Method and system for obtaining thermal decomposition rate parameters of solid materials, and storage medium

Abstract

To acquire a thermal decomposition speed parameter for a solid material.SOLUTION: A method for acquiring a thermal decomposition speed parameter for a solid material includes the steps of: collecting an experiment curve of mass loss percent along with a change of the temperature or time of a thermal decomposition material sample; constructing a library of dynamic mechanism functions, and scanning each dynamic mechanism function to acquire a simulation curve of a change of the mass loss percent along with a change of the temperature or time corresponding to each dynamic mechanism function; calculating the root-mean-square error (RMSE) of the simulation curve of the mass loss percent along with the change of the temperature or time, and of the experiment curve of the mass loss percent along with the change of the temperature and time; and sorting the plurality of root-mean-square errors (RMSE), and determining the dynamic mechanism function corresponding to the minimum root-mean-square error (RMSE) as the thermal decomposition speed parameter for the solid material.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This application claims priority to Chinese Patent Application No. 202410203886.3, filed on 23 February 2024, which is incorporated herein by reference in its entirety.

[0002] This invention belongs to the technical field of pyrolysis rate parameter analysis, and more specifically, to a method and system for obtaining pyrolysis rate parameters of solid materials, and a storage medium. [Background technology]

[0003] Thermal decomposition kinetics is a field of study that investigates the dynamics of physical changes or chemical reactions using thermal analysis and calorimetry techniques. In the study of pyrolysis kinetics, the most probable mechanism function is one of the important pyrolysis rate parameters in solid materials.

[0004] In related technologies, the most probable mechanism function is often obtained through subjective or approximate inference methods. As a result, the obtained most probable mechanism function is often inaccurate, requiring iterative verification inference, which seriously impacts the efficiency of obtaining parameters for the thermal decomposition rate of solid materials. [Overview of the project]

[0005] The object of the present invention is to provide a method and system for obtaining the thermal decomposition rate parameters of solid materials, and a storage medium.

[0006] To solve the above technical problems, the present invention provides a method for obtaining the thermal decomposition rate parameters of a solid material. This method is The steps include: collecting experimental curves of the percentage of mass loss as the temperature or time changes in a pyrolysis material sample; The steps include: constructing a library of dynamic mechanism functions and scanning each dynamic mechanism function to obtain a simulation curve of the percentage of mass loss with respect to temperature or time corresponding to each dynamic mechanism function; The steps include: calculating the root mean square error RMSE between the simulated curve of the mass loss percentage with respect to temperature or time, and the experimental curve of the mass loss percentage with respect to temperature or time; The method includes the steps of sorting multiple root mean square errors (RMSE) and using the dynamic mechanism function corresponding to the smallest RMSE as the thermal decomposition rate parameter for the solid material. [Effects of the Invention]

[0007] The beneficial effect of the present invention is that, in the method for obtaining the thermal decomposition rate parameters of solid materials, each dynamic mechanism function is scanned sequentially according to the sequence number, and during the scanning process, the activation energy E and frequency factor A of the dynamic parameters of each dynamic mechanism function are optimized based on the quasi-Newton method, reducing the error between the simulation curve of the mass loss percentage with respect to temperature or time and the experimental curve of the mass loss percentage with respect to temperature or time, and optimizing multiple root mean square error RMSE values ​​to the minimum, approaching the theoretical minimum. This ensures the accuracy of sorting the dynamic mechanism functions and, at the same time, ensures globality by scanning all dynamic mechanism functions in the function library sequentially. As a result, the accuracy of the obtained most probable mechanism function is increased, and the efficiency of obtaining the thermal decomposition rate parameters of solid materials can be improved.

[0008] Other features and advantages of the present invention are described in the following specification, and some are evident from the specification or understood by practicing the invention. The object and other advantages of the present invention are realized and obtained by the structures specifically pointed out in the specification and drawings.

