Method and system for predicting material pyrolysis component conversion process based on mechanism constraint

Abstract

This invention discloses a method and system for predicting the pyrolysis component transformation process of materials based on mechanism constraints, belonging to the field of thermal analysis kinetics technology. The method includes the following steps: replacing the initial mass term in the rate change equation of the basic equation of thermal analysis kinetics with a real-time mass term, while retaining the mechanism function term, to construct a pyrolysis component transformation reaction process prediction model; determining the kinetic parameters in the pyrolysis process based on thermogravimetric data, and substituting the kinetic parameters into the pyrolysis component transformation reaction process prediction model to obtain the transformation process data of each component of the target material during pyrolysis as a function of temperature or time. This invention modifies the mass term in the rate change equation of the basic equation of thermal analysis kinetics, changing it from a constant value to a dynamically changing value with the pyrolysis process. Simultaneously, it introduces a reaction mechanism function to constrain the transformation rate, thereby effectively constraining the pyrolysis reaction process and improving the problem of insufficient prediction accuracy in traditional methods.

Description

Technical Field

[0001] This invention relates to the field of thermal analysis kinetics technology, and in particular to a method and system for predicting the transformation process of material pyrolysis components based on mechanistic constraints. Background Technology

[0002] In materials pyrolysis research, thermogravimetric analysis (TG / DTG) is a commonly used experimental method to reveal the mass change patterns of materials under different heating conditions. However, due to limitations such as experimental cycle, operating conditions, and equipment conditions, experimental methods cannot fully cover the various heating paths and reaction conditions in the actual pyrolysis process. Therefore, it is necessary to use pyrolysis kinetic models to describe and predict the transformation process of materials during pyrolysis to compensate for the shortcomings of experimental research.

[0003] In existing technologies, various explorations have been conducted on modeling and analyzing the component transformation process of material pyrolysis. For example, some studies have constructed isothermal pyrolysis models for photovoltaic laminate materials to evaluate pyrolysis efficiency under specific mechanistic function conditions; in biomass pyrolysis research, some literature describes the evolution of density and mass of each component during pyrolysis using mass loss equations; in addition, some studies have introduced machine learning methods to predict thermogravimetric curves and differential thermogravimetric curves under given heating rates. Although the above methods have improved the characterization and prediction capabilities of pyrolysis processes to some extent, existing technologies mostly focus on fitting macroscopic thermogravimetric curves or empirical predictions, with insufficient explicit introduction and system constraints of kinetic mechanism functions in the pyrolysis reaction, resulting in limited accuracy in component transformation prediction. Summary of the Invention

[0004] Based on the shortcomings of the existing technology, the present invention provides a method and system for predicting the transformation process of material pyrolysis components based on mechanism constraints. This solves the problem that the existing technology focuses on fitting macroscopic thermogravimetric curves or empirical prediction, and lacks explicit introduction and system constraints of kinetic mechanism functions in the pyrolysis reaction, resulting in limited accuracy of component transformation prediction.

[0005] The present invention adopts the following technical solution: In a first aspect, the present invention provides a method for predicting the transformation process of material pyrolysis components based on mechanism constraints, comprising the following steps: Obtain thermogravimetric data of the target material during the pyrolysis process; The initial mass term in the rate change equation of the basic equation of thermal analysis kinetics is replaced with the real-time mass term, while the mechanism function term is retained, to construct a predictive model for the pyrolysis component conversion reaction process. Based on thermogravimetric data, the kinetic parameters in the pyrolysis process are determined. These kinetic parameters are then substituted into a pyrolysis component conversion reaction process prediction model to obtain the conversion process data of each component of the target material as a function of temperature or time during the pyrolysis process.

[0006] Preferably, the expression for the pyrolysis component conversion reaction process prediction model under a constant heating rate is: ; In the formula, m For quality, For conversion rate, For temperature, Pre-exponential factor, For the heating rate, For activation energy, The gas constant is... For reaction mechanism function, For the reaction to temperature T The quality at that time, i.e., the real-time quality item.

[0007] Preferably, the expression for the pyrolysis component conversion reaction process prediction model under isothermal conditions is: ; In the formula, t For time, For the pyrolysis reactants in t The quality of time.

