A method and system for calculating oxidation rate by monitoring oxidation air numerical value

Abstract

The present invention relates to a method and system for numerically calculating oxidation rate by monitoring oxidation air, and belongs to the field of oxidation rate detection technology. The method includes obtaining historical oxidation data, constructing an oxidation data set according to the historical oxidation data, establishing an oxidation decision function, and constructing an initial oxidation decision solution equation by introducing insensitive loss and relaxation variables, converting the initial oxidation decision solution equation through Lagrangian function to obtain an oxidation decision solution equation, obtaining an ammonium sulfite oxidation rate regression equation through radial basis kernel function according to the oxidation decision solution equation, and obtaining an ammonium sulfite oxidation rate calculation model through opposition search optimization; real-time monitoring of oxidation wind data, outputting an ammonium sulfite oxidation rate calculation value through the ammonium sulfite oxidation rate calculation model according to the monitored oxidation wind data and the current absorbed dose, and the present invention realizes real-time and accurate calculation of ammonium sulfite oxidation rate, reduces the cumbersome operation of sampling titration method, and reduces labor intensity.

Description

Technical Field

[0001] The invention belongs to the technical field of oxidation rate detection, and in particular relates to a method and a system for calculating oxidation rate by monitoring oxidation air numerical values. Background Art

[0002] Ammonia desulfurization is a commonly used desulfurization method in industrial production. Ammonia is usually used as an absorbent to react with SO2 in the flue gas to produce ammonium sulfite, which is then oxidized to produce stable ammonium sulfate. In this process, oxidation rate detection is required to determine whether all the ammonium sulfite produced by the absorbent has been oxidized.

[0003] Oxidation rate detection is a key step in industrial production for evaluating and controlling chemical reaction processes. Currently, oxidation rate detection is mainly carried out by sampling titration. Although this method can meet the needs of oxidation rate detection to a certain extent, it has the following disadvantages:

[0004] The sampling titration method requires professionals to frequently sample the oxidation system in the production process. The sampling process is cumbersome, complex, labor-intensive, time-consuming and error-prone. Moreover, since the sampling titration method requires a certain amount of time to complete the processing and analysis of the sample, the oxidation rate detection results have a certain lag and cannot reflect the oxidation status of the production process in real time. In addition, the sampling titration method requires the use of a large amount of chemical reagents, which not only increases production costs, but also has a certain impact on the environment.

[0005] In view of the problems existing in the above oxidation rate detection method, such as high labor intensity, delayed detection results and reliance on chemical titration, there is an urgent need to develop a new method and system for measuring oxidation rate. Summary of the invention

[0006] In order to solve the above problems existing in the prior art, the present invention proposes a method and system for calculating the oxidation rate by monitoring the oxidation air value.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for calculating oxidation rate by monitoring oxidation air numerical value, comprising:

[0009] Acquire historical oxidation data, wherein the historical oxidation data includes historical oxidation air inlet oxygen content, historical oxidation air outlet oxygen content, historical oxidation air volume, historical absorption dose, and historical ammonium sulfite oxidation rate;

[0010] Constructing an ammonium sulfite oxidation rate estimation model based on the historical oxidation data by using an adversarial search regression algorithm;

[0011] The step of constructing the ammonium sulfite oxidation rate calculation model comprises:

[0012] constructing an oxidation data set based on the historical oxidation data;

[0013] Obtaining an oxidation training set and an oxidation test set by randomly dividing the oxidation data set;

[0014] constructing an ammonium sulfite oxidation rate regression equation through high-dimensional mapping according to the oxidation training set;

[0015] According to the ammonium sulfite oxidation rate regression equation, an ammonium sulfite oxidation rate calculation model is obtained through opposition search optimization;

[0016] The steps of the opposition search optimization include:

[0017] Define the number of search agents;

[0018] Randomly initialize search agent parameter settings, wherein the search agent parameters include a penalty factor value and a kernel function value;

[0019] Preset initial search step size;

[0020] generating a reverse search agent parameter setting according to the search agent through an adversarial learning strategy;

