestone slurry can be accurately measured by controlling the density of the limestone slurry. The desulfurization efficiency of the unit and the economical efficiency in the desulfurization process are improved.

Description

Technical Field

[0001] The present application relates to the technical field of wet desulfurization, and in particular to a method, device, storage medium, and electronic device for predicting the density of limestone slurry. Background Art

[0002] In the limestone wet desulfurization system of thermal power plants, limestone slurry acts as an absorbent to react with sulfur dioxide in flue gas to generate calcium sulfite or calcium sulfate, thereby achieving the purpose of desulfurization. If the density of limestone slurry is not well controlled during the desulfurization operation, it will seriously affect the desulfurization efficiency of the unit and the economy of the desulfurization process.

[0003] At present, the density of limestone slurry can be measured by nuclear radiation densitometer, differential pressure densitometer, mass densitometer, tuning fork densitometer and other measurement methods. However, these methods are easily affected by corrosion, suspended particles and scaling in the use environment, and the measurement accuracy is poor. Summary of the invention

[0004] In view of this, the present application provides a method, device, storage medium and electronic device for predicting the density of limestone slurry, the main purpose of which is to improve the technical problem that the limestone slurry density measurement method in the current prior art is easily affected by the environment, resulting in poor measurement accuracy.

[0005] In a first aspect, the present application provides a method for predicting the density of limestone slurry, comprising:

[0006] Acquiring characteristic data related to limestone slurry, the characteristic data including limestone slurry tank level, limestone slurry pump current value, limestone slurry instantaneous flow rate and limestone powder instantaneous flow rate;

[0007] Based on the characteristic data, a support vector machine (SVM) model optimized by particle swarm optimization is used to predict the density of the limestone slurry.

[0008] Optionally, before obtaining characteristic data related to the limestone slurry, the method further includes:

[0009] The factors affecting the density of limestone slurry were determined by Pierce correlation analysis.

[0010] The step of obtaining characteristic data related to limestone slurry includes:

[0011] Based on the factors affecting the density of the limestone slurry, the characteristic data is retrieved from a distributed control system (DCS).

[0012] Optionally, the training process of the support vector machine model includes:

[0013] Retrieve multiple sets of characteristic data at different time periods and under different loads from DCS to construct the target data set;

[0014] Based on the target data set, a particle swarm optimization algorithm (PSO) is used to perform model training to obtain the support vector machine model.

[0015] Optionally, the performing model training based on the target data set using a particle swarm optimization algorithm to obtain the support vector machine model includes:

[0016] According to the regularization parameters and kernel function parameters, the particle swarm optimization algorithm is used to perform iterative optimization to obtain the optimal regularization parameters and the optimal kernel function parameters;

[0017] According to the target data set, model training is performed based on the optimal regularization parameters and the optimal kernel function parameters to obtain the support vector machine model.

[0018] Optionally, the iterative optimization using a particle swarm optimization algorithm according to the regularization parameter and the kernel function parameter to obtain the optimal regularization parameter and the optimal kernel function parameter includes:

[0019] Initialize various parameters in the particle swarm optimization algorithm according to the regularization parameter and the kernel function parameter;

[0020] Iteratively updating various parameters set based on initialization to obtain the global optimal value of the particles in the particle swarm;

[0021] The optimal regularization parameter and the optimal kernel function parameter are determined according to the optimization parameters corresponding to the global optimal value.

[0022] Optionally, the iterative updating of various parameters based on the initialization settings to obtain the global optimal value of the particles in the particle swarm includes:

[0023] In each iterative update, the parameters corresponding to the particles are evaluated by absolute residual, mean square error and / or model determination coefficient to obtain the fitness value of each particle;

[0024] Update the position and velocity of individual particles in the particle swarm, and calculate the updated fitness value;

[0025] According to the fitness value, the global optimal value of the particle is updated.

[0026] Optionally, predicting the density of the limestone slurry based on the characteristic data using a support vector machine model optimized by a particle swarm, comprises:

[0027] The characteristic data and the range corresponding to the characteristic data are input into the support vector machine model to obtain the density of the limestone slurry.