[0009] To make the above-mentioned objectives, features, and advantages of the present invention easier to understand, preferred embodiments are given below and described in detail in conjunction with the accompanying drawings. [Brief explanation of the drawing]

[0010] To more clearly describe specific embodiments of the present invention or technical solutions in the prior art, the drawings that are necessary for describing specific embodiments or prior art are briefly described below. The drawings described below are some embodiments of the present invention, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without any creative effort.

[0011] [Figure 1] Figure 1 is a diagram illustrating, in sequence, methods for obtaining the thermal decomposition kinetic parameters of solid materials according to several embodiments. [Figure 2] Figure 2 is a schematic diagram comparing the third most probable mechanism function with experimental data in several embodiment cases. [Figure 3] Figure 3 is a schematic diagram comparing the tenth non-most probable mechanism function with experimental data in several embodiment cases. [Figure 4] Figure 4 is a schematic block diagram of a system for acquiring solid material pyrolysis rate parameters according to several embodiments. [Figure 5] Figure 5 is a schematic block diagram of an electronic device according to several embodiments. [Modes for carrying out the invention]

[0012] To further clarify the object, technical aspects and advantages of the embodiments of the present invention, the technical aspects of the present invention will be described below clearly and completely in reference to the accompanying drawings, although it is clear that the embodiments described are only some embodiments of the present invention and not all embodiments. All other embodiments obtained based on the embodiments of the invention, without creative work by a person skilled in the art, are within the scope of the protection of the invention.

[0013] In mechanism-related technologies, the acquisition of the most probable mechanism function in the thermal decomposition rate parameters of solid materials is often obtained by subjective reasoning or approximate reasoning methods. As a result, the obtained most probable mechanism function is often inaccurate, requiring iterative verification reasoning, which seriously affects the acquisition efficiency of the thermal decomposition rate parameters of solid materials.

[0014] Accordingly, at least one embodiment provides a method for acquiring the thermal decomposition rate parameter of a solid material. This method comprises: collecting an experimental curve of mass loss percentage with the change of temperature or time of a pyrolysis material sample; constructing a library of dynamic mechanism functions, and scanning each dynamic mechanism function to obtain a simulation curve of mass loss percentage with the change of temperature or time corresponding to each dynamic mechanism function; respectively calculating the root mean square error (RMSE) between the simulation curve of mass loss percentage with the change of temperature or time and the experimental curve of mass loss percentage with the change of temperature or time; and sorting a plurality of root mean square errors RMSE, and taking the dynamic mechanism function corresponding to the minimum root mean square error RMSE as the thermal decomposition rate parameter of the solid material.

[0015] Various non-limiting embodiments of the embodiments of the present disclosure are described in detail below in conjunction with the accompanying drawings.

[0016] As shown in Figure 1, some embodiments provide a method for acquiring the thermal decomposition rate parameter of a solid material. This method includes the following steps S101 to S104.

[0017] In step S101, an experimental curve of mass loss percentage with the change of temperature or time of a pyrolysis material sample is collected.

[0018] In step S102, a library of dynamic mechanism functions is constructed, each dynamic mechanism function is scanned, and a simulation curve of the percentage of mass loss with respect to temperature or time corresponding to each dynamic mechanism function is obtained.

[0019] In step S103, the root mean square error RMSE is calculated between the simulated curve of the mass loss percentage with respect to temperature or time, and the experimental curve of the mass loss percentage with respect to temperature or time.

[0020] In step S104, multiple root mean square errors (RMSE) are sorted, and the dynamic mechanism function corresponding to the smallest root mean square error (RMSE) is used as the thermal decomposition rate parameter for the solid material.

[0021] Specifically, at least one embodiment includes, but is not limited to, the library of 41 common dynamic mechanism functions shown in Table 1 below. Here, α is the conversion rate, referring to the percentage or fraction of the conversion of a certain reactant. G(α) and f(α) are the integral and differential mechanism functions, respectively.

