Blast furnace injection coal blending combustion rate prediction method and system based on thermal analysis

Abstract

The invention discloses a blast furnace injection coal blending combustion rate prediction method and system based on thermal analysis, and relates to the technical field of combustion performance detection. According to the method, a thermal analysis test method is adopted for carrying out a combustion performance orthogonal test on pulverized coal, all combustion rate influence factor data and combustion rates in the test process are collected, and various parameters in the obtained influence factor data are used as evaluation indexes to construct an evaluation model to determine the quality evaluation value of the evaluation model. Judging the relationship between the pulverized coal combustion rate and the quality evaluation value, further determining an optimal influence factor and a worst influence factor, constructing a combustion rate prediction threshold value, taking a distance value between the optimal influence factor and the worst influence factor as a step length, and setting a classification gradient value to perform grade division on the distance value to obtain a combustion rate prediction result; and dividing the combustion rate prediction threshold value into a plurality of prediction intervals, and calculating the distance between the actual measurement value of the influence factor of the to-be-detected pulverized coal and the optimal influence factor to determine the prediction interval of the combustion rate of the to-be-detected pulverized coal.

Description

Technical Field

[0001] The present invention relates to the technical field of combustion performance detection, and in particular to a method and system for predicting the combustion rate of blast furnace coal injection based on thermal analysis. Background Art

[0002] Blast furnace pulverized coal injection is the direct injection of bituminous coal, anthracite or a mixture of the two into the furnace from the tuyere of the blast furnace to replace coke to provide heat and reduce the coke ratio and reduce the cost of pig iron. The key indicator of the pulverized coal injection process is the combustion rate of cents. Only by improving the combustion rate of pulverized coal injection can the pulverized coal in the furnace be fully and effectively utilized and the benefits of coal injection can be truly realized.

[0003] The existing prediction of pulverized coal combustion rate is mostly based on the physical and chemical properties of pulverized coal as research indicators, such as the type of pulverized coal, the proportion of pulverized coal, the volatile content, the pulverized coal particle size and other parameters on the combustion rate, ignoring the combustion environment factors and the degree of matching between pulverized coal and the environment on the pulverized coal combustion rate, resulting in a large deviation between the prediction results and the actual combustion rate value. To this end, we propose a blast furnace pulverized coal combustion rate prediction method and system based on thermal analysis. Summary of the invention

[0004] The main purpose of the present invention is to provide a method and system for predicting the combustion rate of blast furnace coal injection based on thermal analysis, which can effectively solve the problems in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for predicting the combustion rate of blast furnace coal injection based on thermal analysis, comprising:

[0007] Step 1: Conduct a multi-factor orthogonal test on the combustion performance of pulverized coal by using a thermal analysis test method, collect data on all factors affecting the combustion rate during the test, and determine the combustion rate of pulverized coal under the conditions of the influencing factors, wherein the influencing factors include the property parameters of the pulverized coal used for the test and the combustion environment parameters during the experiment;

[0008] Step 2: constructing an evaluation model using the various parameters in the acquired influencing factor data as evaluation indicators, and determining the evaluation value of the influencing factor through the model;

[0009] Step 3: determine the relationship between the pulverized coal combustion rate and the quality evaluation value under the influencing factors, determine the best influencing factors and the worst influencing factors according to the determination results, obtain the combustion rate values ​​of the best influencing factors and the worst influencing factors, and construct the combustion rate prediction threshold [br min ,br max ], where br minis the combustion rate value corresponding to the worst influencing factor, br max is the combustion rate value corresponding to the optimal influencing factor;

[0010] Step 4: Calculate the distance between the best influencing factor and the worst influencing factor, take the distance value between the best influencing factor and the worst influencing factor as the step length, set the classification gradient value, and classify the distance value by a formula, wherein the formula is:

[0011]

[0012] In the formula, k is the number of levels divided by the distance value, o(·) is the rounding of the value in the brackets, ε is the gradient value, and d |min-max| is the distance between the best influencing factor and the worst influencing factor, where r imax is the parameter value of the i-th item of the optimal influencing factor, r imin is the parameter value of the i-th item of the worst influencing factor, and n is the parameter type of the influencing factor;

[0013] Step 5: According to the classification result of the distance value, the combustion rate prediction threshold [br min ,br max ] is divided into k prediction intervals;

[0014] Step six, obtaining the measured value of the influencing factor of the coal powder to be measured, and calculating the distance between the measured value of the influencing factor of the coal powder to be measured and the optimal influencing factor, the calculation formula is:

[0015]

[0016] Where, d a-max is the distance between the measured value of the influencing factor of the coal powder to be tested and the optimal influencing factor, ra i is the measured value of the ith parameter of the influencing factor of the pulverized coal to be measured, and the prediction interval of the combustion rate of the pulverized coal to be measured is determined according to the calculation result.