[0028] In a second aspect, the present application provides a device for predicting the density of limestone slurry, comprising:

[0029] An acquisition module is configured to acquire characteristic data related to limestone slurry, wherein the characteristic data includes a limestone slurry tank level, a limestone slurry pump current value, a limestone slurry instantaneous flow rate, and a limestone powder instantaneous flow rate;

[0030] The prediction module is configured to predict the density of the limestone slurry based on the feature data by using a support vector machine model optimized by a particle swarm.

[0031] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0032] In a fourth aspect, the present application provides an electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0033] By means of the above technical scheme, the present application provides a method, device, storage medium and electronic device for predicting the density of limestone slurry. Specifically, firstly, characteristic data related to limestone slurry is obtained, wherein the characteristic data includes the liquid level of limestone slurry tank, the current value of limestone slurry pump, the instantaneous flow rate of limestone slurry and the instantaneous flow rate of limestone powder; then, based on the characteristic data, the density of limestone slurry is predicted using a support vector machine model optimized by particle swarm. Compared with the current prior art, by applying the technical scheme of the present application, based on the characteristic data related to limestone slurry, the density of limestone slurry is predicted in real time using a support vector machine algorithm optimized by particle swarm, thereby improving the accuracy of measuring the density of limestone slurry, providing a basis for production, and further, by controlling the density of limestone slurry, the desulfurization efficiency of the unit and the economy of the desulfurization process can be improved.

[0034] 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

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0037] Figure 1 A schematic flow chart of a method for predicting density of limestone slurry provided in an embodiment of the present application is shown;

[0038] Figure 2 A schematic flow chart of another method for predicting the density of limestone slurry provided in an embodiment of the present application is shown;

[0039] Figure 3 A schematic diagram showing an application example provided by an embodiment of the present application;

[0040] Figure 4 A schematic structural diagram of a limestone slurry density prediction device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0041] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0042] In order to improve the technical problem that the limestone slurry density measurement method in the prior art is easily affected by the environment, resulting in poor measurement accuracy. This embodiment provides a method for predicting the density of limestone slurry, such as Figure 1 As shown, the method includes:

[0043] Step 101: Obtain characteristic data related to limestone slurry.

[0044] Among them, the characteristic data include the limestone slurry tank level, the limestone slurry pump current value, the limestone slurry instantaneous flow rate and the limestone powder instantaneous flow rate.

[0045] In some examples, the limestone slurry tank level may affect the limestone slurry density. The limestone slurry tank level can be measured by ultrasonic level gauges, radar level gauges, float level gauges, etc. Among them, the ultrasonic level gauge measures the liquid level by transmitting and receiving ultrasonic signals, which is suitable for corrosive and conductive liquids; the radar level gauge measures the liquid level by transmitting and receiving radar waves, which is suitable for high temperature and high pressure environments; the float level gauge measures the liquid level by the position of the float ball as the liquid level changes, which is suitable for a smaller range of liquid level changes. If the slurry density increases, the weight of the same volume of slurry increases, and the liquid level may drop; if the slurry density decreases, the weight of the same volume of slurry decreases, and the liquid level may rise.

[0046] In some examples, the current value of the limestone slurry pump is related to the density of the slurry pumped by the limestone slurry. The greater the density, the greater the load on the pump and the higher the current value. The limestone slurry pump current passing through the motor can be measured by a current transformer installed on the motor cable, or the motor current value can be directly read by an ammeter. Abnormal changes in the current value can warn of changes in the slurry density, adjust the slurry density in time, and avoid overloading or damage to the limestone slurry pump.

[0047] In some examples, the instantaneous flow rate of limestone slurry affects the density of limestone slurry. The greater the flow rate, the higher the slurry density. The instantaneous flow rate of limestone slurry can be calculated by using an electromagnetic flowmeter by measuring the electromotive force generated when the fluid passes through a magnetic field, or by using an ultrasonic flowmeter by measuring the propagation time of ultrasonic waves in the fluid, or by using a vortex flowmeter by measuring the frequency generated by the fluid passing through a vortex. Abnormal changes in the instantaneous flow rate of limestone slurry may indicate that the slurry density is unstable and the slurry preparation system needs to be adjusted. Too low a flow rate may result in a decrease in desulfurization efficiency, and too high a flow rate may increase energy consumption.