[0021] Obtaining a global optimal parameter combination through fitness calculation according to the reverse search proxy parameter settings;

[0022] Substituting the global optimal parameter combination into the ammonium sulfite oxidation rate regression equation to obtain the ammonium sulfite oxidation rate calculation model;

[0023] The oxidation wind data are monitored in real time, wherein the oxidation wind data include the oxidation wind inlet oxygen content, the oxidation wind outlet oxygen content, and the oxidation wind volume. The current ammonium sulfite oxidation rate is obtained through the ammonium sulfite oxidation rate calculation model according to the oxidation wind data and the current absorbed dose.

[0024] Preferably, constructing an ammonium sulfite oxidation rate regression equation through high-dimensional mapping according to the oxidation training set comprises:

[0025] An oxidation decision function is established according to the oxidation training set, and the mathematical expression of the oxidation decision function is:

[0026] ,

[0027] Among them, g(a) is the oxidation decision function, r is the oxidation inertia factor, is the oxidation nonlinear mapping function, b is the bias, and a is the oxidation input feature vector;

[0028] According to the oxidation decision function, an oxidation decision solution equation is constructed by maximizing the objective function;

[0029] According to the oxidation decision solving equation, a regression equation of ammonium sulfite oxidation rate is obtained through a radial basis kernel function.

[0030] Preferably, constructing an oxidation decision solution equation according to the oxidation decision function through the maximum value of the objective function includes:

[0031] The initial oxidation decision solution equation is constructed by introducing insensitive loss and relaxation variables;

[0032] The constraint expression of the initial oxidation decision solution equation is:

[0033] ,

[0034] ,

[0035] ,

[0036] Among them, a i is the oxidation feature vector of the i-th sample, g i is the true value of the oxidation rate of ammonium sulfite, is the insensitive loss, α(a i ) is the oxidation nonlinear mapping of the oxidation feature vector of the i-th sample, and is the slack variable, b is the bias;

[0037] The initial oxidation decision solution equation is converted into an oxidation decision solution equation through Lagrangian function.

[0038] Preferably, the mathematical expression of the regression equation of the ammonium sulfite oxidation rate is:

[0039] ,

[0040] ,

[0041] ,

[0042] Where f(a) is the regression equation for the oxidation rate of ammonium sulfite, K(a,a i ) is the radial basis kernel function, is the radial basis kernel parameter, is the radius of the radial basis kernel, a is the oxidation input eigenvector, a i is the oxidation feature vector of the i-th sample, x i and is the Lagrangian operator and b is the bias.

[0043] Preferably, obtaining the global optimal parameter combination by fitness calculation according to the reverse search proxy parameter setting includes:

[0044] Calculate the fitness value for each search agent parameter setting through cross-validation;

[0045] When the fitness value of the reverse search proxy parameter is higher than the fitness value of the initial search proxy parameter, the reverse search proxy parameter is used to update the initial search proxy parameter; otherwise, the initial search proxy parameter setting remains unchanged;

[0046] When the number of iterations reaches a preset maximum number of iterations, the search process is terminated to obtain a global optimal parameter combination, which includes an optimal penalty factor and an optimal kernel function.

[0047] A system for calculating oxidation rate by monitoring oxidation air numerical values, applying the above method for calculating oxidation rate by monitoring oxidation air numerical values, comprising an ammonium sulfite oxidation rate data acquisition module, an ammonium sulfite oxidation rate calculation model construction module, an oxidation rate real-time calculation module, and an oxidation rate visualization module;

[0048] The ammonium sulfite oxidation rate data acquisition module is used to collect the oxygen content of the oxidation air inlet, the oxygen content of the oxidation air outlet, and the oxidation air volume of the monitoring instrument;

[0049] The ammonium sulfite oxidation rate calculation model construction module is used to construct an ammonium sulfite oxidation rate calculation model according to the historical oxidation data through an opposition search regression algorithm;

[0050] The oxidation rate real-time calculation module is used to monitor the oxygen content of the oxidation air inlet, the oxygen content of the oxidation air outlet, the oxidation air volume and the current absorbed dose in real time and output the ammonium sulfite oxidation rate calculation value through the ammonium sulfite oxidation rate calculation model;

[0051] The oxidation rate visualization module is used to display real-time monitoring data and oxidation rate calculation results, so that operators can understand the production status.

