Method and system for obtaining pyrolysis reaction mechanism of fireproof mortar based on multi-peak pyrolysis curve

Abstract

The application discloses a fireproof mortar pyrolysis reaction mechanism obtaining method and system based on a multi-peak pyrolysis curve, and comprises the following steps: S1, collecting non-isothermal pyrolysis experimental data of the fireproof mortar, i.e. thermogravimetric experimental data; S2, processing the thermogravimetric data to obtain a differential thermogravimetric curve of the fireproof mortar, and determining the number of reaction peaks; S3, fitting the differential thermogravimetric curve based on a deconvolution method to obtain a sub-reaction peak; S4, optimizing the sub-reaction peak by using a particle swarm algorithm to determine the combined kinetic parameters thereof; and S5, obtaining the pyrolysis reaction mechanism and kinetic parameters of each reaction peak. The application processes the multi-peak pyrolysis conversion rate data of the fireproof mortar by using the deconvolution method, overcomes the difficulty in obtaining the pyrolysis mechanism function of the sub-reaction peak by using the combined kinetics and the particle swarm algorithm, solves the problem that the mechanism function needs to be assumed in the traditional method, and obtains the reaction mechanism function based on the combined kinetics, which is more suitable for the actual pyrolysis process of the fireproof mortar.

Description

Technical Field

[0001] This invention relates to the technical field of digital computing or data processing methods specifically applicable to particular applications, and specifically to a method for obtaining the pyrolysis reaction mechanism of fireproof mud based on multi-peak pyrolysis curves. Background Technology

[0002] Converter stations play a crucial role in high-voltage direct current (HVDC) transmission systems, primarily utilizing polymer materials such as cables. However, these materials can potentially cause electrical fires. Furthermore, the valve halls of converter stations contain large quantities of transformer oil, which, if ignited, poses a significant fire risk. Fire-resistant sealing materials can play a preventative and control role at various stages of fire development in converter stations. In particular, fire-resistant putty, a typical organic elastic sealing material, possesses numerous advantages, including maintaining a non-curing state for a long period, good plasticity, and high-temperature resistance. Therefore, studying the chemical reactions and decomposition processes of fire-resistant putty under high-temperature fire conditions is of paramount importance.

[0003] Current research mainly focuses on the physicochemical properties and fire resistance of fireproof mortar at the large scale. For example, the study "Research on the Performance Changes of Fireproof Mortar Before and After Fire Resistance Simulation Test [J]. Fire Science and Technology, 2023, 42(03):379-383" investigated the density, corrosivity, water resistance, acid resistance, alkali resistance, oil resistance, temperature resistance, and freeze-thaw cycle resistance of fireproof mortar under various fire temperature curves. There is relatively little research on the pyrolysis mechanism of fireproof mortar at the microscale, especially for complex multi-peak pyrolysis curves, which make it difficult to analyze its multi-component and multi-stage processes. Moreover, most studies use series reaction mechanisms as assumptions to solve for relevant kinetic parameters.

[0004] However, the deconvolution method can fit the multi-peak pyrolysis curve well and analyze the pyrolysis process of the multi-component fireproofing mud. Furthermore, the combined kinetics simultaneously considers series reaction mechanisms, power-law mechanisms, and nucleation and diffusion mechanism models. Based on this, a heuristic optimization algorithm can be used to optimize its kinetic parameters to obtain the multi-peak pyrolysis reaction mechanism and kinetic parameters of the fireproofing mud. Summary of the Invention

[0005] The technical problem to be solved by this invention is how to obtain the multi-peak pyrolysis reaction mechanism and kinetic parameters of fireproof mud.

[0006] The present invention solves the above-mentioned technical problems through the following technical means:

[0007] The method for obtaining the pyrolysis reaction mechanism of fireproof mud based on multi-peak pyrolysis curves includes the following steps:

[0008] S1. Collect non-isothermal pyrolysis experimental data of fireproof mud, i.e. thermogravimetric experimental data;

[0009] S2. Process thermogravimetric data to obtain the differential thermogravimetric curve of the fireproof mud and determine the number of reaction peaks;

[0010] S3. Sub-reaction peaks are obtained by fitting the differential thermogravimetric curve based on the deconvolution method;

[0011] S4. The particle swarm optimization algorithm is used to optimize the sub-reaction peaks and determine their combined kinetic parameters;

[0012] S5. Obtain the pyrolysis reaction mechanism and kinetic parameters of each reaction peak.