[0022] Table 1. Library of Common Dynamic Mechanism Functions [Table 1a] [Table 1b]

[0023] In some embodiments, the step of obtaining a simulation curve of the percentage of mass loss with respect to temperature or time change corresponding to each dynamic mechanism function by scanning each dynamic mechanism function is: The steps include setting the initial activation energy E and the initial frequency factor A, Each dynamic mechanism function is scanned sequentially, and simultaneously, the initial activation energy E and initial frequency factor A are optimized based on the quasi-Newton method, and the optimization equation is:

number

[0024] Specifically, the initial activation energy E and frequency factor A are both artificially assigned initial values. For example, while not limited to these, the initial value of E could be 70000 and the initial value of A could be e^10, or these values ​​could be within ±10% of each other.

[0025] In the method for obtaining the thermal decomposition rate parameters of solid materials in this embodiment, each dynamic mechanism function is scanned sequentially according to the sequence number, and during the scanning process, the activation energy E and frequency factor A of the dynamic parameters are optimized based on the quasi-Newton method. Next, each dynamic mechanism function is used as a simulation model, and the optimized activation energy E and frequency factor A are used as parameters of the simulation model to obtain a simulation curve of the mass loss percentage with respect to temperature or time. Next, the root mean square error RMSE between the experimental curve of the mass loss percentage with respect to temperature or time and the simulation curve of the mass loss percentage with respect to temperature or time is used as the target function, and 41 root mean square error RMSE values ​​are obtained. These root mean square error RMSE values ​​are sorted, and the dynamic mechanism function corresponding to the smallest root mean square error RMSE value is set as the most probable mechanism function. To optimize both the activation energy E and frequency factor A parameters of the simulation model for each dynamic mechanism function, the error between the simulation curve of the mass loss percentage with respect to temperature or time changes and the experimental curve of the mass loss percentage with respect to temperature or time changes is reduced. By optimizing multiple root mean square error RMSE values ​​to minimize them and approaching the theoretical minimum, the accuracy of sorting the dynamic mechanism functions is ensured. At the same time, all dynamic mechanism functions in the function library are sequentially scanned (traversal), ensuring the globality of the most probable mechanism functions. As a result, the accuracy of the obtained most probable mechanism functions is increased, improving the efficiency of obtaining parameters for the thermal decomposition rate of solid materials.

[0026] The final output most probable mechanism function, the activation energy E as a dynamic parameter of the most probable mechanism function, the frequency factor A as a dynamic parameter of the most probable mechanism function, and the root mean square error RMSE of the experimental curve of the mass loss percentage with respect to temperature or time are used as parameters for the thermal decomposition rate of the solid material. More accurate dynamic mechanism functions and dynamic parameters are useful for constructing more accurate application models for industrial processes such as thermal decomposition, gasification, and combustion. Furthermore, by more accurately understanding the operating conditions inside the reactor through computational fluid dynamics (CFD), the development of related reactors can be promoted.

[0027] In some embodiments, the step of obtaining an experimental curve of the percentage mass loss with respect to a change in temperature or time of a pyrolysis material sample is: In a non-isothermal thermal decomposition mode, the steps include: collecting mass loss data as samples from thermal decomposition material samples corresponding to multiple thermal decomposition heating rates; The process includes a step of obtaining an experimental curve of the percentage of mass loss with respect to changes in temperature or time, using the thermal decomposition time and temperature value as inputs and the percentage of mass loss as output for each thermal decomposition heating rate.

[0028] In some embodiments, the steps for calculating the root mean square error RMSE between a simulated curve of the mass loss percentage with respect to temperature or time and an experimental curve of the mass loss percentage with respect to temperature or time are given by the following equation:

number

[0029] In some embodiments, the step of sorting multiple root mean square errors RMSE and using the dynamic mechanism function corresponding to the smallest root mean square error RMSE as the dynamic parameter for the thermal decomposition of the solid material is: The first mechanism function sorted in ascending order based on the root mean square error RMSE is defined as the most probable mechanism function. This includes the step of using the most probable mechanism function as the thermal decomposition rate parameter of the solid material.