[0017] A blast furnace pulverized coal blending combustion rate prediction system based on thermal analysis, the system is used to implement the steps of the method, including a data acquisition module, a combustion rate determination module, a data processing module, a data storage module, an influencing factor quality evaluation module, an influencing factor quality determination module, a quality distance classification module, and a combustion rate prediction module

[0018] The data acquisition module is used to collect data of all combustion rate influencing factors during the multi-factor orthogonal test of coal powder combustion performance;

[0019] The combustion rate determination module is used to determine the coal powder combustion rate under the influencing factors during each test process to obtain coal powder combustion rate data;

[0020] The data storage module is connected to the data acquisition module and the combustion rate determination module, and is used to store all acquired combustion rate influencing factor data and coal powder combustion rate data;

[0021] The data processing module is connected to the data acquisition module and is used to perform preprocessing including noise reduction, cleaning, and supplementation of missing values ​​on the acquired combustion rate influencing factor data;

[0022] The influencing factor quality evaluation module is connected to the data processing module, and is used to construct an evaluation model based on various parameters in the preprocessed combustion rate influencing factor data as evaluation indicators, and determine the quality evaluation value of the influencing factor through the model;

[0023] The influencing factor quality judgment module is used to judge the relationship between the coal powder combustion rate and the quality evaluation value under the influencing factor conditions, determine the best influencing factor and the worst influencing factor according to the judgment result, obtain the combustion rate values ​​of the best influencing factor and the worst influencing factor to construct the combustion rate prediction threshold;

[0024] The superiority and inferiority distance classification module is connected to the superiority and inferiority judgment module of the influencing factors, and is used to calculate the distance between the best influencing factor and the worst influencing factor, and use the distance value between the best influencing factor and the worst influencing factor as the step length, and classify the distance value by setting the classification gradient value;

[0025] The combustion rate prediction module is connected to the superior and inferior distance grading module, and is used to divide the combustion rate prediction threshold into multiple prediction intervals according to the grading result of the distance value, and obtain the measured value of the influencing factor of the coal powder to be tested, calculate the distance between the obtained measured value of the influencing factor of the coal powder to be tested and the optimal influencing factor, and determine the prediction interval of the combustion rate of the coal powder to be tested according to the calculation result;

[0026] The system further includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor.

[0027] The present invention has the following beneficial effects:

[0028] Compared with the prior art, the technical solution of the present invention adopts a thermal analysis test method to conduct a multi-factor orthogonal test on the combustion performance of coal powder, collects all combustion rate influencing factor data during the test, and determines the coal powder combustion rate under the conditions of the influencing factors, and constructs an evaluation model using the various parameters in the acquired influencing factor data as evaluation indicators, determines the goodness or badness evaluation value of the influencing factor through the model, judges the relationship between the coal powder combustion rate under the conditions of the influencing factors and the goodness or badness evaluation value, determines the best influencing factor and the worst influencing factor according to the judgment result, obtains the combustion rate values ​​of the best influencing factor and the worst influencing factor to construct a combustion rate prediction threshold, and calculates the relationship between the best influencing factor and the worst influencing factor. The distance between the elements is taken as the step size, and the distance value between the optimal influencing factor and the worst influencing factor is taken as the step size, and the classification gradient value is set to grade the distance value, and the combustion rate prediction threshold is divided into multiple prediction intervals according to the grade classification result of the distance value, and the measured value of the influencing factor of the coal powder to be tested is obtained, and the distance between the measured value of the influencing factor of the coal powder to be tested and the optimal influencing factor is calculated, and the prediction interval of the combustion rate of the coal powder to be tested is determined according to the calculation result, and the physical and chemical properties of coal powder, combustion environment factors and the matching degree of coal powder and the environment on the combustion rate of coal powder are comprehensively considered, so that the predicted combustion rate value is closer to the actual situation, and the accuracy of the prediction is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a method for predicting the combustion rate of blast furnace coal injection based on thermal analysis of the present invention;