[0013] This invention utilizes deconvolution to process the multi-peak pyrolysis conversion rate data of fireproof mud; it overcomes the difficulty of obtaining the pyrolysis mechanism function of sub-reaction peaks by using combinatorial dynamics and particle swarm optimization, and solves the problem that the mechanism function needs to be assumed in traditional methods; the reaction mechanism function obtained based on combinatorial dynamics is more closely related to the actual pyrolysis process of fireproof mud.

[0014] Furthermore, in step S1, the thermogravimetric experimental data are the mass loss data of the fireproof mud at a specific heating rate, which need to be further processed into conversion rate data. The expression for the conversion rate α is as follows:

[0015]

[0016] Where, m t m is the instantaneous mass of the sample during pyrolysis, m0 is the initial mass, and m ∞ For final quality.

[0017] Furthermore, in step S2, the conversion rate data at different heating rates are differentiated with respect to the pyrolysis temperature to obtain a differential thermogravimetric curve, and the number of reaction peaks is determined based on the image; the differential conversion rate expression based on the Arrhenius equation is as follows:

[0018]

[0019] Where A refers to the pre-factor, and s -1 β is the heating rate, K / min; E is the activation energy, J / mol. -1 R is the gas constant; f(α) is a reaction mechanism model that depends on the conversion rate α.

[0020] Furthermore, in step S3, the deconvolution method uses the bi-Gaussian equation, expressed as follows:

[0021]

[0022] Where y0, H, x, x cThe parameters w1 and w2 correspond to the baseline offset, maximum peak height, independent variable, peak center, left and right half-peak widths, and half-peak widths, respectively. The objective function for fitting is to minimize the sum of squared residuals (RSS), and its expression is:

[0023]

[0024] and These are the actual value and the calculated value, respectively, where N is the total number of data points. The integral of the deconvolutioned differential conversion rate response peak is normalized to 0-1, as shown in the following equation:

[0025]

[0026] In the formula, For the normalized data, a i For sample data; a min Let a be the minimum value in the sample data sequence. max This is the maximum value.

[0027] Furthermore, in step S4, the combined dynamics formula is as follows:

[0028] f(α)=α m (1-α) n [-ln(1-α)] p

[0029] Wherein, the combined kinetic parameters m, p, and n represent the series reaction mechanism, power-law mechanism, and nucleation and diffusion mechanism models, respectively; substitution formula Later:

[0030]

[0031] Taking the natural logarithm of both sides, we have:

[0032]

[0033] The optimization objective of the particle swarm optimization algorithm is to maximize the Pearson correlation coefficient between the expression on the left side of the above equation and -1 / T.

[0034] Furthermore, in step S5, the pyrolysis reaction mechanism of each sub-peak is obtained by substituting the parameters obtained in step S4 into the combined kinetic equation, and the kinetic parameters are determined by fitting the slope and intercept beforehand.

[0035] lnA = b

[0036] E = kR

[0037] k and b are the slope and intercept of the fitted line, respectively, and R is the universal gas constant.

[0038] Corresponding to the above method, the present invention also describes a system for obtaining the pyrolysis reaction mechanism of fireproof mud based on multi-peak pyrolysis curves, comprising:

[0039] The data collection module is used to collect non-isothermal pyrolysis experimental data of fireproof mud, i.e. thermogravimetric experimental data;

[0040] The reaction peak calculation module is used to process thermogravimetric data to obtain the differential thermogravimetric curve of fireproof mud and determine the number of reaction peaks.

[0041] The sub-reaction peak calculation module is used to obtain sub-reaction peaks by fitting differential thermogravimetric curves based on the deconvolution method.

[0042] The combined kinetic parameter calculation module is used to optimize sub-reaction peaks using the particle swarm optimization algorithm to determine their combined kinetic parameters.

[0043] The parameter acquisition module is used to obtain the pyrolysis reaction mechanism and kinetic parameters of each reaction peak.

[0044] Furthermore, in the data collection module, the thermogravimetric experimental data is the mass loss data of the fireproof mud at a specific heating rate, which needs to be further processed into conversion rate data. The expression for the conversion rate 'a' is as follows:

[0045]

[0046] Where, m t m is the instantaneous mass of the sample during pyrolysis, m0 is the initial mass, and m ∞ For final quality.