[0030] In some embodiments, a method for obtaining the thermal decomposition rate parameter of a solid material is: The method further includes the step of using the activation energy E of the most probable mechanism function, the frequency factor A of the most probable mechanism function, and the least root mean square error RMSE as parameters for the thermal decomposition rate of the solid material.

[0031] Assuming that the third mechanism function obtained by the method for acquiring the thermal decomposition rate parameters of the solid material in this embodiment is the most probable mechanism function, a comparison with the experimental data is shown in Figure 2, and it is clear that the root mean square error (RMSE) is small. A comparison with the experimental data of the tenth mechanism function, which is not the most probable mechanism function, is shown in Figure 3, and it is clear that its root mean square error (RMSE) is large.

[0032] As shown in Figure 4, some embodiments further provide a system for obtaining the thermal decomposition rate parameters of solid materials. This system includes a computer device, the computer device is A storage unit configured to store experimental curves of the percentage of mass loss of collected pyrolysis material samples with respect to changes in temperature or time, A scanning simulation unit is configured to construct a library of dynamic mechanism functions and, by scanning each dynamic mechanism function, obtain a simulation curve of the percentage of mass loss with respect to temperature or time corresponding to each dynamic mechanism function. A computing unit configured to calculate the root mean square error RMSE between a simulation curve of the percentage mass loss with respect to temperature or time, and an experimental curve of the percentage mass loss with respect to temperature or time, The system includes a sorting unit configured to sort multiple root mean square errors (RMSE) and to use the dynamic mechanism function corresponding to the smallest root mean square error (RMSE) as the pyrolysis rate parameter of the solid material.

[0033] The specific functions related to the memory unit, scan simulation unit, calculation unit, and sorting unit are then implemented in a computer device.

[0034] In some embodiments, the memory unit stores experimental curves, specifically, in a non-isothermal thermal decomposition mode, it collects mass loss data of thermal decomposition material samples corresponding to multiple thermal decomposition heating rates as samples, and for each thermal decomposition heating rate, it takes the thermal decomposition time and temperature value as inputs and outputs the mass loss percentage, obtaining and storing experimental curves of the mass loss percentage as a result of changes in temperature or time.

[0035] In some embodiments, the storage unit is further configured to store a library of dynamic mechanism functions, simulation curves of mass loss percentage with changes in temperature or time, root mean square error RMSE, and sorting results of a plurality of root mean square error RMSEs.

[0036] In some embodiments, the storage unit may be regarded as a data storage space.

[0037] In some embodiments, the scanning simulation unit constructs a library of dynamic mechanism functions, and by scanning each dynamic mechanism function, obtaining the simulation curve of mass loss percentage with changes in temperature or time corresponding to each dynamic mechanism function refers to obtaining the simulation curve of mass loss percentage with changes in temperature or time corresponding to each dynamic mechanism function by scanning each dynamic mechanism function, that is, setting the initial activation energy E and the initial frequency factor A, scanning each dynamic mechanism function in sequence, optimizing the initial activation energy E and the initial frequency factor A based on the quasi-Newton method simultaneously with the scanning, wherein the optimization goal is to minimize the function RMSE(E,A), and the optimization formula is [Formula] , and obtaining the optimized activation energy E and frequency factor A corresponding to each dynamic mechanism function, wherein in this formula, E a+1 represents the activation energy value at the (a+1)-th iteration in the optimization process, which depends on the activation energy value and gradient information at the a-th iteration, E a represents the activation energy value at the a-th iteration in the optimization process, which takes the initial activation energy value when a=1, A a+1This represents the frequency factor at the (a+1)th iteration in the optimization process, and depends on the frequency factor and gradient information at the a-th iteration. A a This represents the frequency factor at the a-th iteration in the optimization process, where the initial frequency factor is taken when a=1. RMSE(E a ,A a ) is the root mean square error between the simulation curve and the experimental curve for the percentage of mass loss with respect to temperature or time, when the activation energy is Ea and the frequency factor is Aa, and Each dynamic mechanism function is represented as a thermogravimetric analysis simulation model, and the optimized activation energy E and frequency factor A are used as parameters for each thermogravimetric analysis simulation model to perform simulations. The simulation curve of the percentage of mass loss with respect to temperature or time corresponding to each dynamic mechanism function is output, where a is the number of iterations in the optimization process and a ≥ 1.