[0052] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for calculating the oxidation rate by monitoring the oxidation air value is implemented.

[0053] A storage medium containing computer executable instructions, which are used to perform the above method of calculating the oxidation rate by monitoring the oxidation air value when executed by a computer processor.

[0054] The beneficial effects of the present invention are:

[0055] By real-time monitoring of the oxygen content at the inlet and outlet of the oxidation air, establishing the corresponding relationship between the oxygen content and the absorbed dose through the ammonium sulfite oxidation rate calculation model, and obtaining the current oxidation rate through the ammonium sulfite oxidation rate calculation model, the cumbersome operation of the sampling titration method is reduced, the reagent consumption and environmental pollution in the chemical titration detection process are avoided, the labor intensity is reduced, and the accuracy of the oxidation rate detection is improved. By continuously monitoring the oxygen content at the inlet and outlet of the oxidation air, the situation of the oxidation process can be understood in real time, which is helpful to timely adjust the production parameters, optimize the production process, and improve the production efficiency.

[0056] The ammonium sulfite oxidation rate calculation model was constructed through the adversarial search regression algorithm, and the adversarial search strategy was used to optimize the model parameter combination. The iterative optimization gradually approached the global optimal solution, thereby improving the stability and reliability of the model and the accuracy of the oxidation rate prediction. By introducing insensitive losses and slack variables, the overfitting problem caused by the high complexity of the model was effectively avoided. The radial basis kernel function can handle nonlinear problems, so that the model can adapt to more complex oxidation processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0058] Figure 1 The present invention is a flow chart of a method for calculating oxidation rate by monitoring oxidation air numerical value. DETAILED DESCRIPTION

[0059] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0060] See also Figure 1 , a method for calculating oxidation rate by monitoring oxidation air numerical value, comprising:

[0061] S1: Acquire historical oxidation data, wherein the historical oxidation data includes historical oxidation air inlet oxygen content, historical oxidation air outlet oxygen content, historical oxidation air volume, historical absorption dose, and historical ammonium sulfite oxidation rate;

[0062] S2: constructing an ammonium sulfite oxidation rate calculation model based on the historical oxidation data by using an opposition search regression algorithm;

[0063] S3: real-time monitoring of oxidation wind data, wherein the oxidation wind data includes oxidation wind inlet oxygen content, oxidation wind outlet oxygen content, and oxidation wind volume, and the current ammonium sulfite oxidation rate is obtained through the ammonium sulfite oxidation rate calculation model according to the oxidation wind data and the current absorbed dose.

[0064] In this embodiment, historical oxidation data is obtained by accessing a production database, wherein the production database is historical ammonium sulfite oxidation rate data recorded in industrial production.

[0065] In this embodiment, the construction of the ammonium sulfite oxidation rate calculation model by the opposition search regression algorithm according to the historical oxidation data is specifically implemented by the following steps:

[0066] S201: constructing an oxidation data set according to the historical oxidation data;

[0067] S202: Obtaining an oxidation training set and an oxidation test set by randomly dividing the oxidation data set;

[0068] S203: constructing an ammonium sulfite oxidation rate regression equation through high-dimensional mapping according to the oxidation training set;

[0069] S203-1: Establishing an oxidation decision function according to the oxidation training set, wherein the mathematical expression of the oxidation decision function is:

[0070] ,

[0071] Among them, g(a) is the oxidation decision function, r is the oxidation inertia factor, is the oxidation nonlinear mapping function, b is the bias, and a is the oxidation input feature vector;

[0072] S203-2: constructing an oxidation decision solution equation according to the oxidation decision function by maximizing the objective function;

[0073] S203-21: An initial oxidation decision solution equation is constructed by introducing insensitive loss and relaxation variables. The mathematical expression of the initial oxidation decision solution equation is:

[0074] ,

[0075] Among them, Q is the penalty factor, and is the slack variable, n is the total number of samples in the oxidation training set, and r is the oxidation inertia factor;

[0076] The constraint expression of the initial oxidation decision solution equation is:

[0077] ,

[0078] ,

[0079] ,

[0080] Among them, a i is the oxidation feature vector of the i-th sample, gi is the true value of the oxidation rate of ammonium sulfite, is the insensitive loss, α(a i ) is the oxidation nonlinear mapping of the oxidation feature vector of the i-th sample, and is the slack variable, b is the bias;

[0081] S203-22: Obtaining an oxidation decision solution equation by converting the initial oxidation decision solution equation through a Lagrangian function;

[0082] The mathematical expression of the oxidation decision solution equation is:

[0083] ,

[0084] The constraint condition expression of the oxidation decision solution equation is:

[0085] ,

[0086] ,

[0087] Among them, g i is the true value of the oxidation rate of ammonium sulfite, is the insensitive loss, n is the total number of oxidation training set samples, Solving the equation for the oxidation decision, α(a i ) is the oxidation nonlinear mapping of the oxidation feature vector of the i-th sample, α(a j ) is the oxidation nonlinear mapping of the jth sample oxidation feature vector, x i 、x j , , is the Lagrangian operator.

[0088] S203-3: Obtaining an ammonium sulfite oxidation rate regression equation through a radial basis kernel function according to the oxidation decision solving equation;

[0089] The mathematical expression of the regression equation of the ammonium sulfite oxidation rate is:

[0090] ,

[0091] ,

[0092] ,

[0093] Where f(a) is the regression equation for the oxidation rate of ammonium sulfite, K(a,a i ) is the radial basis kernel function, is the radial basis kernel parameter, is the radius of the radial basis kernel, a is the oxidation input eigenvector, a i is the oxidation feature vector of the i-th sample, x i and is the Lagrangian operator.

[0094] It should be noted that exp() is an exponential function, and ||a-ai||² is the square of the Euclidean distance between the oxidation input feature vector and the oxidation feature vector of the i-th sample, which is used to measure the degree of difference between them.

[0095] S204: Obtaining an ammonium sulfite oxidation rate calculation model through opposition search optimization according to the ammonium sulfite oxidation rate regression equation;

[0096] S204-1: Define the number of search agents;

[0097] S204-2: Randomly initialize search proxy parameter settings, where the search proxy parameters include a penalty factor value and a kernel function value;

[0098] S204-3: Preset initial search step length;

[0099] S204-4: generating a reverse search agent parameter setting according to the search agent through an adversarial learning strategy;

[0100] The mathematical expression of the adversarial learning is:

[0101] ,

[0102] Among them, A i_new For reverse search proxy parameters, A i To initialize the search agent parameters, L is the lower bound, U is the upper bound, and k is a random number between [0,1];

[0103] Specifically, the lower bound is a preset minimum value of the initialization search proxy parameter, and the upper bound is a preset maximum value of the initialization search proxy parameter.

[0104] S204-5: Obtaining a global optimal parameter combination through fitness calculation according to the reverse search proxy parameter settings;

[0105] Specifically, the fitness value of each search agent parameter setting is calculated through cross-validation, and the fitness value calculation adopts the negative value of mean square error;

[0106] In this embodiment, the cross validation adopts five-fold cross validation;

[0107] When the fitness value of the reverse search proxy parameter is higher than the fitness value of the initial search proxy parameter, the reverse search proxy parameter is used to update the initial search proxy parameter; otherwise, the initial search proxy parameter setting is kept unchanged.