[0008] Preferably, determining the kinetic parameters during the pyrolysis process based on thermogravimetric data specifically includes the following steps: Based on the thermogravimetric data, the kinetic parameters of the target pyrolysis reaction are obtained using the isothermal method; The apparent activation energy at different conversion rates was calculated using the isoconversion method, and combined with Malek... y The (α)–α method determines the corresponding mechanism function, and the apparent activation energy and mechanism function are substituted into the basic equation of thermal analysis kinetics to calculate the pre-exponential factor, thus obtaining multiple sets of kinetic parameters.

[0009] Preferred options also include: By substituting different kinetic parameters into the pyrolysis component conversion reaction process prediction model, the corresponding pyrolysis reaction process prediction data are calculated. By comparing the coefficients of determination between predicted pyrolysis reaction progress data and experimental thermogravimetric data under different heating rates, the optimal kinetic parameters were obtained by screening the combinations of kinetic parameters.

[0010] Secondly, the present invention provides a material pyrolysis component transformation process prediction system based on mechanism constraints, comprising: The acquisition module is used to acquire thermogravimetric data of the target material during the pyrolysis process; A module is built to replace the initial mass term in the rate change equation of the basic equation of thermal analysis kinetics with the real-time mass term, while retaining the mechanism function term, to build a predictive model for the pyrolysis component conversion reaction process; The prediction module is used to determine the kinetic parameters in the pyrolysis process based on thermogravimetric data. The kinetic parameters are then substituted into the pyrolysis component conversion reaction process prediction model to obtain the conversion process data of each component of the target material as a function of temperature or time during the pyrolysis process.

[0011] Compared with the prior art, the above-mentioned at least one technical solution adopted by the present invention can achieve the following beneficial effects: This invention modifies the mass term in the rate change equation of the basic equation of thermal analysis kinetics, changing it from a constant value to a dynamic value that changes with the pyrolysis process. At the same time, a reaction mechanism function is introduced to constrain the conversion rate, thereby effectively constraining the pyrolysis reaction process. This invention systematically characterizes the component conversion reaction process of materials during pyrolysis, enabling quantitative prediction of the pyrolysis reaction process and improving the problem of insufficient prediction accuracy of traditional methods. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a method for predicting the transformation process of material pyrolysis components based on mechanism constraints according to the present invention. Figure 2 This is a simplified diagram of the pyrolysis reaction path of the TPT backsheet of the present invention under a nitrogen atmosphere; Figure 3 The TPT backplane ln( ) of this invention is fitted using the mechanism function F2. k )~1000 / T Line graph; in, Figure 3 (a): F2: α =0~1, Figure 3 (b): F2: α =0.02~0.92; Figure 4 This is a comparison chart of the calculated results and experimental thermogravimetric values ​​of the TPT backplate of the present invention at different heating rates. in, Figure 4 (a): 5℃ / min Figure 4 (b): 10℃ / min Figure 4 (c): 20℃ / min Figure 4 (d): 40℃ / min; Figure 5This is a comparison chart of the residuals between the calculated results and experimental thermogravimetric values ​​of the TPT backplate of the present invention at different heating rates. in, Figure 5 (a): 5℃ / min Figure 5 (b): 10℃ / min Figure 5 (c): 20℃ / min Figure 5 (d): 40℃ / min; Figure 6 This is a simplified diagram of the pyrolysis reaction path of the EVA film of the present invention under a nitrogen atmosphere; Figure 7 This is a diagram showing the optimal results of the EVA film of the present invention at different heating rates; in, Figure 7 (a): 5℃ / min Figure 7 (b): 10℃ / min Figure 7 (c): 20℃ / min Figure 7 (d): 40℃ / min; Figure 8 This is a residual plot showing the optimal results of the EVA film of the present invention at different heating rates. in, Figure 8 (a): 5℃ / min Figure 8 (b): 10℃ / min Figure 8 (c): 20℃ / min Figure 8 (d): 40℃ / min; Figure 9 This is a flowchart of a method for predicting the transformation process of material pyrolysis components based on mechanistic constraints according to the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0015] On the one hand, existing technologies mostly focus on fitting macroscopic thermogravimetric curves or empirical predictions, with insufficient explicit introduction and system constraints of kinetic mechanism functions in pyrolysis reactions. This makes it difficult to fully characterize the transformation relationships between components in multi-stage pyrolysis from the perspective of kinetic parameters, resulting in limited accuracy in component transformation prediction. On the other hand, the lack of a unified screening and verification mechanism among different kinetic parameter acquisition methods leads to uncertainty in the physical rationality and applicability of model prediction results.