[0038] In some embodiments, the calculation unit calculates the root mean square error RMSE between the simulated curve of the mass loss percentage with respect to temperature or time and the experimental curve of the mass loss percentage with respect to temperature or time, respectively. formula,

number

[0039] In some embodiments, a sorting unit sorts multiple root mean square errors (RMSEs), and the dynamic mechanism function corresponding to the smallest root mean square error (RMSE) is used as the dynamic parameter for the thermal decomposition of the solid material. The most probable mechanism function is defined as the first dynamic mechanism function obtained by sorting all dynamic mechanism functions in ascending order based on the root mean square error (RMSE), and, This involves using the most probable mechanism function as the parameter for the thermal decomposition rate of the solid material.

[0040] The electronic device in the embodiments of this disclosure will be described below from the perspective of hardware processing.

[0041] Some embodiments also provide a readable (computer-readable) storage medium that stores computer programs / instructions that, when executed by a processor, perform one of the above possible methods for obtaining thermal decomposition rate parameters of a solid material.

[0042] As shown in Figure 5, some embodiments further provide a computer device / system including memory, a processor, and a computer program stored in memory. The processor executes the computer program to perform one of the possible methods described above for obtaining the dynamic parameters of the thermal decomposition of a solid material.

[0043] In other embodiments, computer devices and industrial computers can also be considered types of electronic devices.

[0044] The structure shown in Figure 5 is not limited to electronic devices and may include fewer or more components than those shown, or may include combinations of specific components, or may include different arrangements of components.

[0045] In some embodiments, the communication interface may be a communication interface that can be connected to an external bus adapter, such as an RS232, RS485, USB port, and TYPE port. A wired or wireless network interface may also be included, and the network interface may optionally include wired and / or wireless interfaces (e.g., a Wi-Fi interface, a Bluetooth® interface, etc.) that are typically used to establish a communication connection between the computer device and other electronic devices.

[0046] Among these, the memory module, readable storage medium, or computer-readable storage medium includes at least one type of memory. The memory includes flash memory, hard disks, multimedia cards, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disks, optical disks, etc. In some embodiments, it may be an internal storage unit of the computer device, such as a hard disk of the computer device. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard disk equipped in the computer device, a Smart Media Card (SMC), a Secure Digital (SD), or a Flash Card. Furthermore, the memory may include both an internal storage unit and an external storage device of the computer device. The memory is used to store various data such as application software and computer program code installed in the computer device, as well as to temporarily store output data or data to be output.

[0047] In some embodiments, the processor may be a central processing unit (CPU), a controller, a single-chip computer, a microprocessor, or another data processing chip, and is used to execute process code stored in memory or to process data, for example, to run a computer program.

[0048] In some embodiments, the communication bus may be an input / output bus, such as a Peripheral Component Interconnect (PCI) bus or an Enhanced Industry Standard Architecture (EISA) bus. This bus can be divided into an address bus, a data bus, a control bus, and so on.

[0049] Optionally, the computer device may further include a user interface. The user interface may include input units such as a display and a keyboard, and optionally, the user interface may also include a standard wired interface and a wireless interface. Optionally, in some embodiments, the display or display module may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. In this case, the display or display module is also called a display screen or display unit for displaying information processed within the computer device and for displaying a visualized user interface.

[0050] Some embodiments provide a computer program product that includes a computer program / command, which, when executed by a processor, implements the method for obtaining the thermal decomposition kinetic parameters of the solid material described above.

