Method and system for acquiring thermal decomposition speed parameter for solid material, 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 is based on and claims priority from Chinese Patent Application No. 202410203886.3, filed on February 23, 2024, the entire contents of which are incorporated herein by reference.

[0002] The present invention relates to the technical field of thermal decomposition rate parameter analysis, and more particularly to a method and system for acquiring thermal decomposition rate parameters of solid materials, and a storage medium. [Background technology]

[0003] Thermal decomposition kinetics is the study of physical changes or chemical reaction dynamics using thermal analysis and calorimetry techniques. In pyrolysis kinetics studies, the most probable mechanism function is one of the important parameters for the thermal decomposition rate of solid materials.

[0004] In the related art, the most probable mechanism function is often obtained by subjective or approximate inference, so the obtained most probable mechanism function is often inaccurate and requires repeated verification inference, which seriously affects the efficiency of obtaining the thermal decomposition kinetic parameters of solid materials. Summary of the Invention

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

[0006] In order to solve the above technical problems, the present invention provides a method for obtaining thermal decomposition rate parameters of a solid material, the method comprising: collecting an experimental curve of percent mass loss with temperature or time for a pyrolysis material sample; building a library of dynamic mechanism functions and scanning through each dynamic mechanism function to obtain a simulation curve of percent mass loss with temperature or time corresponding to each dynamic mechanism function; Calculating the root mean square error (RMSE) between the simulated curve of percent mass loss over temperature or time and the experimental curve of percent mass loss over temperature or time, respectively; and sorting the plurality of root mean square errors RMSE and determining the dynamic mechanism function corresponding to the smallest root mean square error RMSE as the thermal decomposition rate parameter of the solid material. [Effects of the Invention]

[0007] The beneficial effect of the present invention is that in the method for obtaining thermal decomposition kinetic parameters of solid materials, each kinetic mechanism function is scanned in sequence according to its sequence number. During the scanning process, the activation energy E and frequency factor A of the kinetic parameters of each kinetic mechanism function are optimized using the Quasi-Newton method, thereby reducing the error between the simulated curve of mass loss percentage with temperature or time and the experimental curve of mass loss percentage with temperature or time, and minimizing the root mean square error (RMSE) values, which approach the theoretical minimum. This ensures the accuracy of sorting the kinetic mechanism functions, and at the same time, globality is ensured because all kinetic mechanism functions in the function library are scanned in sequence. This increases the accuracy of the obtained most probable mechanism function, thereby improving the efficiency of obtaining thermal decomposition kinetic parameters of solid materials.

[0008] Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and obtained by the structure particularly pointed out in the description and drawings.

[0009] In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0010] In order to more clearly describe the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly describe the drawings that need to be used to describe the specific embodiments or the prior art. The drawings described in the following description are some embodiments of the present invention, and it is obvious that those skilled in the art can obtain other drawings from these drawings without any creative efforts.

[0011] [Figure 1] FIG. 1 is a flow chart illustrating a method for obtaining thermal decomposition kinetic parameters of a solid material according to some embodiments. [Figure 2] FIG. 2 is a schematic diagram comparing the third most probable mechanism function with experimental data for a case according to some embodiments. [Figure 3] FIG. 3 is a schematic diagram comparing ten non-most probable mechanism functions with experimental data for cases according to some embodiments. [Figure 4] FIG. 4 is a schematic block diagram of a solid material pyrolysis kinetics parameter acquisition system, according to some embodiments. [Figure 5] FIG. 5 is a schematic block diagram of an electronic device according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0012] In order to clarify the objectives, technical aspects and advantages of the embodiments of the present invention, the technical aspects of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. However, it is clear that the described embodiments are only some of the embodiments of the present invention and do not represent all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without performing creative work fall within the scope of protection of the invention.

[0013] In mechanism-related technologies, the most probable mechanism function for the thermal decomposition rate parameters of solid materials is often obtained by subjective inference or approximate inference methods, and the obtained most probable mechanism function is often inaccurate and requires repeated verification inference, which seriously affects the efficiency of obtaining the thermal decomposition rate parameters of solid materials.