[0016] This invention proposes a method and system for constructing a computational model of the pyrolysis reaction process from the perspective of component transformation, using pyrolysis kinetic parameters as the core input and combining reaction mechanism function constraints. This model enables continuous prediction of the transformation process of pyrolysis components such as gases, solids, and intermediate products during the pyrolysis process, thereby providing a reliable computational method for modeling and analyzing the pyrolysis process of materials.

[0017] This invention combines the fundamental equations of thermal analysis kinetics with a biomass pyrolysis particle model to construct a predictive model for the conversion process of pyrolysis components based on reaction mechanism constraints. This model can not only verify the physical rationality of the three kinetic factors but also characterize the conversion process of different product components during multi-stage pyrolysis. (Refer to...) Figure 1 and Figure 9 This includes the following steps:

[0018] Step 1: Obtain thermogravimetric data of the target material during the pyrolysis process.

[0019] Step 2: Replace the initial mass term in the rate change equation of the basic equation of thermal analysis kinetics with the real-time mass term, and retain the mechanism function term to construct a predictive model for the pyrolysis component conversion reaction process.

[0020] Step 3: Determine the kinetic parameters in the pyrolysis process based on thermogravimetric data, and substitute the kinetic parameters into the pyrolysis component conversion reaction process prediction model to obtain the conversion process data of each component of the target material as a function of temperature or time during the pyrolysis process.

[0021] Fundamental equations of thermal analysis kinetics: (1); In the formula, For conversion rate, For temperature, Pre-exponential factor, For the heating rate, For activation energy, The gas constant is... These are reaction mechanism functions, including n-order reactions (Fn), one-dimensional diffusion (D1), two-dimensional diffusion (D2), three-dimensional diffusion (Jander) (D3), three-dimensional diffusion (Ginstring-Brounshtein) (D4), nucleation and growth (JMA) (Am), and phase interface reactions (Rn), etc.

[0022] (2); In the formula, , and These are the pyrolysis reactants at the initial time, t The quality of the moment and the final moment.

[0023] Using the fundamental equations of thermal kinetics, we can obtain the expression characterizing the rate of mass change of a material with temperature at a certain heating rate, which is: (3); If the pyrolysis reactants are completely pyrolyzed, that is Then the above formula becomes: .

[0024] The difference between the pyrolysis component conversion reaction process prediction equation based on reaction mechanism constraints and the rate change equation based on the fundamental equation of thermal analysis kinetics lies in the different ways in which the mass term of the pyrolysis material is set. In the actual pyrolysis process, when the reactants are basically consumed, the mechanism function... The mass loss rate is not necessarily zero. If the rate change formula (3) is directly used, it may lead to a non-zero mass loss rate when the mass approaches zero, resulting in physically unreasonable calculation results. This invention modifies the mass term on the right side of the equation by introducing the component transformation relationship, changing it from a constant value to a dynamic value that changes with the pyrolysis process. This effectively constrains the pyrolysis reaction process and avoids the physically unreasonable phenomenon of a non-zero mass loss rate when the material mass approaches zero.

[0025] Mass loss equation for biomass pyrolysis process: (4); In the formula, Let be the density. Equation (3) is similar to equation (4), except that the initial mass in equation (3) is replaced with the mass at temperature T after the reaction, while retaining the mechanism function term. The expression for this equation at a constant heating rate is:

[0026] (5); Equation (5) can also be transformed into a prediction model under constant temperature conditions, and its expression is: (6); The pyrolysis component transformation reaction process prediction model based on reaction mechanism constraints is further transformed into a difference form during actual numerical calculations: (7); (8); In the formula, ∆ m This represents the change in material mass over a single temperature step. m T For temperature T The mass of the material at that time, ∆ T The temperature step size affects the calculation accuracy.

[0027] The following examples illustrate the pyrolysis process of TPT backsheets and EVA films in retired crystalline silicon photovoltaic modules under a nitrogen atmosphere. The component transformation process of the materials in single-stage and two-stage pyrolysis processes is predicted and analyzed to specifically explain the method of the present invention.

[0028] Example 1 (single-stage pyrolysis process).

[0029] This embodiment describes a mechanism-constrained method for predicting the transformation process of material pyrolysis components in a single pyrolysis stage. Taking the pyrolysis process of a TPT backsheet under a nitrogen atmosphere as an example, the specific implementation steps are as follows: S1: Weigh a TPT backplate sample with a mass of 10±1 mg. In a nitrogen atmosphere with a flow rate of 50 mL / min, heat the sample from 30 °C to 1000 °C at heating rates of 5 °C / min, 10 °C / min, 20 °C / min and 40 °C / min, and record the thermogravimetric data of the sample mass change with temperature.