[0051] In some embodiments of the present invention, the disclosed apparatus and methods can, of course, be implemented in other ways. The embodiments of the apparatus described above are merely illustrative; for example, flowcharts and block diagrams in the drawings illustrate implementable architectures, functions, and operations of apparatus, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or part of code. The module, program segment, or part of code contains executable commands for implementing one or more predetermined logical functions. In some alternative implementations, the functions represented in the blocks may occur in an order different from that shown in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes in reverse order depending on the related functions. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, may be implemented in a dedicated hardware-based system that performs a predetermined function or operation, or in a combination of dedicated hardware and computer instructions.

[0052] Furthermore, in each embodiment of the present invention, each functional module may be integrated to form a single independent part, each module may exist individually, or two or more modules may be integrated to form a single independent part.

[0053] The above functions can be implemented in the form of software function modules and, when sold or used as independent products, can be stored on a single computer-readable storage medium. Based on this understanding, the technical solutions of the present invention, in their essence or contribution to the prior art, or parts thereof, can be represented in the form of a software product. The computer software product is stored on a single storage medium and contains a plurality of commands that cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0054] By illustrating the preferred embodiments of the present invention described above, those skilled in the art can make various changes and modifications to the above-described content without departing from the technical spirit of the present invention. The technical scope of the present invention is not limited to the contents of the specification and must be determined based on the claims.

Claims

1. A method for obtaining thermal decomposition rate parameters of a solid material performed by a computer device, The steps include: collecting experimental curves of the percentage of mass loss as the temperature or time changes in a pyrolysis material sample; The steps include: constructing a library of dynamic mechanism functions and scanning each dynamic mechanism function to obtain a simulation curve of the percentage of mass loss with respect to temperature or time corresponding to each dynamic mechanism function; The steps include: calculating the root mean square error RMSE between the simulated curve of the mass loss percentage with respect to temperature or time, and the experimental curve of the mass loss percentage with respect to temperature or time; A method for obtaining the thermal decomposition rate parameter of a solid material, characterized by including the step of sorting multiple root mean square errors RMSE and using the dynamic mechanism function corresponding to the smallest root mean square error RMSE as the thermal decomposition rate parameter of the solid material.

2. The step of obtaining an experimental curve of the percentage of mass loss with respect to temperature or time changes in a pyrolysis material sample is: In a non-isothermal thermal decomposition mode, the steps include: collecting mass loss data as samples from thermal decomposition material samples corresponding to multiple thermal decomposition heating rates; A method for obtaining thermal decomposition rate parameters of a solid material according to claim 1, comprising the step of obtaining an experimental curve of the mass loss percentage with respect to changes in temperature or time, with thermal decomposition time and temperature value as inputs and mass loss percentage as output for each thermal decomposition heating rate.

3. The step of scanning each dynamic mechanism function to obtain a simulation curve of the percentage of mass loss with respect to temperature or time corresponding to each dynamic mechanism function is: The steps include setting the initial activation energy E and the initial frequency factor A, Each dynamic mechanism function is scanned sequentially, and simultaneously, the initial activation energy E and initial frequency factor A are optimized based on the quasi-Newton method. The goal of optimization is to minimize the function RMSE(E,A), and the optimization equation is: [Math 1] The step is to obtain the optimized activation energy E and frequency factor A corresponding to each dynamic mechanism function, and in this equation, E a+1 This represents the activation energy value at the (a+1)th iteration in the optimization process, and depends on the activation energy value and gradient information at the a-th iteration. E a This represents the activation energy value at the a-th iteration in the optimization process, where a=1 is the initial activation energy value. A a+1 This represents the frequency factor at the (a+1)th iteration in the optimization process, and depends on the frequency factor and gradient information at the a-th iteration. A a This represents the frequency factor at the a-th iteration in the optimization process, where the initial frequency factor is taken when a=1. RMSE(E a ,A a ) is the step, which is the root mean square error between the simulation curve and the experimental curve for the percentage of mass loss with respect to temperature or time, when the activation energy is Ea and the frequency factor is Aa. A method for obtaining thermal decomposition rate parameters of a solid material according to claim 1, comprising the steps of: using a thermogravimetric analysis simulation model for each dynamic mechanism function; performing simulations using the optimized activation energy E and frequency factor A as parameters for each thermogravimetric analysis simulation model; and outputting a simulation curve of the percentage of mass loss with respect to temperature or time corresponding to each dynamic mechanism function, wherein a is the number of iterations in the optimization process and a ≥ 1.