[0014] Accordingly, at least one embodiment provides a method for obtaining thermal decomposition rate parameters of a solid material, the method comprising: collecting an experimental curve of percent mass loss with temperature or time for a pyrolyzed material sample; building a library of dynamic mechanism functions and scanning through each dynamic mechanism function to obtain a simulation curve of percent mass loss with temperature or time corresponding to each dynamic mechanism function; Calculating the root mean square error (RMSE) between the simulated curve of percent mass loss over temperature or time and the experimental curve of percent mass loss over temperature or time, respectively; Then, the method includes a step of sorting the plurality of root mean square errors RMSE, and determining the kinetic mechanism function corresponding to the smallest root mean square error RMSE as the thermal decomposition rate parameter of the solid material.

[0015] Various non-limiting 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 obtaining thermal decomposition rate parameters of a solid material, which includes the following steps S101 to S104.

[0017] In step S101, an experimental curve of percent mass loss with temperature or time for a pyrolyzed material sample is collected.

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

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

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

[0021] Specifically, at least one embodiment includes, but is not limited to, a library of 41 common kinetic mechanism functions, as shown in Table 1 below, where α is the conversion rate, which refers to the percentage or fraction of conversion of a given reactant, and G(α) and f(α) are the mechanism functions in integral and differential form, 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 percent mass loss with temperature or time corresponding to each dynamic mechanism function by scanning each dynamic mechanism function comprises: setting an initial activation energy E and an initial frequency factor A; Each dynamic mechanism function is scanned in turn, and the initial activation energy E and the initial frequency factor A are simultaneously optimized based on the quasi-Newton method, and the optimization formula is:

number

[0024] Specifically, the initial activation energy E and the frequency factor A are both artificially given initial values. For example, but not limited to, the initial value of E is 70000, and the initial value of A is e^10, and these values ​​may be ±10%.

[0025] In the method for obtaining the thermal decomposition rate parameters of a solid material in this embodiment, each kinetic mechanism function is scanned in turn according to its sequence number. During the scanning process, the activation energy E and frequency factor A of the kinetic parameters are optimized based on the quasi-Newton method. Then, each kinetic mechanism function is used as a simulation model. The optimized activation energy E and frequency factor A are used as the parameters of the simulation model to obtain a simulation curve of the percentage mass loss with temperature or time. Next, the root mean square error (RMSE) between the experimental curve of the percentage mass loss with temperature or time and the simulation curve of the percentage mass loss with temperature or time is used as the target function. 41 root mean square error (RMSE) values ​​are obtained. These root mean square error (RMSE) values ​​are sorted, and the kinetic mechanism function corresponding to the smallest root mean square error (RMSE) value is used as the most probable mechanism function. The activation energy E and frequency factor A of the simulation model of each kinetic mechanism function are both optimized to reduce the error between the simulated curve of mass loss percentage with temperature or time and the experimental curve of mass loss percentage with temperature or time, and the root mean square error RMSE values ​​are all optimized to minimize and approach the theoretical minimum value, thereby ensuring the accuracy of sorting the kinetic mechanism functions. At the same time, all kinetic mechanism functions in the function library are traversed in order to ensure the globality of the most probable mechanism function. This increases the accuracy of the obtained most probable mechanism function and improves the efficiency of obtaining the thermal decomposition kinetic parameters of solid materials.

[0026] The final output is the most probable mechanism function, the activation energy E as the dynamic parameter of the most probable mechanism function, the frequency factor A as the 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 temperature or time changes, which are the thermal decomposition rate parameters of the solid material. More accurate dynamic mechanism functions and dynamic parameters will help to build more accurate application models for industrial processes such as pyrolysis, gasification, and combustion, and will further promote the development of related reactors by more accurately understanding the internal operating conditions of the reactors using computational fluid dynamics (CFD).

[0027] In some embodiments, obtaining an experimental curve of percent mass loss with temperature or time for a pyrolyzed material sample comprises: In a non-isothermal pyrolysis mode, collecting mass loss data of a pyrolyzed material sample corresponding to a plurality of pyrolysis heating rates as samples; For each pyrolysis heating rate, pyrolysis time and temperature values ​​are input and percent mass loss is output to obtain an experimental curve of percent mass loss with temperature or time.