[0048] In some examples, the instantaneous flow rate of limestone powder affects the density of limestone slurry. The greater the flow rate, the higher the slurry density. The instantaneous flow rate of limestone powder can be calculated by measuring the mass flow rate of the fluid using a mass flow meter; the instantaneous flow rate of limestone powder can be calculated by measuring the rotation speed of the fluid passing through the rotor using a rotor flow meter; and the instantaneous flow rate of limestone powder can also be calculated by measuring the rotation speed of the screw feeder using a screw feeder. Abnormal changes in the flow rate of limestone powder will affect the control of slurry density, and thus affect the desulfurization efficiency. If the flow rate is too low, the desulfurization effect may be poor.

[0049] Step 102: Based on the feature data, the density of the limestone slurry is predicted using a particle swarm optimized support vector machine model.

[0050] In some examples, the feature data can be preprocessed first, including normalizing all feature data to eliminate dimensional effects, filling or deleting missing values, and identifying and processing outliers to ensure the accuracy of the data, thereby effectively predicting the density of the limestone slurry. This embodiment combines the global search capability of the particle swarm optimization algorithm and the powerful learning ability of the support vector machine model to improve the prediction accuracy and generalization ability of the model. The support vector machine model trained by the particle swarm optimization algorithm is deployed in the actual system to predict the density of the limestone slurry in real time. It is then convenient to adjust the parameters of the slurry preparation system in a timely manner according to the prediction results to keep the density of the limestone slurry within the optimal range.

[0051] This embodiment first obtains characteristic data related to limestone slurry, wherein the characteristic data includes the limestone slurry tank level, limestone slurry pump current value, limestone slurry instantaneous flow rate and limestone powder instantaneous flow rate; then based on the characteristic data, the density of limestone slurry is predicted using a particle swarm optimized support vector machine model. Due to the influence of the working environment, in the prior art method for measuring the density of limestone slurry, the mass density meter has a service life of less than one year; the differential pressure density meter has a long service life, but cannot measure continuously, and has many equipment defects; the nuclear density meter has many restrictions on use. Compared with the current prior art, this embodiment is based on the characteristic data related to limestone slurry, and uses a particle swarm optimized support vector machine algorithm to predict the density of limestone slurry in real time, thereby improving the accuracy of measuring the density of limestone slurry, providing a basis for production, and further improving the desulfurization efficiency of the unit and the economy of the desulfurization process by controlling the density of limestone slurry.

[0052] Further, as a refinement and extension of the above embodiment, in order to fully illustrate the specific implementation process of the method of this embodiment, this embodiment provides the following Figure 2 The specific method shown includes:

[0053] Step 201: Acquire characteristic data related to limestone slurry.

[0054] Among them, the characteristic data include the limestone slurry tank level, the limestone slurry pump current value, the limestone slurry instantaneous flow rate and the limestone powder instantaneous flow rate.

[0055] Exemplarily, DCS is a control system widely used in the field of industrial automation. It manages complex production processes through decentralized controllers and centralized monitoring systems. Various process parameters such as temperature, pressure, flow, liquid level, etc. can be collected in real time through sensors and measuring equipment. These data are transmitted to the central control room for operators to monitor and analyze, ensure the real-time and accuracy of the data, and promptly discover and handle abnormal situations. In this embodiment, characteristic data related to the current limestone slurry can be retrieved from the DCS. Specifically, instruments such as level meters, ammeters, flow meters, etc. for collecting characteristic data can be connected to a data acquisition system (such as DCS), and then the sampling frequency can be set, such as collecting data once per second or every minute. And the collected characteristic data is stored in a database or file system for subsequent analysis. In this process, outliers and noise can also be removed to ensure the accuracy and reliability of the data. Further, through statistical analysis and machine learning methods, the laws and trends in the data are mined to optimize the operating system.

[0056] Optionally, the method of this embodiment may further include: determining factors affecting the density of limestone slurry through Pierce correlation analysis; accordingly, step 201 may specifically include: retrieving characteristic data from a distributed control system DCS based on the factors affecting the density of limestone slurry.