[0030] Figure 2 The present invention is a block diagram of the overall structure of a blast furnace coal injection combustion rate prediction system based on thermal analysis. DETAILED DESCRIPTION

[0031] The present invention will be further described below in conjunction with specific implementation methods, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limiting the present invention. In order to better illustrate the specific implementation methods of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0032] Example 1

[0033] like Figure 1 The technical solution of the present invention is a flowchart of a method for predicting the combustion rate of blast furnace coal injection based on thermal analysis and Figure 2 The figure shows the overall structural block diagram of a blast furnace coal injection combustion rate prediction system based on thermal analysis.

[0034] The implementation process of the technical solution of the present invention specifically includes the following steps:

[0035] Step 1, design a multi-factor orthogonal test on the combustion performance of coal powder according to the types of factors affecting the combustion rate, determine the property parameters of the coal powder before the test, collect the mass proportion of each type of coal powder and the coal powder particle size data through the data acquisition module, use the thermal analysis test method to test the combustion performance of the coal powder, and collect the combustion environment parameter data including combustion temperature, coal feed amount, single injection amount of coal powder, oxygen enrichment rate, blast volume, single injection time, injection interval time, injection pressure value, etc. during the experiment. In this embodiment, we set the types of coal powder to two, namely Class A coal powder and Class B coal powder, and their mass proportions are a% and b% respectively. The experiment explores the relationship between the mass proportion of coal powder, coal powder particle size, combustion temperature, oxygen enrichment rate, blast volume and other parameters on the coal powder combustion rate. The number of experiments is set to 10 times, and the combustion rate data values ​​obtained are f1-f10. The obtained data are shown in the following table:

[0036]

[0037]

[0038] Table 1 Combustion rate influencing factors and combustion rate data table

[0039] Among them, the pulverized coal combustion rate can be detected by ash balance calculation using a dry dust collection method or measured using a synchronous thermal analyzer. The pulverized coal combustion rate is determined using a thermal gravimetric analysis method, which will not be described in detail here.

[0040] Step 2: After obtaining the original data, the data processing module performs preprocessing on the acquired combustion rate influencing factor data, including noise reduction, cleaning, and supplementation of missing values.

[0041] Step 3: construct an evaluation model by using the influencing factor quality evaluation module with various parameters in the pre-processed combustion rate influencing factor data as evaluation indicators, and determine the quality evaluation value of the influencing factor by using the model. The specific steps are as follows:

[0042] Step 31, obtaining various parameters in the influencing factor data, and classifying the obtained various parameters into four categories according to type: extremely small indicator parameters, intermediate indicator parameters, interval indicator parameters and extremely large indicator parameters, wherein the classification basis of parameter types is:

[0043] When the parameter value is larger, the pulverized coal combustion rate is higher, and this type of parameter is an extremely large index parameter;

[0044] When the parameter value is smaller, the pulverized coal combustion rate is higher, and this type of parameter is an extremely small indicator parameter;

[0045] When the parameter value approaches a certain fixed value, the pulverized coal combustion rate is higher, and this type of parameter is an intermediate indicator parameter;

[0046] When the parameter value is within a certain fixed value range, the higher the pulverized coal combustion rate, the type of parameter is an interval indicator parameter;

[0047] According to the above classification basis, the mass proportion of Class A coal powder and the mass proportion of Class B coal powder are divided into intermediate index parameters, the coal powder particle size is divided into extremely small index parameters, the combustion temperature is divided into interval index parameters, and the oxygen enrichment rate and blast volume are divided into extremely large index parameters. Among them, the conversion formula of extremely small index parameters is:

[0048] x'=Mx

[0049] The conversion formula of the intermediate indicator parameters is:

[0050]

[0051] The conversion formula for interval indicator parameters is:

[0052]

[0053] In the formula, x' is the transformed data, x is the data before transformation, M is the maximum value of this type of data in all experiments, m is the minimum value of this type of data in all experiments, and [a, b] is the optimal interval of interval indicator parameters;

[0054] Step 32, the extremely small index parameters, the intermediate index parameters and the interval index parameters are forward processed, and the evaluation matrix is ​​constructed using the processed data, wherein the expression of the evaluation matrix is:

[0055]