[0047] Furthermore, in the reaction peak calculation module, the conversion rate data at different heating rates are differentiated with respect to the pyrolysis temperature to obtain a differential thermogravimetric curve, and the number of reaction peaks is determined based on the image; combined with the differential conversion rate expression of the Arrhenius equation, it is as follows:

[0048]

[0049] Where A refers to the pre-factor, and s -1 β is the heating rate, K / min; E is the activation energy, J / mol. -1 R is the gas constant; f(α) is a reaction mechanism model that depends on the conversion rate α.

[0050] Furthermore, in the sub-reaction peak calculation module, the deconvolution method adopts the bi-Gaussian equation, as shown in the following expression:

[0051]

[0052]

[0053] Where y0, H, x, x c The parameters w1 and w2 correspond to the baseline offset, maximum peak height, independent variable, peak center, left and right half-peak widths, and half-peak widths, respectively. The objective function for fitting is to minimize the sum of squared residuals (RSS), and its expression is:

[0054]

[0055] and These are the actual value and the calculated value, respectively, where N is the total number of data points. The integral of the deconvolutioned differential conversion rate response peak is normalized to 0-1, as shown in the following equation:

[0056]

[0057] In the formula, For the normalized data, a i For sample data; a min Let a be the minimum value in the sample data sequence. max This is the maximum value.

[0058] Furthermore, in the combined dynamics parameter calculation module, the combined dynamics formula is as follows:

[0059] f(α)=α m (1-α) n [-ln(1-α)] p

[0060] Wherein, the combined kinetic parameters m, p, and n represent the series reaction mechanism, power-law mechanism, and nucleation and diffusion mechanism models, respectively; substitution formula Later:

[0061]

[0062] Taking the natural logarithm of both sides, we have:

[0063]

[0064] The optimization objective of the particle swarm optimization algorithm is to maximize the Pearson correlation coefficient between the expression on the left side of the above equation and -1 / T.

[0065] Furthermore, in the parameter acquisition module, the pyrolysis reaction mechanism of each sub-peak is obtained by substituting the parameters obtained from the combined kinetic parameter calculation module into the combined kinetic equation, and the kinetic parameters are determined by fitting the slope and intercept beforehand.

[0066] lnA = b

[0067] E = kR

[0068] k and b are the slope and intercept of the fitted line, respectively, and R is the universal gas constant.

[0069] The advantages of this invention are:

[0070] This invention utilizes deconvolution to process the multi-peak pyrolysis conversion rate data of fireproof mud; it overcomes the difficulty of obtaining the pyrolysis mechanism function of sub-reaction peaks by using combinatorial dynamics and particle swarm optimization, and solves the problem that the mechanism function needs to be assumed in traditional methods; the reaction mechanism function obtained based on combinatorial dynamics is more closely related to the actual pyrolysis process of fireproof mud.

[0071] This invention simplifies the acquisition of the multi-peak reaction mechanism of fireproof mud pyrolysis, avoids the process of solving complex kinetic parameters, and overcomes the difficulty of simultaneously determining the reaction mechanism of multiple pyrolysis peaks. It boasts high computational efficiency and strong reliability, opening up new avenues for obtaining the pyrolysis reaction mechanism of related materials. Attached Figure Description

[0072] Figure 1 This is a flowchart of the method for obtaining the pyrolysis reaction mechanism of fireproof mud based on multi-peak pyrolysis curves as described in Embodiment 1 of the present invention;

[0073] Figure 2 This is a graph showing the pyrolysis conversion rate and differential conversion rate of fireproof mud at different heating rates in Example 1 of the present invention;

[0074] Figure 3 This is a graph showing the deconvolution results of the differential conversion rate of fireproof mud at different heating rates in Example 1 of the present invention;

[0075] Figure 4 This is a diagram showing the optimization results of sub-reaction peaks based on combinatorial dynamics and particle swarm optimization in Embodiment 1 of the present invention. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] Example 1

[0078] Please refer to Figure 1 This is a flowchart illustrating the method for obtaining the pyrolysis reaction mechanism of fireproof mud based on multi-peak pyrolysis curves according to the present invention. The method for obtaining the pyrolysis reaction mechanism of fireproof mud based on multi-peak pyrolysis curves according to the present invention includes the following steps:

[0079] S1. Collect pyrolysis mass loss data of fireproof mud at different heating rates, and further adjust it into conversion rate data. The expression for conversion rate 'a' is:

[0080]

[0081] Where, m t m is the instantaneous mass of the sample during pyrolysis, m0 is the initial mass, and m ∞ For final quality.