[0057] In some examples, Pierce Correlation Analysis can be used in MATLAB to calculate the factors that affect the density of limestone slurry, that is, to find the set of independent variables that have the strongest explanatory power for the dependent variable. The factors and their ranges that affect the density of limestone slurry calculated by correlation analysis are shown in Table 1:

[0058] Table 1

[0059]

[0060] Step 202: Input the feature data and the range corresponding to the feature data into a support vector machine model to obtain the density of the limestone slurry.

[0061] In some examples, the support vector machine model is a classification and regression tool.

[0062] Optionally, the training process of the support vector machine model may specifically include: retrieving multiple sets of feature data at different time periods and under different loads from the DCS to construct a target data set; based on the target data set, using a particle swarm optimization algorithm to perform model training to obtain a support vector machine model.

[0063] For example, 60 to 100 sets of data at different time periods and loads can be retrieved in the desulfurization control DCS system, including four variables (limestone slurry tank level, limestone slurry pump current, limestone slurry instantaneous flow rate, limestone powder instantaneous flow rate) and the corresponding values ​​of limestone slurry density. The libsvm toolbox is used in MATLAB to model the mathematical relationship between the above four variables and the limestone slurry density. The particle swarm optimization algorithm is used to treat each particle as a potential solution, and the optimal solution is approached by continuously iteratively updating the position and speed. The trained model is deployed in the actual system to predict the density of limestone slurry in real time, which can not only improve the accuracy of the prediction, but also help optimize the operation of the desulfurization system.

[0064] Optionally, the above-mentioned model training using a particle swarm optimization algorithm based on the target data set to obtain a support vector machine model may specifically include: iterative optimization using a particle swarm optimization algorithm according to regularization parameters and kernel function parameters to obtain optimal regularization parameters and optimal kernel function parameters; model training based on the optimal regularization parameters and optimal kernel function parameters according to the target data set to obtain a support vector machine model.

[0065] In some examples, the kernel functions commonly used in support vector machine models include linear kernels, polynomial kernels, RBF kernels, etc. The main parameters include the penalty parameter C that controls the degree of fit of the model to the training data and the kernel function parameter σ that controls the smoothness of the decision boundary. The performance of the model depends largely on the selection of its parameters, which directly affects the complexity and generalization ability of the model. If the parameters are set improperly, the model may be overfitted or underfitted, thereby affecting the prediction accuracy. Therefore, it is necessary to use the PSO toolbox algorithm in the MATLAB environment to optimize the parameters (C, σ). By using the particle swarm optimization algorithm to optimize these parameters, the prediction accuracy and generalization ability of the model can be significantly improved. The automated parameter optimization process can not only reduce the time and effort of manual trial and error, but also automatically find the optimal parameter combination to improve the performance of the model.

[0066] Optionally, the above-mentioned iterative optimization using a particle swarm optimization algorithm according to the regularization parameters and the kernel function parameters to obtain the optimal regularization parameters and the optimal kernel function parameters may specifically include: initializing various parameters in the particle swarm optimization algorithm according to the regularization parameters and the kernel function parameters; iteratively updating various parameters based on the initialization settings to obtain the global optimal value of particles in the particle swarm; and determining the optimal regularization parameters and the optimal kernel function parameters based on the optimization parameters corresponding to the global optimal value.

[0067] In some examples, such as Figure 3As shown in the figure, a set of initial solutions (particles) are randomly generated, each particle represents a set of SVM parameters, and the velocity and position of the particle are initialized. For each particle, the SVM model is trained using the corresponding parameters, the individual optimal position and the global optimal position of each particle are updated, and the velocity and position of the particle are updated according to the global optimal position and the individual optimal position. Repeat the above steps until the maximum number of iterations is reached or the convergence condition is met, that is, by iteratively updating the position and velocity of the particle, the parameter combination that makes the model performance optimal is found. At this time, the optimization parameters corresponding to the global optimal value are the optimal parameters, including the optimal regularization parameters and the optimal kernel function parameters.

[0068] Optionally, the above-mentioned iterative update of various parameters based on the initialization settings to obtain the global optimal value of the particles in the particle swarm includes: in each iterative update, evaluating the parameters corresponding to the particles through absolute residual, mean squared error (MSE) and / or model determination coefficient to obtain the fitness value of each particle; updating the position and speed of individual particles in the particle swarm, and calculating the updated fitness value; updating the global optimal value of the particle according to the fitness value.