[0056] In the formula, r mn It is represented as the nth parameter data value of the influencing factor of the mth test. In this embodiment, the value of m is 10, the value of n is 6, and the evaluation matrix is:

[0057]

[0058] Step 33, each element in the evaluation matrix is ​​standardized by a formula to obtain a standardized value of the element, wherein the standardized processing formula is:

[0059]

[0060] In the formula, z mn To evaluate the standardized values ​​of the elements in the matrix, a standardized matrix is ​​constructed according to the obtained standardized values, where the expression of the standardized matrix is:

[0061]

[0062] Step 34, calculating the distance between the influencing factor of the mth test and the optimal value and the worst value according to the obtained standardized matrix, wherein the calculation formula with the optimal value is:

[0063]

[0064] In the formula, is the distance between the influencing factor of the mth test and the optimal value, is the weight value of the jth parameter, j∈n. In this embodiment, z jmax is the maximum value of the column where the jth parameter in the standardized matrix is ​​located, and the calculation formula for the worst value is;

[0065]

[0066] In the formula, is the distance between the influencing factor of the mth test and the worst value, z jmin is the minimum value of the column where the jth parameter in the standardized matrix is ​​located;

[0067] Step 35, calculate and obtain the goodness evaluation value of the influencing factor according to the calculation result of the distance between the influencing factor and the optimal value and the worst value of the mth test, wherein the calculation formula of the goodness evaluation value of the influencing factor is:

[0068]

[0069] In the formula, C m is the evaluation value of the influencing factors of the mth test.

[0070] Step 4: The relationship between the pulverized coal combustion rate and the evaluation value of the quality under the influencing factor condition is judged by the influencing factor quality judgment module, and the best influencing factor and the worst influencing factor are determined according to the judgment result. The determination mechanism is:

[0071] When the influencing factor with the maximum coal combustion rate value is the same as the influencing factor with the maximum quality evaluation value, it is determined to be the optimal influencing factor;

[0072] When the influencing factor when the pulverized coal combustion rate value is the largest is different from the influencing factor when the quality evaluation value is the largest, the influencing factor when the quality evaluation value is the largest is determined to be the optimal influencing factor;

[0073] When the influencing factor with the minimum coal powder combustion rate value is the same as the influencing factor with the minimum goodness / badness evaluation value, it is determined to be the worst influencing factor;

[0074] When the influencing factor when the pulverized coal combustion rate value is the minimum is different from the influencing factor when the quality evaluation value is the minimum, the influencing factor when the quality evaluation value is the minimum is determined to be the worst influencing factor;

[0075] Obtain the combustion rate values ​​of the best and worst influencing factors to construct the combustion rate prediction threshold, denoted as [br min ,br max ], where br min is the combustion rate value corresponding to the worst influencing factor, br max is the combustion rate value corresponding to the optimal influencing factor.

[0076] Step 5: Calculate the distance d between the best influencing factor and the worst influencing factor through the good-bad distance classification module min-max , the calculation formula is:

[0077]

[0078] In the formula, r imax is the parameter value of the i-th item of the optimal influencing factor, r imin is the parameter value of the i-th item of the worst influencing factor, and the distance value between the best influencing factor and the worst influencing factor is taken as the step length. The distance value is graded by setting the classification gradient value. The expression of the division formula is:

[0079]

[0080] In the formula, k is the number of levels divided by the distance value, o(·) is the value in the brackets rounded to the nearest integer, and ε is the gradient value. The value range of the gradient value ε is determined according to the empirical formula, which is:

[0081]

[0082] Where m is the number of times the combustion performance test of pulverized coal is conducted, m' is the number of tests with the same measured combustion rate, and α is a constant coefficient, where α∈[0.5,1].

[0083] Step 6: The burning rate prediction module uses the burning rate prediction threshold [br min ,br max ] is divided into k prediction intervals, and the measured values ​​of the influencing factors of the coal powder to be measured are obtained, and the distance between the measured values ​​of the influencing factors of the coal powder to be measured and the optimal influencing factors is calculated. The calculation formula is:

[0084]

[0085] Where, d a-max is the distance between the measured value of the influencing factor of the coal powder to be tested and the optimal influencing factor, ra iis the measured value of the ith parameter of the influencing factor of the coal powder to be measured, and the prediction interval of the coal powder combustion rate to be measured is determined according to the calculation result, and the corresponding prediction threshold value closest to the calculation result is taken as the predicted value of the coal powder combustion rate to be measured.