[0030] S21: Based on the thermogravimetric data obtained in step S1, take the experimental results at 5℃ / min, and use the isothermal method to fit the kinetic parameters of the TPT backplate thermogravimetric data to obtain the kinetic three-factor combination.

[0031] The isothermal method involves fitting and solving the thermogravimetric data of a material at a specific heating rate to obtain the activation energy, pre-exponential factor, and reaction mechanism function. Its expression is: (9); It should be noted that during the isothermal fitting process, the kinetic parameters and fitting accuracy corresponding to different reaction mechanism functions are closely related to the selected pyrolysis conversion rate range. To avoid the uncertainty caused by manually setting the conversion rate range, this invention adopts an adaptive adjustment method for the conversion rate range, where the lower limit of the conversion rate is set to 0.02~0.10 and the upper limit is set to 0.90~0.95. For each mechanism function, the conversion rate range corresponding to the fitting result with the highest coefficient of determination is selected, and finally, the combination of kinetic parameters with a coefficient of determination greater than 0.9 is selected. Figure 3 To give the ln( when using the mechanism function F2) k )-1000 / T Relationship curve graph.

[0032] S22: Based on the thermogravimetric data obtained in step S1, calculate the apparent activation energy under different conversion rates using the isoconversion method, and combine it with Malek's method. y The (α)–α method determines the corresponding reaction mechanism function. Subsequently, the determined activation energy and reaction mechanism function are substituted into the basic equation of thermal analysis kinetics to calculate the corresponding pre-exponential factors, thereby obtaining multiple possible combinations of three factors for pyrolysis kinetics.

[0033] The isoconversion method is used to calculate the apparent activation energy at different conversion rates, and its expressions are as follows: KAS: (10); FWO: (11); Vyazovkin: (12); Malek y The (α)–α method is used to supplement the results obtained from the isoconversion method to determine the reaction mechanism function, and its expression is: (13); S3: Based on the pyrolysis diagram of the TPT backplate under a nitrogen atmosphere Figure 2 A predictive model for the pyrolysis component conversion reaction process of TPT based on reaction mechanism constraints is established, and its expression is: (14); In the formula, m The sample mass is represented by the subscripts TPT, Gas, and Char, which represent the TPT backplate, gaseous products, and solid carbon residue products, respectively. T 0、 T These represent the temperatures at the initial stage of the reaction and during the reaction process, respectively; ∆ T This refers to the temperature step size. x 1. x 2 represents the mass fractions of the gaseous product and the solid residual carbon product, respectively; ω % TPT,T for T The percentage of thermogravimetric analysis (TGA) on the TPT backplate.

[0034] Based on the three factors of TPT backplate pyrolysis kinetics determined by S2, the reaction rate constant can be further calculated. k . At the set temperature step ∆ T Under the condition of 0.001℃, combined with the pyrolysis experimental conditions determined in step S1, the pyrolysis reaction process data under different combinations of kinetic parameters can be obtained through numerical iterative calculation.

[0035] S4: By comparing the coefficients of determination between the model predictions and experimental thermogravimetric data under different heating rates, the resulting combinations of kinetic parameters are screened to obtain... Figure 4 The first five optimal results shown are Figure 5The residual plot is shown. Although the coefficients of determination for different calculation method combinations are all greater than 0.99 across the entire temperature range, seemingly indicating high prediction accuracy, the thermogravimetric changes of the TPT backsheet are mainly concentrated in the effective comparison interval (conversion rate 0.05~0.95), while the mass changes are smaller in the non-effective comparison interval. This leads to an inflated coefficient of determination across the entire temperature range, making it difficult to truly reflect the model's predictive ability. Therefore, this invention selects data within the effective comparison interval as the evaluation criterion. The results show that when the mechanistic function F2 is used in combination with the Vyazovkin method, the prediction results show the highest degree of agreement with the experimental data. The coefficients of determination are all greater than 0.99 across the entire temperature range, and all greater than 0.95 within the effective comparison interval. The maximum absolute residual does not exceed 9.8%, and the sum of squared residuals is less than 2.5.

[0036] Equation (8) is used in numerical simulations to calculate the dynamic changes in the mass of each component. In numerical simulations, time is usually used as the process variable for transient solutions, so the reaction process is often described with time as the independent variable.