[0082] S2. Based on the conversion rate data, differentiate the selected heating rate curves to obtain differential conversion rate data curves and determine the number of reaction peaks. The differential conversion rate is combined with the Arrhenius equation as follows:

[0083]

[0084] Where A is the pre-exponential factor of Arrhenius, and s -1 β is the heating rate, K / min; R is the gas constant; E is the activation energy, J / mol. -1 f(α) is a reaction mechanism model that depends on the conversion rate α.

[0085] S3. Deconvolve the differential conversion rate data based on the number of reaction peaks to obtain the differential conversion rate data for each reaction peak. Preferably, the deconvolution method uses the bi-Gaussian equation, as shown below:

[0086]

[0087]

[0088] Where y0, H, x, x c The parameters w1 and w2 correspond to the baseline offset, maximum peak height, independent variable, peak center, left and right half-peak widths, and half-peak widths, respectively. The objective function for fitting is to minimize the residual sum of squares (RSS), which is expressed as:

[0089]

[0090] and These are the actual value and the calculated value, respectively, and N is the total number of data points.

[0091] Integrating the deconvolutioned differential conversion rate response peak and normalizing it to 0-1, as shown in the following equation:

[0092]

[0093] In the formula, For the normalized data, a i For sample data; amin Let a be the minimum value in the sample data sequence. max This is the maximum value. Differentiating the normalized data yields the differential conversion rate data for each reaction peak.

[0094] S4. The particle swarm optimization algorithm is used to optimize the combinatorial dynamics parameters of the conversion rate data of each sub-peak. The combinatorial dynamics formula is as follows:

[0095] f(α)=α m (1-α) n [-ln(1-α)] p

[0096] Where m, p, and n represent the series reaction mechanism, power-law mechanism, and nucleation and diffusion mechanism models, respectively. Substituting... Later:

[0097]

[0098] Taking the natural logarithm of both sides, we have:

[0099]

[0100] By maximizing the Pearson correlation coefficient between the left side of Equation (7) and -1 / T using the particle swarm optimization algorithm, the range of values ​​for the three key combined kinetic parameters is set to [-8,8], [0,7] and [-8,8], and β and dα / dT are then incorporated into the differential conversion data for each reaction peak.

[0101] S5. Substitute the optimized m, p, and n into f(α) = α m (1-α) n [-ln(1-α)] p The pyrolysis reaction mechanism function for each reaction peak is obtained; at the same time, the kinetic parameters are solved based on the slope and intercept of the fitted line.

[0102] The following uses a certain fireproof putty as an example to further explain the aforementioned steps:

[0103] In step S1, pyrolysis mass loss data of fireproof mud at different pyrolysis heating rates are collected and further processed into conversion rate data. The expression for conversion rate a is as follows:

[0104]

[0105] Where, m t m is the instantaneous mass of the sample during pyrolysis, m0 is the initial mass, and m ∞ For the final mass, three sets of mass loss data for fireproof mud at different pyrolysis heating rates of 5, 10, and 40 K / min were collected, and their conversion rate graphs are shown below. Figure 2 As shown in (A).

[0106] In step S2, based on Figure 2 The conversion rate data in (A) were differentiated with respect to temperature for 5, 10, and 40 K / min, respectively, to obtain differential conversion rate curves, as shown below. Figure 2 As shown in (B), the number of reaction peaks is 4, as can be seen from the image.

[0107] In step S3, based on the number of reaction peaks—4—the differential conversion rate data is deconvolved and fitted. Preferably, the deconvolution method uses the bi-Gaussian equation, as shown in the following expression:

[0108]

[0109]

[0110] Where y0, H, x, x c The parameters w1 and w2 correspond to the baseline offset, maximum peak height, independent variable, peak center, left and right half-peak widths, and half-peak widths, respectively. The residual sum of squares (RSS) expression is:

[0111]

[0112] and These are the actual value and the calculated value, respectively, where N is the total number of data points. The differential transformation rate after deconvolution is integrated and normalized to 0-1, as shown in the following equation:

[0113]

[0114] In the formula, For the normalized data, a i For sample data; a min Let a be the minimum value in the sample data sequence. max This is the maximum value.