4. The steps for calculating the root mean square error RMSE between the simulated curve of the percentage mass loss with respect to temperature or time and the experimental curve of the percentage mass loss with respect to temperature or time are given by the following equation: [Math 2] This involves calculating the root mean square error RMSE for each, and in this formula, i is the sequence number in the library of dynamic mechanism functions, where 1 ≤ i ≤ I, and I is the total number of dynamic mechanism functions. t n This is the nth time in the thermogravimetric experiment process, n is the time sequence in the thermogravimetric experiment, where 1 ≤ n ≤ N. N is the total number of time points in the thermogravimetric experiment process. m exp (t n ) is the weight of the pyrolyzed material sample at time t in the thermogravimetric experiment process n at m E,A (t n ) is the optimized activation energy E and frequency factor A, at time t during the thermogravimetric analysis simulation process. n A method for obtaining the thermal decomposition rate parameter of a solid material according to claim 3, wherein the weight of the thermal decomposition material sample is the weight of the sample.

5. The step of sorting multiple root mean square errors (RMSE) and using the dynamic mechanism function corresponding to the smallest root mean square error (RMSE) as the thermal decomposition rate parameter for the solid material is: The first dynamic mechanism function sorted in ascending order based on the root mean square error RMSE is defined as the most probable mechanism function. A method for obtaining a thermal decomposition rate parameter for a solid material according to claim 1, comprising the step of using the most probable mechanism function as the thermal decomposition rate parameter for the solid material.

6. The method for obtaining the thermal decomposition rate parameter of the solid material is: A method for obtaining the thermal decomposition rate parameters of a solid material according to claim 1, further comprising the step of using the activation energy E corresponding to the most probable mechanism function, the frequency factor A corresponding to the most probable mechanism function, and the least root mean square error RMSE as thermal decomposition rate parameters of the solid material.

7. A system for acquiring parameters of the thermal decomposition rate of a solid material, comprising a computer device, the computer device is A storage unit configured to store experimental curves of the percentage of mass loss with respect to temperature or time changes in collected pyrolysis material samples, A scanning simulation unit is configured to construct a library of dynamic mechanism functions and, by scanning each dynamic mechanism function, obtain a simulation curve of the percentage of mass loss with respect to temperature or time corresponding to each dynamic mechanism function. A computing unit configured to calculate the root mean square error RMSE between a simulation curve of the percentage mass loss with respect to temperature or time, and an experimental curve of the percentage mass loss with respect to temperature or time, A system for obtaining the pyrolysis rate parameter of a solid material, comprising a sorting unit configured to sort multiple root mean square errors (RMSE) and to use the dynamic mechanism function corresponding to the smallest root mean square error (RMSE) as the pyrolysis rate parameter of the solid material.

8. The memory unit stores the experimental curve, In a non-isothermal thermal decomposition mode, mass loss data from thermal decomposition material samples corresponding to multiple thermal decomposition heating rates are collected as samples. A system for acquiring thermal decomposition rate parameters of a solid material according to claim 7, comprising taking the thermal decomposition time and temperature value as inputs and the mass loss percentage as output for each thermal decomposition heating rate, to obtain and store an experimental curve of the mass loss percentage with respect to changes in temperature or time.