[0069] For example, Figure 3 As shown, the fitness function value of the particle is calculated. If the current fitness function value is better than the individual optimal value, the individual optimal value and position are updated. If the current fitness function value is better than the global optimal value, the global optimal value and position are updated. The fitness function can be used to evaluate the performance of the support vector machine model. The particle swarm optimized support vector machine (PSO-SVM) model can usually use the absolute residual δ and the mean square error MSE and / or the model determination coefficient R 2 As the criterion, the particle's speed and position are updated according to the particle's current position, individual optimal position, and personal optimal position.

[0070] In some examples, the results of inputting the characteristic data related to limestone slurry into the original support vector machine model are: Mean squared error = 0.135792 (regression), Squared correlation coefficient = 0.434852 (regression); Mean squared error = 0.565684 (regression), Squared correlation coefficient = 0.00622 (regression). The results of inputting the same characteristic data into the support vector machine model after particle swarm optimization are: Mean squared error = 0.00323137 (regression), Squared correlation coefficient = 0.490683 (regression); Mean squared error = 0.00391151 (regression); Squared correlation coefficient = 0.288028 (regression). By comparing the calculation results, it can be seen that the results after particle swarm optimization are better than the calculation results of the original support vector machine, which proves the effectiveness of the particle swarm optimization support vector machine model.

[0071] Compared with the current prior art, this embodiment retrieves feature data with a high correlation with limestone slurry density in the DCS system, uses a support vector machine algorithm in the MATLAB environment to construct a mathematical model between feature data with a high correlation with limestone slurry density and limestone slurry density, and performs real-time prediction of limestone slurry density as a supplement and correction to the existing limestone slurry density measurement method. The method of this embodiment avoids the problems of existing measurement methods being greatly affected by the use environment, having many equipment defects, large spare parts loss, and long measurement cycles, improves the accuracy of measuring limestone slurry density, provides a basis for production, and further can improve the desulfurization efficiency of the unit and the economy of the desulfurization process by controlling the limestone slurry density.

[0072] Further, as Figure 1 The specific implementation of the method shown in the embodiment provides a device for predicting the density of limestone slurry, such as Figure 4 As shown, the device includes: an acquisition module 31 and a prediction module 32.

[0073] An acquisition module 31 is configured to acquire characteristic data related to limestone slurry, wherein the characteristic data includes a limestone slurry tank level, a limestone slurry pump current value, a limestone slurry instantaneous flow rate, and a limestone powder instantaneous flow rate;

[0074] The prediction module 32 is configured to predict the density of the limestone slurry based on the feature data by using a support vector machine model optimized by particle swarm optimization.

[0075] In some examples, the factors affecting the density of the limestone slurry are determined by Pierce correlation analysis; accordingly, the acquisition module 31 is specifically configured to retrieve the characteristic data from the DCS based on the factors affecting the density of the limestone slurry.

[0076] In some examples, the prediction module 32 is specifically configured to retrieve multiple sets of feature data at different time periods and under different loads from the DCS to construct a target data set; based on the target data set, a particle swarm optimization algorithm is used to perform model training to obtain the support vector machine model.

[0077] In some examples, the prediction module 32 is further configured to perform iterative optimization using a particle swarm optimization algorithm based on the regularization parameters and the kernel function parameters to obtain the optimal regularization parameters and the optimal kernel function parameters; and perform model training based on the optimal regularization parameters and the optimal kernel function parameters according to the target data set to obtain the support vector machine model.

[0078] In some examples, the prediction module 32 is further configured to initialize various parameters in the particle swarm optimization algorithm according to the regularization parameter and the kernel function parameter; perform iterative updates based on the various parameters initialized to obtain the global optimal value of the particles in the particle swarm; and determine the optimal regularization parameter and the optimal kernel function parameter based on the optimization parameter corresponding to the global optimal value.

[0079] In some examples, the prediction module 32 is further configured to evaluate the parameters corresponding to the particles by absolute residual, mean square error and / or model determination coefficient in each iterative update to obtain the fitness value of each particle; update the position and velocity of individual particles in the particle group, and calculate the updated fitness value; and update the global optimal value of the particle based on the fitness value.

[0080] In some examples, the prediction module 32 is further configured to input the feature data and the range corresponding to the feature data into the support vector machine model to obtain the density of the limestone slurry.