[0037] Example 2 (Two-stage pyrolysis process) This embodiment describes a mechanism-constrained method for predicting the conversion process of pyrolysis components in two pyrolysis stages. Taking the pyrolysis process of EVA film under a nitrogen atmosphere as an example, the specific implementation steps are as follows: S5: Weigh an EVA film sample with a mass of 10±1 mg. Under a nitrogen atmosphere with a flow rate of 50 mL / min, heat the sample from 30℃ to 700℃ at heating rates of 5℃ / min, 10℃ / min, 20℃ / min and 40℃ / min, and record the thermogravimetric data of the sample mass change with temperature.

[0038] S61: Based on the thermogravimetric data obtained in step S5, using the experimental results at 5℃ / min, the isothermal method is employed to fit the kinetic parameters of the EVA pyrolysis process to obtain a combination of three kinetic factors. It should be noted that since the pyrolysis process of the EVA film includes two distinct reaction stages, the kinetic parameters and fitting accuracy corresponding to different reaction stages are closely related to the selected pyrolysis conversion rate range. Therefore, during the isothermal fitting process, it is necessary to divide the temperature range corresponding to the two pyrolysis stages and perform kinetic parameter fitting calculations for each stage separately.

[0039] S62: Based on the thermogravimetric data obtained in step S5, calculate the apparent activation energy under different conversion rates using the isoconversion method, and combine it with Malek... yThe (α)–α method determines the reaction mechanism function corresponding to each pyrolysis stage. The determined apparent activation energy and mechanism function are substituted into the basic equation of thermal analysis kinetics to calculate the pre-exponential factor corresponding to each pyrolysis stage, thereby obtaining multiple possible combinations of three factors for EVA pyrolysis kinetics.

[0040] S7: Based on the pyrolysis diagram of EVA film under nitrogen atmosphere Figure 6 A predictive model for the pyrolysis component conversion reaction process of EVA based on reaction mechanism constraints is established, and its expression is as follows: (15); In the formula, m The mass of the sample is EVA; EVA, Gas, and IS are EVA film, gaseous product, and intermediate solid product, respectively. T 0、 T These represent the temperatures at the initial stage of the reaction and during the reaction process, respectively; ∆ T This refers to the temperature step size. x 1. x 2 represents the mass fractions of the gaseous products and intermediate solid products directly generated during the pyrolysis of EVA, respectively. k 1. k 2 represents the reaction rate constants of the EVA film in the first and second stages of pyrolysis, respectively; ω% EVA,T for T The percentage of EVA in terms of thermogravimetric density.

[0041] Based on the three EVA pyrolysis kinetic factors determined in S6, the reaction rate constants corresponding to each pyrolysis stage can be further calculated. This is done within a set temperature step. T Under the condition of 0.001℃, combined with the pyrolysis experimental conditions determined in step S5, the pyrolysis reaction process data under different combinations of kinetic parameters can be obtained through numerical iterative calculation.

[0042] S7: By comparing the coefficients of determination between the model predictions and experimental thermogravimetric data under different heating rates, the resulting combinations of kinetic parameters are screened to obtain... Figure 7 The optimal results shown are Figure 8 The residual plot is shown.

[0043] Although the coefficients of determination for different calculation method combinations are all greater than 0.99 across the entire temperature range, seemingly indicating high prediction accuracy, the thermogravimetric changes of EVA are also mainly concentrated in the effective comparison range (conversion rate 0.05~0.99), while the mass changes are smaller in the non-effective comparison range. This leads to an inflated coefficient of determination across the entire temperature range, making it difficult to truly reflect the model's predictive ability. Therefore, this invention also selects data from the effective comparison range as the evaluation criterion.

[0044] The results show that when the Vyazovkin method combined with the mechanism function A3 / 2 is used in the first pyrolysis stage and the KAS method combined with the mechanism function A2 is used in the second pyrolysis stage, the model prediction results are in the best agreement with the experimental data. The coefficient of determination is greater than 0.99 in the entire temperature range, the coefficient of determination is greater than 0.99 in the effective comparison interval, the sum of squared residuals is less than 1, and the maximum absolute residual does not exceed 5.3%.

[0045] During implementation, the proposed verification model was calculated using MATLAB programming, supporting reaction process prediction and analysis under multiple heating rate conditions and multi-stage pyrolysis processes. For the differential form of the prediction model, the rectangular finite difference method was used for numerical solution, enabling continuous calculation of the changes of each component with temperature or time during pyrolysis. This effectively simplified the integration process while ensuring computational accuracy.