[0028] In some embodiments, the step of calculating the root mean square error RMSE between the simulated curve of percent mass loss over temperature or time and the experimental curve of percent mass loss over temperature or time, respectively, is performed using the formula:

number

[0029] In some embodiments, the step of sorting the plurality of root mean square errors RMSE and using the kinetic mechanism function corresponding to the smallest root mean square error RMSE as the kinetic parameter of the thermal decomposition of the solid material comprises: a step of sorting all mechanism functions in ascending order based on the root mean square error (RMSE), and determining the first mechanism function as the most probable mechanism function; and determining the most probable mechanism function as the thermal decomposition rate parameter of the solid material.

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

[0031] Assuming that the mechanism function No. 3 obtained by the method for obtaining thermal decomposition rate parameters of solid materials in this example is the most probable mechanism function, a comparison of it with the experimental data is shown in Figure 2, and the root mean square error (RMSE) is clearly smaller. A comparison of the mechanism function No. 10, which is not the most probable mechanism function, with the experimental data is shown in Figure 3, and the root mean square error (RMSE) is clearly larger.

[0032] As shown in Figure 4, some embodiments further provide a system for obtaining thermal decomposition rate parameters of a solid material, the system including a computer device, the computer device comprising: a storage unit configured to store an experimental curve of percent mass loss with temperature or time for the collected pyrolysis material sample; a scanning simulation unit configured to build a library of dynamic mechanism functions and to obtain a simulation curve of mass loss percentage with temperature or time corresponding to each dynamic mechanism function by scanning each dynamic mechanism function; a calculation unit configured to calculate a root mean square error (RMSE) between a simulated curve of percent mass loss over temperature or time and an experimental curve of percent mass loss over temperature or time, respectively; and a sorting unit configured to sort the plurality of root mean square errors RMSE and determine the kinetic mechanism function corresponding to the smallest root mean square error RMSE as the thermal decomposition kinetic parameter of the solid material.

[0033] The specific functions of the storage unit, the scan simulation unit, the calculation unit and the sorting unit are realized in a computer device.

[0034] In some embodiments, the memory unit stores experimental curves, specifically, in a non-isothermal pyrolysis mode, mass loss data of pyrolysis material samples corresponding to multiple pyrolysis heating rates are collected as samples, and at each pyrolysis heating rate, pyrolysis time and temperature values ​​are used as inputs, and mass loss percentages are used as outputs, to obtain and store experimental curves of mass loss percentages with changes in temperature or time.

[0035] In some embodiments, the storage unit is further configured to store a library of dynamic mechanism functions, a simulation curve of percent mass loss with changes in temperature or time, a root mean square error RMSE, and a sorted result of the plurality of root mean square errors RMSE.

[0036] In some embodiments, a storage unit may be considered a data storage space.

[0037] In some embodiments, the scanning simulation unit constructs a library of dynamic mechanism functions, and obtains a simulation curve of mass loss percentage with temperature or time change corresponding to each dynamic mechanism function by scanning each dynamic mechanism function, i.e., sets an initial activation energy E and an initial frequency factor A, sequentially scans each dynamic mechanism function, and simultaneously optimizes the initial activation energy E and the initial frequency factor A based on a quasi-Newton method while scanning, with the goal of the optimization being to minimize the function RMSE(E,A), and the optimization formula is:

number

[0038] In some embodiments, the calculating unit calculating the root mean square error RMSE between the simulated curve of percent mass loss over temperature or time and the experimental curve of percent mass loss over temperature or time, respectively, comprises: formula,

number

[0039] In some embodiments, the sorting unit sorts the plurality of root mean square errors RMSE, and the kinetic mechanism function corresponding to the smallest root mean square error RMSE is taken as the kinetic parameter of the thermal decomposition of the solid material, sorting all the dynamic mechanism functions in ascending order based on the root mean square error (RMSE), the first dynamic mechanism function being the most probable mechanism function; and It involves taking the most probable mechanism function as the thermal decomposition rate parameter of the solid material.