[0086] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for predicting the combustion rate of blast furnace coal injection based on thermal analysis, characterized in that: include, Step 1: Conduct a multi-factor orthogonal test on the combustion performance of pulverized coal by using a thermal analysis test method, collect data on all factors affecting the combustion rate during the test, and determine the combustion rate of pulverized coal under the conditions of the influencing factors, wherein the influencing factors include the property parameters of the pulverized coal used for the test and the combustion environment parameters during the experiment; Step 2: constructing an evaluation model using the various parameters in the acquired influencing factor data as evaluation indicators, and determining the evaluation value of the influencing factor through the model; Step 3: determine the relationship between the pulverized coal combustion rate and the quality evaluation value under the influencing factors, determine the best influencing factors and the worst influencing factors according to the determination results, obtain the combustion rate values ​​of the best influencing factors and the worst influencing factors, and construct the combustion rate prediction threshold [br min ,br max ], where br min is the combustion rate value corresponding to the worst influencing factor, br max is the combustion rate value corresponding to the optimal influencing factor; Step 4: Calculate the distance between the best influencing factor and the worst influencing factor, take the distance value between the best influencing factor and the worst influencing factor as the step length, set the classification gradient value, and classify the distance value by a formula, wherein the formula is: In the formula, k is the number of levels divided by the distance value, o(·) is the rounding of the value in the brackets, ε is the gradient value, and d min-max is the distance between the best influencing factor and the worst influencing factor, where r imax is the parameter value of the i-th item of the optimal influencing factor, r imin is the parameter value of the i-th item of the worst influencing factor, and n is the parameter type of the influencing factor; Step 5: According to the classification result of the distance value, the combustion rate prediction threshold [br min ,br max ] is divided into k prediction intervals; Step six, obtaining the measured value of the influencing factor of the coal powder to be measured, and calculating the distance between the measured value of the influencing factor of the coal powder to be measured and the optimal influencing factor, the calculation formula is: Where, d a-max is the distance between the measured value of the influencing factor of the coal powder to be tested and the optimal influencing factor, ra i is the measured value of the ith parameter of the influencing factor of the pulverized coal to be measured, and the prediction interval of the combustion rate of the pulverized coal to be measured is determined according to the calculation result.

2. The method for predicting the combustion rate of blast furnace coal injection based on thermal analysis according to claim 1 is characterized in that: The specific steps of step 2 are: Step s21, obtaining various parameters in the influencing factor data, and classifying the obtained various parameters into four categories according to type: extremely small indicator parameters, intermediate indicator parameters, interval indicator parameters and extremely large indicator parameters; Step s22, the extremely small index parameters, the intermediate index parameters and the interval index parameters are forward processed, and the evaluation matrix is ​​constructed using the processed data, wherein the expression of the evaluation matrix is: In the formula, r mn It is expressed as the nth parameter data value of the influencing factor of the mth test; Step s23, standardize each element in the evaluation matrix through a formula to obtain a standardized value of the element, wherein the standardization formula is: In the formula, z mn To evaluate the standardized values ​​of the elements in the matrix, a standardized matrix is ​​constructed according to the obtained standardized values, wherein the expression of the standardized matrix is: Step s24, calculating the distance between the influencing factor of the mth test and the optimal value and the worst value according to the obtained standardized matrix, wherein the calculation formula with the optimal value is: In the formula, is the distance between the influencing factor of the mth test and the optimal value, is the weight value of the jth parameter, j∈n, z jmax is the maximum value of the column where the jth parameter in the standardized matrix is ​​located, and the calculation formula for the worst value is; In the formula, is the distance between the influencing factor of the mth test and the worst value, z jmin is the minimum value of the column where the jth parameter in the standardized matrix is ​​located; Step s25, calculate and obtain the goodness evaluation value of the influencing factor according to the calculation result of the distance between the influencing factor and the optimal value and the worst value of the mth test, wherein the calculation formula of the goodness evaluation value of the influencing factor is: In the formula, C m is the evaluation value of the influencing factors of the mth test.