9. The scanning simulation unit constructs a library of dynamic mechanism functions and, by scanning each dynamic mechanism function, obtains a simulation curve of the percentage of mass loss with respect to temperature or time corresponding to each dynamic mechanism function. By scanning each dynamic mechanism function, a simulation curve of the percentage of mass loss with respect to temperature or time corresponding to each dynamic mechanism function is obtained, i.e., To set the initial activation energy E and the initial frequency factor A, Each dynamic mechanism function is scanned sequentially, and simultaneously, the initial activation energy E and initial frequency factor A are optimized based on the quasi-Newton method. The goal of optimization is to minimize the function RMSE(E,A), and the optimization equation is: [Math 3] The method involves obtaining the optimized activation energy E and frequency factor A corresponding to each dynamic mechanism function, and in this equation, E a+1 This represents the activation energy value at the (a+1)th iteration in the optimization process, and depends on the activation energy value and gradient information at the a-th iteration. E a This represents the activation energy value at the a-th iteration in the optimization process, where a=1 is the initial activation energy value. A a+1 This represents the frequency factor at the (a+1)th iteration in the optimization process, and depends on the frequency factor and gradient information at the a-th iteration. A a This represents the frequency factor at the a-th iteration in the optimization process, where the initial frequency factor is taken when a=1. RMSE(E a ,A a ) is the root mean square error between the simulation curve and the experimental curve for the percentage of mass loss with respect to temperature or time, when the activation energy is Ea and the frequency factor is Aa, and A system for obtaining thermal decomposition rate parameters of a solid material according to claim 7, wherein each of the dynamic mechanism functions is a thermogravimetric analysis simulation model, the optimized activation energy E and frequency factor A are used as parameters for each thermogravimetric analysis simulation model to perform a simulation, and a simulation curve of the percentage of mass loss with respect to temperature or time corresponding to each dynamic mechanism function is output, wherein a is the number of iterations in the optimization process and a ≥ 1.

10. The calculation unit calculates the root mean square error RMSE between the simulated curve of the mass loss percentage with respect to temperature or time and the experimental curve of the mass loss percentage with respect to temperature or time, respectively. formula, [Math 4] This involves calculating the root mean square error RMSE between the simulated curve of the mass loss percentage with respect to temperature or time, and the experimental curve of the mass loss percentage with respect to temperature or time, and in this formula, i is the sequence number in the library of dynamic mechanism functions, where 1 ≤ i ≤ I, and I is the total number of dynamic mechanism functions. t n This is the nth time in the thermogravimetric experiment process, n is the time sequence in the thermogravimetric experiment, where 1 ≤ n ≤ N. N is the total number of time points in the thermogravimetric experiment process. m exp (t n ) is time t in the thermogravimetric experiment process. n This is the weight of the pyrolysis material sample. m E,A (t n ) is the optimized activation energy E and frequency factor A, at time t during the thermogravimetric analysis simulation process. n A system for obtaining the thermal decomposition rate parameter of a solid material according to claim 7, wherein the weight of the thermal decomposition material sample is the weight of the sample.

11. The sorting unit sorts multiple root mean square errors (RMSE) and uses the dynamic mechanism function corresponding to the smallest root mean square error (RMSE) as the thermal decomposition rate parameter of the solid material. The most probable mechanism function is defined as the first dynamic mechanism function obtained by sorting all dynamic mechanism functions in ascending order based on the root mean square error (RMSE), and, A system for obtaining a thermal decomposition rate parameter of a solid material according to claim 7, comprising using the most probable mechanism function as the thermal decomposition rate parameter of the solid material.

12. The system for acquiring thermal decomposition rate parameters of a solid material according to claim 7, wherein the memory unit is further configured to store a library of dynamic mechanism functions, simulation curves of mass loss percentages with respect to temperature or time, root mean square error RMSE, and sorted results of a plurality of root mean square error RMSEs.

13. A computer-readable storage medium that stores a computer program, and when the computer program is executed by a processor, performs the method for obtaining the thermal decomposition kinetic parameters of a solid material according to any one of claims 1 to 6.

14. A computer system comprising memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to perform the method for obtaining the thermal decomposition kinetic parameters of a solid material according to any one of claims 1 to 6.

15. A computer program, when executed by a processor, that performs the method for obtaining the thermal decomposition kinetic parameters of a solid material according to any one of claims 1 to 6.

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