[0115] The fitting results are as follows Figure 3 As shown in Table 1, the parameters of the bi-Gaussian equations are as follows:

[0116] Table 1. Deconvolution parameters of the bi-Gaussian equation for the differential conversion rate of fire-resistant mud at 5, 10, and 40 K / min.

[0117]

[0118] In step S4, the particle swarm optimization algorithm is used to optimize the combinatorial dynamics parameters of the conversion rate data for each sub-peak. The combinatorial dynamics formula is as follows:

[0119] f(α)=α m(1-α) n [-ln(1-α)]p

[0120] Where m, n, and p represent the series reaction mechanism, power-law mechanism, and nucleation and diffusion mechanism models, respectively. Substituting these into the differential conversion equation... Later:

[0121]

[0122] Taking the natural logarithm of both sides, we have:

[0123]

[0124] The Pearson correlation coefficient between the left side of equation (7) and -1 / T was maximized using the particle swarm optimization algorithm. Preferably, the ranges of the three key combined kinetic parameters were set to [-8,8], [0,7], and [-8,8], with β and dα / dT incorporating the differential conversion data for each reaction peak. The optimization results are as follows: Figure 4 As shown in Table 2, the three key combined kinetic parameters of each sub-reaction peak are shown in Table 2.

[0125] Table 2 Combined kinetic parameters of each sub-reaction peak

[0126]

[0127] In step S5, the fitted lines for the four sub-peaks are y = 9564.8264x + 21.3159, y = 31260.3826x + 23.0767, y = 24503.9192x + 15.8719, and y = 19837.2001x + 8.6315, respectively. The kinetic parameters of each reaction peak are determined based on the slope and intercept of these lines, and the resulting expression is as follows:

[0128] lnA = b

[0129] E = kR

[0130] k and b are the slope and intercept of the fitted line, respectively. The solution results are shown in Table 3.

[0131] Table 3 Kinetic parameters of each sub-reaction peak

[0132]

[0133] The beneficial effects of the technical solution provided by this invention are: the deconvolution method is used to process the multi-peak pyrolysis conversion rate data of fireproof mud; the combination dynamics and particle swarm optimization algorithm are used to overcome the difficulty of obtaining the pyrolysis mechanism function of each reaction peak; the problem of assuming the mechanism function in the traditional method is solved, which is more in line with the actual pyrolysis process of fireproof mud.

[0134] Example 2

[0135] This embodiment describes a system for obtaining the pyrolysis reaction mechanism of fireproof mud based on multi-peak pyrolysis curves, applied to the method of Embodiment 1, specifically including:

[0136] The data collection module is used to collect non-isothermal pyrolysis experimental data of fireproof mud, i.e. thermogravimetric experimental data;

[0137] The reaction peak calculation module is used to process thermogravimetric data to obtain the differential thermogravimetric curve of fireproof mud and determine the number of reaction peaks.

[0138] The sub-reaction peak calculation module is used to obtain sub-reaction peaks by fitting differential thermogravimetric curves based on the deconvolution method.

[0139] The combined kinetic parameter calculation module is used to optimize sub-reaction peaks using the particle swarm optimization algorithm to determine their combined kinetic parameters.

[0140] The parameter acquisition module is used to obtain the pyrolysis reaction mechanism and kinetic parameters of each reaction peak.

[0141] In the data collection module, the thermogravimetric experimental data are the mass loss data of fireproof mud at a specific heating rate. This data needs to be further processed into conversion rate data. The expression for the conversion rate 'a' is as follows:

[0142]

[0143] Where, m t m is the instantaneous mass of the sample during pyrolysis, m0 is the initial mass, and m ∞ For final quality.

[0144] In the reaction peak calculation module, the conversion rate data at different heating rates are differentiated with respect to the pyrolysis temperature to obtain a differential thermogravimetric curve. The number of reaction peaks is determined based on the image. The differential conversion rate expression based on the Arrhenius equation is as follows:

[0145]

[0146] Where A refers to the pre-factor, and s -1 β is the heating rate, K / min; E is the activation energy, J / mol. -1 R is the gas constant; f(α) is a reaction mechanism model that depends on the conversion rate α.