[0040] Hereinafter, electronic devices according to embodiments of the present disclosure will be described from the viewpoint of hardware processing.

[0041] Some embodiments also provide a computer-readable storage medium having stored thereon a computer program / instructions that, when executed by a processor, implements any of the above possible methods for obtaining thermal decomposition rate parameters of a solid material.

[0042] Some embodiments further provide a computer apparatus / device / system including a memory, a processor, and a computer program stored in the memory, as shown in Figure 5. The processor executes the computer program to implement any of the above-mentioned possible methods for obtaining kinetic parameters of the thermal decomposition of a solid material.

[0043] In other embodiments, computer equipment, industrial computers, etc. may also be types of electronic devices.

[0044] The structure shown in FIG. 5 is not limiting of the electronic device, which may include fewer or more components than shown, or may combine certain components, or may include a different arrangement of components.

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

[0046] The storage module, readable storage medium, or computer-readable storage medium includes at least one type of memory. Memory may include flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, it may be an internal storage unit of a computer device, such as a hard disk of the computer device. In other embodiments, the memory may be an external storage device of a computer device, such as a plug-in hard disk installed in the computer device, a Smart Media Card (SMC), a Secure Digital Card (SD), a flash card, etc. Furthermore, the memory may include both an internal storage unit of a computer device and an external storage 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] The processor, in some embodiments, may be a central processing unit (CPU), controller, single-chip computer, microprocessor, or other data processing chip, and is used to execute process code stored in memory or process data, e.g., to execute a computer program.

[0048] In some embodiments, the communication bus may be an input / output bus, which may be a Peripheral Component Interconnect (PCI) bus or an Enhanced Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc.

[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 or 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, an OLED (Organic Light-Emitting Diode) touch device, etc. In this case, the display or display module is also called a display screen or a display unit, since it displays information processed in the computer device and a visualized user interface.

[0050] Some embodiments provide a computer program product comprising computer programs / commands that, when executed by a processor, perform the above-described method for obtaining thermal decomposition kinetic parameters of a solid material.

[0051] In some embodiments of the present invention, it should be understood that the disclosed apparatus and method may also be implemented in other manners. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the drawings illustrate possible architectures, functions, and operations of apparatuses, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, a program segment, or a portion of code. The module, program segment, or portion of code includes executable commands for implementing one or more predetermined logical functions. Note that in some alternative implementations, the functions depicted in the blocks may occur in a different order than depicted in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented in a dedicated hardware-based system that performs the predetermined functions or operations, or in a combination of dedicated hardware and computer instructions.

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

[0053] The above functions can be realized in the form of software functional modules and stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention essentially or the part that contributes to the prior art or the part of the technical solution can be expressed in the form of a software product. The computer software product is stored in a storage medium and includes multiple commands to make a computer device (which may be a personal computer, a server, a network device, etc.) perform all or part of the steps of the methods described in each embodiment of the present invention.

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

Claims

1. A method for obtaining thermal decomposition rate parameters of a solid material, comprising: collecting an experimental curve of percent mass loss with temperature or time for a pyrolyzed material sample; building a library of dynamic mechanism functions and scanning through each dynamic mechanism function to obtain a simulation curve of percent mass loss with temperature or time corresponding to each dynamic mechanism function; Calculating the root mean square error (RMSE) between the simulated curve of percent mass loss over temperature or time and the experimental curve of percent mass loss over temperature or time, respectively; a step of sorting a plurality of root mean square errors (RMSE) and determining the dynamic mechanism function corresponding to the smallest root mean square error (RMSE) as the thermal decomposition rate parameter of the solid material.

2. Obtaining an experimental curve of percent mass loss with temperature or time for a pyrolyzed material sample includes: In a non-isothermal pyrolysis mode, collecting mass loss data of a pyrolyzed material sample corresponding to a plurality of pyrolysis heating rates as samples; and obtaining an experimental curve of percent mass loss with temperature or time for each pyrolysis heating rate, using pyrolysis time and temperature values ​​as inputs and percent mass loss as output.