[0108] When the number of iterations reaches a preset maximum number of iterations, the search process is terminated to obtain a global optimal parameter combination, wherein the global optimal parameter combination includes an optimal penalty factor and an optimal kernel function;

[0109] S204-6: Substituting the global optimal parameter combination into the ammonium sulfite oxidation rate regression equation to obtain the ammonium sulfite oxidation rate calculation model;

[0110] In this embodiment, oxygen content analyzers are installed at the oxidation air inlet and outlet respectively to monitor the oxygen content of the oxidation air inlet and outlet in real time; a flow meter is installed on the oxidation air duct to measure the oxidation air volume; and the ammonium sulfite oxidation rate calculation model is used to output the ammonium sulfite oxidation rate calculation value based on the monitored oxidation air inlet oxygen content, oxidation air outlet oxygen content, oxidation air volume and current absorbed dose.

[0111] It should be noted that the absorbent dosage is the amount of absorbent ammonia sent into the desulfurization device. The absorbent dosage is affected by the SO2 concentration in the flue gas and the flue gas flow rate. The higher the SO2 concentration and the greater the flue gas flow rate, the more ammonia is added. In actual operation, the absorbent is sent in batches to prevent ammonia escape.

[0112] The absorbent in the desulfurization device reacts with SO2 to generate ammonium sulfite, which is sent to the oxidation reaction device for further oxidation treatment to obtain ammonium sulfate. During the oxidation process of ammonium sulfite, the temperature in the oxidation reaction device is set to 52°C and the pH value is controlled between 6.5 and 7.5.

[0113] A system for calculating oxidation rate by monitoring oxidation air numerical values ​​comprises an ammonium sulfite oxidation rate data acquisition module, an ammonium sulfite oxidation rate calculation model construction module, an oxidation rate real-time calculation module, and an oxidation rate visualization module;

[0114] The ammonium sulfite oxidation rate data acquisition module is used to collect the oxygen content of the oxidation air inlet, the oxygen content of the oxidation air outlet, and the oxidation air volume of the monitoring instrument;

[0115] The ammonium sulfite oxidation rate calculation model construction module is used to construct an ammonium sulfite oxidation rate calculation model according to the historical oxidation data through an opposition search regression algorithm;

[0116] The oxidation rate real-time calculation module is used to monitor the oxygen content of the oxidation air inlet, the oxygen content of the oxidation air outlet, the oxidation air volume and the current absorbed dose in real time and output the ammonium sulfite oxidation rate calculation value through the ammonium sulfite oxidation rate calculation model;

[0117] The oxidation rate visualization module is used to display real-time monitoring data and oxidation rate calculation results, so that operators can understand the production status.

[0118] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0119] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0120] The program code included on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0121] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for calculating oxidation rate by monitoring oxidation air numerical value, characterized in that: include: Acquire historical oxidation data, wherein the historical oxidation data includes historical oxidation air inlet oxygen content, historical oxidation air outlet oxygen content, historical oxidation air volume, historical absorption dose, and historical ammonium sulfite oxidation rate; Constructing an ammonium sulfite oxidation rate estimation model based on the historical oxidation data by using an adversarial search regression algorithm; The step of constructing the ammonium sulfite oxidation rate calculation model comprises: constructing an oxidation data set based on the historical oxidation data; Obtaining an oxidation training set and an oxidation test set by randomly dividing the oxidation data set; constructing an ammonium sulfite oxidation rate regression equation through high-dimensional mapping according to the oxidation training set; According to the ammonium sulfite oxidation rate regression equation, an ammonium sulfite oxidation rate calculation model is obtained through opposition search optimization; The steps of the opposition search optimization include: Define the number of search agents; Randomly initialize search agent parameter settings, wherein the search agent parameters include a penalty factor value and a kernel function value; Preset initial search step size; generating a reverse search agent parameter setting according to the search agent through an adversarial learning strategy; Obtaining a global optimal parameter combination through fitness calculation according to the reverse search proxy parameter settings; Substituting the global optimal parameter combination into the ammonium sulfite oxidation rate regression equation to obtain the ammonium sulfite oxidation rate calculation model; The oxidation wind data are monitored in real time, wherein the oxidation wind data include the oxidation wind inlet oxygen content, the oxidation wind outlet oxygen content, and the oxidation wind volume. The current ammonium sulfite oxidation rate is obtained through the ammonium sulfite oxidation rate calculation model according to the oxidation wind data and the current absorbed dose.