3. The method for predicting the combustion rate of blast furnace coal injection based on thermal analysis according to claim 1, characterized in that: In step 3, the mechanism for determining the best and worst influencing factors is: When the influencing factor with the maximum coal combustion rate value is the same as the influencing factor with the maximum quality evaluation value, it is determined to be the optimal influencing factor; When the influencing factor when the pulverized coal combustion rate value is the largest is different from the influencing factor when the quality evaluation value is the largest, the influencing factor when the quality evaluation value is the largest is determined to be the optimal influencing factor; When the influencing factor with the minimum coal powder combustion rate value is the same as the influencing factor with the minimum goodness / badness evaluation value, it is determined to be the worst influencing factor; When the influencing factor when the pulverized coal combustion rate value is minimum is different from the influencing factor when the quality evaluation value is minimum, the influencing factor when the quality evaluation value is minimum is determined to be the worst influencing factor.

4. The method for predicting the combustion rate of blast furnace coal injection based on thermal analysis according to claim 2, characterized in that: In step s21, the parameter types are divided according to: When the parameter value is larger, the coal powder combustion rate is higher, and this type of parameter is an extremely large index parameter; When the parameter value is smaller, the coal powder combustion rate is higher, and this type of parameter is an extremely small index parameter; When the parameter value approaches a certain fixed value, the coal powder combustion rate is higher, and this type of parameter is an intermediate indicator parameter; When the parameter value is within a certain fixed value range, the higher the pulverized coal combustion rate is, the type of parameter is an interval indicator parameter.

5. The method for predicting the combustion rate of blast furnace coal injection based on thermal analysis according to claim 1, characterized in that: The coal powder property parameters include the mass proportion of each type of coal powder and the coal powder particle size; the combustion environment parameters include combustion temperature, coal feed rate, single coal powder injection amount, oxygen enrichment rate, air volume, single injection time, injection interval time, and injection pressure value.

6. The method for predicting the combustion rate of blast furnace coal injection based on thermal analysis according to claim 1, characterized in that: In step 4, the range of the gradient value ε is determined according to the empirical formula: Where m is the number of times the combustion performance test of pulverized coal is conducted, m' is the number of tests with the same measured combustion rate, and α is a constant coefficient, where α∈[0.5,1].

7. A blast furnace coal injection combustion rate prediction system based on thermal analysis, characterized in that: The system is used for the steps of the method, including a data acquisition module, a combustion rate determination module, a data processing module, a data storage module, an influencing factor quality evaluation module, an influencing factor quality determination module, a quality distance classification module, and a combustion rate prediction module; The data acquisition module is used to collect data of all combustion rate influencing factors during the multi-factor orthogonal test of coal powder combustion performance; The combustion rate determination module is used to determine the coal powder combustion rate under the influencing factors during each test process to obtain coal powder combustion rate data; The data storage module is connected to the data acquisition module and the combustion rate determination module, and is used to store all acquired combustion rate influencing factor data and coal powder combustion rate data; The data processing module is connected to the data acquisition module and is used to perform preprocessing including noise reduction, cleaning, and supplementation of missing values ​​on the acquired combustion rate influencing factor data; The influencing factor quality evaluation module is connected to the data processing module, and is used to construct an evaluation model based on various parameters in the preprocessed combustion rate influencing factor data as evaluation indicators, and determine the quality evaluation value of the influencing factor through the model; The influencing factor quality judgment module is used to judge the relationship between the coal powder combustion rate and the quality evaluation value under the influencing factor conditions, determine the best influencing factor and the worst influencing factor according to the judgment result, obtain the combustion rate values ​​of the best influencing factor and the worst influencing factor to construct the combustion rate prediction threshold; The superiority and inferiority distance classification module is connected to the superiority and inferiority judgment module of the influencing factors, and is used to calculate the distance between the best influencing factor and the worst influencing factor, and use the distance value between the best influencing factor and the worst influencing factor as the step length, and classify the distance value by setting the classification gradient value; The combustion rate prediction module is connected to the superior-inferior distance grading module, and is used to divide the combustion rate prediction threshold into multiple prediction intervals according to the grading result of the distance value, and obtain the measured value of the influencing factor of the coal powder to be tested, calculate the distance between the obtained measured value of the influencing factor of the coal powder to be tested and the optimal influencing factor, and determine the prediction interval of the combustion rate of the coal powder to be tested according to the calculation result.

8. The blast furnace coal injection combustion rate prediction system based on thermal analysis according to claim 7 is characterized in that: The system also includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor, wherein the processor can implement the steps of the method according to any one of claims 1 to 6 when running the electronic program.

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