[0147] In the sub-reaction peak calculation module, the deconvolution method uses the bi-Gaussian equation, as shown below:

[0148]

[0149]

[0150] Where y0, H, x, x c The parameters w1 and w2 correspond to the baseline offset, maximum peak height, independent variable, peak center, left and right half-peak widths, and half-peak widths, respectively. The objective function for fitting is to minimize the sum of squared residuals (RSS), and its expression is:

[0151]

[0152] and These are the actual value and the calculated value, respectively, where N is the total number of data points. The integral of the deconvolutioned differential conversion rate response peak is normalized to 0-1, as shown in the following equation:

[0153]

[0154] In the formula, For the normalized data, a i For sample data; a min Let a be the minimum value in the sample data sequence. max This is the maximum value.

[0155] In the combined dynamics parameter calculation module, the combined dynamics formula is as follows:

[0156] f(α)=α m (1-α) n [-ln(1-α)] p

[0157] Wherein, the combined kinetic parameters m, p, and n represent the series reaction mechanism, power-law mechanism, and nucleation and diffusion mechanism models, respectively; substitution formula Later:

[0158]

[0159] Taking the natural logarithm of both sides, we have:

[0160]

[0161] The optimization objective of the particle swarm optimization algorithm is to maximize the Pearson correlation coefficient between the left-hand side of the above equation and -1 / T. Preferably, the ranges of the three key combined kinetic parameters are set to [-8,8], [0,7], and [-8,8], and β and dα / dT are substituted with the differential conversion data of each reaction peak.

[0162] In the parameter acquisition module, the pyrolysis reaction mechanism of each sub-peak is obtained by substituting the parameters obtained from the combined kinetic parameter calculation module into the combined kinetic equation. The kinetic parameters are then determined by fitting the slope and intercept beforehand.

[0163] lnA = b

[0164] E = kR

[0165] k and b are the slope and intercept of the fitted line, respectively, and R is the universal gas constant.

[0166] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for obtaining the pyrolysis reaction mechanism of fireproof mortar based on a multi-peak pyrolysis curve, characterized in that, It comprises the following steps: S1, collecting the non-isothermal pyrolysis experimental data of fireproof mortar, i.e. the thermogravimetric experimental data; S2, processing the thermogravimetric data to obtain the differential thermogravimetric curve of the fireproof mortar, and determining the number of reaction peaks; S3, fitting the differential thermogravimetric curve based on the deconvolution method to obtain the sub-reaction peaks; S4, using the particle swarm algorithm to optimize the sub-reaction peaks to determine the combined kinetic parameters; the combined kinetic formula is as follows: where the combined kinetic parameters m , p and n represent the series reaction mechanism, the power law mechanism and the nucleation and diffusion mechanism model, respectively; and the expression is followed by: Taking the natural logarithm on both sides has: The optimization objective of the particle swarm algorithm is to maximize the Pearson correlation coefficient between the expression on the left side of the above equation and -1 T between the expression on the left side of the above equation and -1 S5, obtaining the pyrolysis reaction mechanism and kinetic parameters of each reaction peak.

2. The method for obtaining the pyrolysis reaction mechanism of fireproof mortar based on the multi-peak pyrolysis curve according to claim 1, characterized in that: In the step S1, the thermogravimetric experimental data is the mass loss data of the fireproof mortar under a specific heating rate, which needs to be further processed into conversion rate data, and the conversion rate a The expression is as follows: wherein, m t is the initial mass of the sample, m 0 is the initial mass, is the final mass.

3. The method for obtaining the pyrolysis reaction mechanism of fireproof mortar based on the multi-peak pyrolysis curve according to claim 2, characterized in that: In the step S2, the conversion rate data under different heating rates are differentiated with respect to the pyrolysis temperature to obtain the differential thermogravimetric curve, and the number of reaction peaks is determined according to the image; the differential conversion rate expression of the Arrhenius equation is as follows: wherein A s refers to the pre-exponential factor, s -1 ; β is the heating rate, K / min; E is the activation energy, J mol -1 ; R is the gas constant; f ( α ) is a reaction mechanism model dependent on the conversion α .