3. The step of obtaining a simulation curve of percent mass loss with change in temperature or time corresponding to each dynamic mechanism function by scanning each dynamic mechanism function includes: setting an initial activation energy E and an initial frequency factor A; Each dynamic mechanism function is scanned in turn, and at the same time, the initial activation energy E and the initial frequency factor A are optimized based on the quasi-Newton method. The optimization goal is to minimize the function RMSE (E, A), and the optimization formula is: [Equation 1] and obtaining the optimized activation energy E and frequency factor A corresponding to each dynamic mechanism function, where: E a+1 represents the activation energy value at iteration (a+1) in the optimization process, and is dependent on the activation energy value at iteration (a) and the gradient information; E a represents the activation energy value at the ath iteration in the optimization process, and takes the initial activation energy value when a=1; A a+1 represents the frequency factor at the (a+1)th iteration of the optimization process, and depends on the frequency factor at the (a)th iteration and the gradient information, A a represents the frequency factor at the ath iteration in the optimization process, and takes the initial frequency factor when a=1; RMSE(E a ,A a ) is the root mean square error between the simulated and experimental curves of percent mass loss with temperature or time at activation energy Ea and frequency factor Aa; 2. The method for obtaining the thermal decomposition rate parameters of a solid material according to claim 1, comprising the steps of: using each of the dynamic mechanism functions as a thermogravimetric simulation model; performing a simulation using the optimized activation energy E and frequency factor A as parameters of each of the thermogravimetric simulation models; and outputting a simulation curve of the mass loss percentage with the change in temperature or time corresponding to each of the dynamic mechanism functions, 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 percent mass loss over temperature or time and the experimental curve of percent mass loss over temperature or time, respectively, are as follows: [Equation 2] This involves calculating the root mean square error (RMSE) at i is the sequence number of the dynamic mechanism function in the function library, 1≦i≦I, and I is the total number of dynamic mechanism functions; t n is the nth time in the thermogravimetric experiment, n is the time sequence in the thermogravimetric experiment, 1≦n≦N, N is the total number of time points in the thermogravimetric experiment. m exp (t n ) is the time t during the thermogravimetric experiment n is the weight of the pyrolyzed material sample at m E,A (t n ) is the time t in the thermogravimetric analysis simulation process with the optimized activation energy E and frequency factor A. n The method for obtaining the thermal decomposition rate parameter of a solid material according to claim 3, wherein the parameter is the weight of the thermally decomposed material sample at 1000 kJ / min.

5. The step of sorting the plurality of root mean square errors RMSE and determining the kinetic mechanism function corresponding to the smallest root mean square error RMSE as the thermal decomposition rate parameter of the solid material includes: a step of sorting all the dynamic mechanism functions in ascending order based on the root mean square error (RMSE), and determining the first dynamic mechanism function as the most probable mechanism function; and determining the most probable mechanism function as the thermal decomposition rate parameter of the solid material.

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

7. 1. A system for acquiring thermal decomposition rate parameters of a solid material, comprising: a computer device; a storage unit configured to store an experimental curve of percent mass loss with temperature or time for the collected pyrolysis material sample; a scanning simulation unit configured to build a library of dynamic mechanism functions and to obtain a simulation curve of mass loss percentage with temperature or time corresponding to each dynamic mechanism function by scanning each dynamic mechanism function; a calculation unit configured to calculate a root mean square error (RMSE) between a simulated curve of percent mass loss over temperature or time and an experimental curve of percent mass loss over temperature or time, respectively; and a sorting unit configured to sort the plurality of root mean square errors RMSE and define the dynamic mechanism function corresponding to the smallest root mean square error RMSE as the thermal decomposition kinetic parameter of the solid material.

8. The storage unit stores an experimental curve, In a non-isothermal pyrolysis mode, mass loss data of the pyrolyzed material sample corresponding to a plurality of pyrolysis heating rates is collected as samples; 8. The system for acquiring thermal decomposition rate parameters of a solid material according to claim 7, comprising obtaining and storing an experimental curve of percent mass loss with temperature or time change, using thermal decomposition time and temperature values ​​as inputs and percent mass loss as output, for each thermal decomposition heating rate.