[0046] Compared with existing methods for predicting pyrolysis reaction processes, the advantages of this invention are as follows: by introducing a reaction mechanism function to constrain the conversion rate, the component conversion reaction process of the material during pyrolysis can be systematically characterized, enabling quantitative prediction of the pyrolysis reaction process and improving the problem of insufficient prediction accuracy of traditional methods; at the same time, this method is applicable to the prediction and analysis of multi-stage pyrolysis reaction processes, providing a reliable calculation method for pyrolysis process modeling and mechanism research.

[0047] Based on the same concept, the present invention also provides a material pyrolysis component transformation process prediction system based on mechanism constraints, including an acquisition module, a construction module and a prediction module.

[0048] The acquisition module is used to acquire thermogravimetric data of the target material during the pyrolysis process.

[0049] The building module is used to replace the initial mass term in the rate change equation of the basic equation of thermal analysis kinetics with the real-time mass term, while retaining the mechanism function term, to build a predictive model for the pyrolysis component conversion reaction process.

[0050] The prediction module is used to determine the kinetic parameters in the pyrolysis process based on thermogravimetric data. The kinetic parameters are then substituted into the pyrolysis component conversion reaction process prediction model to obtain the conversion process data of each component of the target material as a function of temperature or time during the pyrolysis process.

[0051] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0052] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for predicting the transformation process of material pyrolysis components based on mechanism constraints, characterized in that, Includes the following steps: Obtain thermogravimetric data of the target material during the pyrolysis process; The initial mass term in the rate change equation of the basic equation of thermal analysis kinetics is replaced with the real-time mass term, while the mechanism function term is retained, to construct a predictive model for the pyrolysis component conversion reaction process. Based on thermogravimetric data, the kinetic parameters in the pyrolysis process are determined. These kinetic parameters are then substituted into a pyrolysis component conversion reaction process prediction model to obtain the conversion process data of each component of the target material as a function of temperature or time during the pyrolysis process.

2. The method for predicting the transformation process of material pyrolysis components based on mechanism constraints as described in claim 1, characterized in that, The expression for the pyrolysis component conversion reaction process prediction model under a constant heating rate is: ； In the formula, m For quality, For conversion rate, For temperature, Pre-exponential factor, For the heating rate, For activation energy, The gas constant is... For reaction mechanism function, For the reaction to temperature T The quality at that time, i.e., the real-time quality item.

3. The method for predicting the transformation process of material pyrolysis components based on mechanism constraints as described in claim 2, characterized in that, The expression for the pyrolysis component conversion reaction process prediction model under isothermal conditions is: ； In the formula, t For time, For the pyrolysis reactants in t The quality of time.

4. The method for predicting the transformation process of material pyrolysis components based on mechanism constraints as described in claim 1, characterized in that, The determination of kinetic parameters in the pyrolysis process based on thermogravimetric data specifically includes the following steps: Based on the thermogravimetric data, the kinetic parameters of the target pyrolysis reaction are obtained using the isothermal method; The apparent activation energy at different conversion rates was calculated using the isoconversion method, and combined with Malek... y The (α)–α method determines the corresponding mechanism function, and the apparent activation energy and mechanism function are substituted into the basic equation of thermal analysis kinetics to calculate the pre-exponential factor, thus obtaining multiple sets of kinetic parameters.

5. The method for predicting the transformation process of material pyrolysis components based on mechanism constraints as described in claim 4, characterized in that, Also includes: By substituting different kinetic parameters into the pyrolysis component conversion reaction process prediction model, the corresponding pyrolysis reaction process prediction data are calculated. By comparing the coefficients of determination between predicted pyrolysis reaction progress data and experimental thermogravimetric data under different heating rates, the optimal kinetic parameters were obtained by screening the combinations of kinetic parameters.

6. A material pyrolysis component transformation process prediction system based on mechanism constraints, characterized in that, include: The acquisition module is used to acquire thermogravimetric data of the target material during the pyrolysis process; A module is built to replace the initial mass term in the rate change equation of the basic equation of thermal analysis kinetics with the real-time mass term, while retaining the mechanism function term, to build a predictive model for the pyrolysis component conversion reaction process; The prediction module is used to determine the kinetic parameters in the pyrolysis process based on thermogravimetric data. The kinetic parameters are then substituted into the pyrolysis component conversion reaction process prediction model to obtain the conversion process data of each component of the target material as a function of temperature or time during the pyrolysis process.

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