4. The method for obtaining the pyrolysis reaction mechanism of fireproof mortar based on the multi-peak pyrolysis curve according to claim 3, characterized in that: In the step S3, the bi-Gaussian equation is used in the deconvolution method, and the expression is as follows: wherein, y 0, H , x , x c , w 1 and w 2 parameters correspond to baseline offset, peak maximum height, independent variable, peak center, left and right half-peak width and half-peak width, respectively; the objective function for fitting is the sum of squares of residuals RSS minimization, whose expression is: and are the actual and calculated values, respectively, and N is the total number of data points; the deconvoluted differential conversion rate peak is integrated and normalized to 0-1 as follows: In the formula, is the normalized data, a i is the sample data; a min is the minimum value in the sample data sequence, a max is the maximum value.

5. The method for obtaining the pyrolysis reaction mechanism of fireproof mortar based on the multi-peak pyrolysis curve according to claim 1, characterized in that: In the step S5, the parameters obtained in the step S4 are brought into the combined kinetic equation to obtain the pyrolysis reaction mechanism of each sub-peak, and the kinetic parameters are determined by the slope and intercept before fitting, respectively: k and b are the slope and intercept of the fitted line, respectively, R is the universal gas constant.

6. A system for obtaining the pyrolysis reaction mechanism of fireproof mortar based on a multi-peak pyrolysis curve, characterized in that, It comprises: A data collection module for collecting the non-isothermal pyrolysis experimental data of fireproof mortar, i.e. the thermogravimetric experimental data; A reaction peak calculation module for processing the thermogravimetric data to obtain the differential thermogravimetric curve of the fireproof mortar, and determining the number of reaction peaks; A sub-reaction peak calculation module for fitting the differential thermogravimetric curve based on the deconvolution method to obtain the sub-reaction peaks; A combined kinetic parameter calculation module for using the particle swarm algorithm to optimize the sub-reaction peaks to determine the combined kinetic parameters; the combined kinetic formula is as follows: where the combined kinetic parameters m , p and n represent the series reaction mechanism, the power law mechanism and the nucleation and diffusion mechanism model, respectively; and the expression is followed by: Taking the natural logarithm on both sides has: The optimization objective of the particle swarm algorithm is to maximize the Pearson correlation coefficient between the expression on the left side of the above equation and -1 T between the expression on the left side of the above equation and -1 A parameter acquisition module for obtaining the pyrolysis reaction mechanism and kinetic parameters of each reaction peak.

7. The multi-modal pyrolysis curve based fireclay pyrolysis reaction mechanism acquisition system of claim 6, wherein: In the data collection module, the thermogravimetric experiment data is the mass loss data of the fireproof mortar under a specific heating rate, which needs to be further processed into conversion rate data, and the conversion rate a The expression is as follows: wherein, m t is the initial mass of the sample, m 0 is the initial mass, is the final mass.

8. The multi-modal pyrolysis curve based fireclay pyrolysis reaction mechanism acquisition system of claim 7, wherein: In the reaction peak calculation module, the conversion rate data under different heating rates are differentiated with respect to the pyrolysis temperature to obtain the differential thermogravimetric curve, and the number of reaction peaks is determined according to the image; the differential conversion rate expression of the Arrhenius equation is as follows: wherein A is the pre-exponential factor, s -1 ; β is the heating rate, K / min; E is the activation energy, J mol -1 ; R is the gas constant; f ( α ) is a reaction mechanism model dependent on the conversion α .

9. The multi-modal pyrolysis curve based fireclay pyrolysis reaction mechanism acquisition system of claim 7, wherein: In the sub-reaction peak calculation module, the bi-Gaussian equation is used in the deconvolution method, and the expression is as follows: wherein, y 0, H , x , x c , w 1 and w 2 parameters correspond to baseline offset, peak maximum height, independent variable, peak center, left and right half-peak width and half-peak width, respectively; the objective function for fitting is the sum of squares of residuals RSS minimization, whose expression is: and are the actual and calculated values, respectively, and N is the total number of data points; the deconvoluted differential conversion rate peak is integrated and normalized to 0-1 as follows: In the formula, is the normalized data, a i is the sample data; a min is the minimum value in the sample data sequence, a max is the maximum value.

10. The multi-modal pyrolysis curve based fireclay pyrolysis reaction mechanism acquisition system of claim 6, wherein: In the parameter acquisition module, the parameters obtained in the combined kinetic parameter calculation module are brought into the combined kinetic equation to obtain the pyrolysis reaction mechanism of each sub-peak, and the kinetic parameters are determined by the slope and intercept before fitting, respectively: k and b are the slope and intercept of the fitted line, respectively, R is the universal gas constant.

Images