9. The scanning simulation unit constructs a library of dynamic mechanism functions and scans each dynamic mechanism function to obtain a simulation curve of mass loss percentage with change in temperature or time corresponding to each dynamic mechanism function, By scanning each dynamic mechanism function, a simulation curve of mass loss percentage with temperature or time corresponding to each dynamic mechanism function is obtained, i.e., Setting the initial activation energy E and initial frequency factor A; Each dynamic mechanism function is scanned in turn, and at the same time, the initial activation energy E and the initial frequency factor A are optimized based on the quasi-Newton method. The optimization goal is to minimize the function RMSE (E, A), and the optimization formula is: [Equation 3] and obtaining the optimized activation energy E and frequency factor A corresponding to each dynamic mechanism function, where: E a+1 represents the activation energy value at iteration (a+1) in the optimization process, and is dependent on the activation energy value at iteration (a) and the gradient information; E a represents the activation energy value at the ath iteration in the optimization process, and takes the initial activation energy value when a=1; A a+1 represents the frequency factor at the (a+1)th iteration of the optimization process, and depends on the frequency factor at the (a)th iteration and the gradient information, A a represents the frequency factor at the ath iteration in the optimization process, and takes the initial frequency factor when a=1; RMSE(E a ,A a ) is the root mean square error between the simulated and experimental curves of percent mass loss with temperature or time at activation energy Ea and frequency factor Aa; and 8. The system for acquiring thermal decomposition rate parameters of a solid material according to claim 7, further comprising: performing a simulation using each dynamic mechanism function as a thermogravimetric analysis simulation model, and using the optimized activation energy E and frequency factor A as parameters of each thermogravimetric analysis simulation model; and outputting a simulation curve of mass loss percentage with changes in temperature or time corresponding to each dynamic mechanism function, wherein a is the number of iterations in the optimization process, and a is greater than or equal to 1.

10. The calculation unit calculating a root mean square error (RMSE) between a simulated curve of percent mass loss with a change in temperature or time and an experimental curve of percent mass loss with a change in temperature or time, respectively, comprises: formula, [Equation 4] and calculating the root mean square error RMSE between the simulated curve of percent mass loss over temperature or time and the experimental curve of percent mass loss over temperature or time, respectively, by: i is the sequence number of the dynamic mechanism function in the function library, 1≦i≦I, and I is the total number of dynamic mechanism functions; t n is the nth time in the thermogravimetric experiment, n is the time sequence in the thermogravimetric experiment, 1≦n≦N, N is the total number of time points in the thermogravimetric experiment. m exp (t n ) is the time t during the thermogravimetric experiment n is the weight of the pyrolyzed material sample at m E,A (t n ) is the time t in the thermogravimetric analysis simulation process with the optimized activation energy E and frequency factor A. n 8. The system for acquiring thermal decomposition rate parameters of a solid material according to claim 7, wherein the thermal decomposition rate parameter is the weight of the sample of the thermal decomposition material at 100 kJ / s.

11. The sorting unit sorts a plurality of root mean square errors RMSE, and a kinetic mechanism function corresponding to a minimum root mean square error RMSE is determined as a thermal decomposition rate parameter of the solid material. sorting all the dynamic mechanism functions in ascending order based on the root mean square error (RMSE), the first dynamic mechanism function being the most probable mechanism function; and The system for acquiring a thermal decomposition rate parameter of a solid material according to claim 7, further comprising: setting a most probable mechanism function as the thermal decomposition rate parameter of the solid material.

12. 8. The system for acquiring thermal decomposition kinetic parameters of a solid material according to claim 7, wherein the storage unit is further configured to store a library of dynamic mechanism functions, a simulation curve of mass loss percentage with changes in temperature or time, a root mean square error (RMSE), and a sorted result of a plurality of root mean square errors (RMSE).

13. A computer-readable storage medium having a computer program stored therein, the computer program performing the method for acquiring thermal decomposition kinetic parameters of a solid material according to any one of claims 1 to 6 when executed by a processor.

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

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

Images