2. The method for calculating oxidation rate by monitoring oxidation air numerical value according to claim 1, characterized in that: The step of constructing an ammonium sulfite oxidation rate regression equation through high-dimensional mapping according to the oxidation training set comprises: An oxidation decision function is established according to the oxidation training set, and the mathematical expression of the oxidation decision function is: ， Among them, g(a) is the oxidation decision function, r is the oxidation inertia factor, is the oxidation nonlinear mapping function, b is the bias, and a is the oxidation input feature vector; According to the oxidation decision function, an oxidation decision solution equation is constructed by maximizing the objective function; According to the oxidation decision solving equation, a regression equation of ammonium sulfite oxidation rate is obtained through a radial basis kernel function.

3. The method for calculating oxidation rate by monitoring oxidation air numerical value according to claim 2, characterized in that: The step of constructing an oxidation decision solution equation according to the oxidation decision function by using the maximum value of the objective function includes: The initial oxidation decision solution equation is constructed by introducing insensitive loss and relaxation variables; The constraint expression of the initial oxidation decision solution equation is: ， ， ， Among them, a i is the oxidation feature vector of the i-th sample, g i is the true value of the oxidation rate of ammonium sulfite, is the insensitive loss, α(a i ) is the oxidation nonlinear mapping of the oxidation feature vector of the i-th sample, and is the slack variable, b is the bias; The initial oxidation decision solution equation is converted into an oxidation decision solution equation through Lagrangian function.

4. The method for calculating oxidation rate by monitoring oxidation air numerical value according to claim 2, characterized in that: The mathematical expression of the regression equation of the ammonium sulfite oxidation rate is: ， ， ， Where f(a) is the regression equation for the oxidation rate of ammonium sulfite, K(a,a i ) is the radial basis kernel function, is the radial basis kernel parameter, is the radius of the radial basis kernel, a is the oxidation input eigenvector, a i is the oxidation feature vector of the i-th sample, x i and is the Lagrangian operator and b is the bias.

5. The method for calculating oxidation rate by monitoring oxidation air numerical value according to claim 1, characterized in that: The obtaining of the global optimal parameter combination by fitness calculation according to the reverse search proxy parameter setting comprises: Calculate the fitness value for each search agent parameter setting through cross-validation; When the fitness value of the reverse search proxy parameter is higher than the fitness value of the initial search proxy parameter, the reverse search proxy parameter is used to update the initial search proxy parameter; otherwise, the initial search proxy parameter setting remains unchanged; When the number of iterations reaches a preset maximum number of iterations, the search process is terminated to obtain a global optimal parameter combination, which includes an optimal penalty factor and an optimal kernel function.

6. A system for calculating oxidation rate by monitoring oxidation air numerical value, characterized in that: The system applies the method for calculating the oxidation rate by numerically monitoring the oxidation air as described in claim 1, including an ammonium sulfite oxidation rate data acquisition module, an ammonium sulfite oxidation rate calculation model construction module, an oxidation rate real-time calculation module, and an oxidation rate visualization module; The ammonium sulfite oxidation rate data acquisition module is used to collect the oxygen content of the oxidation air inlet, the oxygen content of the oxidation air outlet, and the oxidation air volume of the monitoring instrument; The ammonium sulfite oxidation rate calculation model construction module is used to construct an ammonium sulfite oxidation rate calculation model according to the historical oxidation data through an opposition search regression algorithm; The oxidation rate real-time calculation module is used to monitor the oxygen content of the oxidation air inlet, the oxygen content of the oxidation air outlet, the oxidation air volume and the current absorbed dose in real time and output the ammonium sulfite oxidation rate calculation value through the ammonium sulfite oxidation rate calculation model; The oxidation rate visualization module is used to display real-time monitoring data and oxidation rate calculation results, so that operators can understand the production status.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for calculating the oxidation rate by monitoring the oxidation air value as described in any one of claims 1 to 5 is implemented.

8. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the method for calculating the oxidation rate by monitoring the oxidation air value as described in any one of claims 1 to 5.

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