[0081] Based on the above Figure 1 and Figure 2 The method shown in the embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Figure 1 and Figure 2 The method shown.

[0082] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0083] Based on the above Figure 1 and Figure 2 The method shown, and Figure 4 In order to achieve the above-mentioned purpose, the embodiment of the present application further provides an electronic device, which may include a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 and Figure 2 The method shown.

[0084] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0085] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0086] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0087] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware. By applying the solution of this embodiment, the existing measurement methods are avoided from being greatly affected by the use environment, having many equipment defects, having large spare parts loss, and having a long measurement cycle. Based on the characteristic data related to the limestone slurry, the support vector machine algorithm optimized by particle swarm is used to predict the density of the limestone slurry in real time, thereby improving the accuracy of measuring the density of the limestone slurry and providing a basis for production. Furthermore, the desulfurization efficiency of the unit and the economy of the desulfurization process can be improved by controlling the density of the limestone slurry.

[0088] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0089] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can 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 these embodiments herein, but will conform to the widest scope consistent with the principles and novel features applied for herein.

Claims

1. A method for predicting the density of limestone slurry, characterized in that: include: Acquiring characteristic data related to limestone slurry, the characteristic data including limestone slurry tank level, limestone slurry pump current value, limestone slurry instantaneous flow rate and limestone powder instantaneous flow rate; Based on the characteristic data, the density of the limestone slurry is predicted using a support vector machine model optimized by particle swarm optimization.

2. The method according to claim 1, characterized in that: Before obtaining characteristic data related to the limestone slurry, the method further includes: The factors affecting the density of limestone slurry were determined by Pierce correlation analysis. The step of obtaining characteristic data related to limestone slurry includes: Based on the factors affecting the density of the limestone slurry, the characteristic data is retrieved from a distributed control system DCS.

3. The method according to claim 1, characterized in that: The training process of the support vector machine model includes: Retrieve multiple sets of characteristic data at different time periods and under different loads from DCS to construct the target data set; Based on the target data set, a particle swarm optimization algorithm is used to perform model training to obtain the support vector machine model.

4. The method according to claim 3, characterized in that The method of performing model training based on the target data set using a particle swarm optimization algorithm to obtain the support vector machine model includes: According to the regularization parameters and kernel function parameters, the particle swarm optimization algorithm is used to perform iterative optimization to obtain the optimal regularization parameters and the optimal kernel function parameters; According to the target data set, model training is performed based on the optimal regularization parameters and the optimal kernel function parameters to obtain the support vector machine model.

5. The method according to claim 4, characterized in that The method of iteratively optimizing the regularization parameters and the kernel function parameters using the particle swarm optimization algorithm to obtain the optimal regularization parameters and the optimal kernel function parameters includes: Initialize various parameters in the particle swarm optimization algorithm according to the regularization parameter and the kernel function parameter; Iteratively updating various parameters set based on initialization to obtain the global optimal value of the particles in the particle swarm; The optimal regularization parameter and the optimal kernel function parameter are determined according to the optimization parameters corresponding to the global optimal value.

6. The method according to claim 5, characterized in that The iterative updating of various parameters based on the initialization settings to obtain the global optimal value of the particles in the particle swarm includes: In each iterative update, the parameters corresponding to the particles are evaluated by absolute residual, mean square error and / or model determination coefficient to obtain the fitness value of each particle; Update the position and velocity of individual particles in the particle swarm, and calculate the updated fitness value; According to the fitness value, the global optimal value of the particle is updated.

7. The method according to claim 1, characterized in that The method of predicting the density of the limestone slurry based on the characteristic data using a support vector machine model optimized by a particle swarm, comprises: The characteristic data and the range corresponding to the characteristic data are input into the support vector machine model to obtain the density of the limestone slurry.

8. A device for predicting the density of limestone slurry, characterized in that: include: An acquisition module is configured to acquire characteristic data related to limestone slurry, wherein the characteristic data includes a limestone slurry tank level, a limestone slurry pump current value, a limestone slurry instantaneous flow rate, and a limestone powder instantaneous flow rate; The prediction module is configured to predict the density of the limestone slurry based on the feature data by using a support vector machine model optimized by particle swarm optimization.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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