Solid Material Pyrolysis Kinetics Using RMSE-Based Mechanism Selection
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Solution Overview
Problem
Existing methods for obtaining pyrolysis kinetics parameters of solid materials rely heavily on subjective or approximate inferences, leading to inaccurate results that require repeated verification, thus affecting efficiency.
Innovation Solution
A method involving collecting experimental curves, constructing a kinetic mechanism function library, traversing each function, calculating root mean square errors (RMSE), and optimizing activation energy and pre-exponential factors using a quasi-newton method to identify the kinetic mechanism function with the minimum RMSE, ensuring high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective inference or approximate inference is used to obtain the most probable mechanism function, then the obtaining process is simple, but the accuracy of the pyrolysis kinetics parameters is low and requires repeated verification
Solution Approach 1:
The patent pre-establishes a kinetic mechanism function library containing multiple candidate functions before the actual parameter determination. This preliminary preparation allows the system to directly compare and select from pre-defined functions during the determination process, avoiding repeated verification and improving both accuracy and efficiency.
Solution Approach 2:
The patent implements a feedback mechanism by calculating the root mean square error (RMSE) between experimental data and simulation curves for each kinetic mechanism function. The function with the minimum RMSE is selected as the most probable mechanism function, providing an objective feedback-based selection criterion that eliminates subjective inference and ensures high accuracy without requiring repeated verification.
2Measurement precision
If a comprehensive traversal of all kinetic mechanism functions is performed with parameter optimization, then the accuracy of the most probable mechanism function is high, but the computational complexity increases
Solution Approach 1:
The kinetic mechanism function library is pre-established with standardized functions and their corresponding parameters before the determination process. This preliminary organization reduces the computational complexity during traversal by providing a structured framework, while still enabling comprehensive comparison of all candidate functions to ensure high accuracy in selecting the most probable mechanism function.
Data Source
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AI summary
The present disclosure relates to the technological field of pyrolysis kinetics and in particular to a method and a system for obtaining a pyrolysis kinetics parameter of a solid material and a storage medium thereof. The method of obtaining a pyrolysis kinetics parameter of a solid material includes: collecting an experimental curve of a mass loss percentage of a pyrolysate sample along with changes in temperature or time; constructing a kinetic mechanism function library and traversing each kinetic mechanism function to obtain a simulation curve of the mass loss percentage corresponding to each kinetic mechanism function along with changes in temperature or time; respectively calculating a root mean square error (RMSE) between the simulation curve of the mass loss percentage corresponding to each kinetic mechanism function along with changes in temperature or time and the experimental curve of the mass loss percentage along with changes in temperature or time; and sorting each RMSE and taking the kinetic mechanism function corresponding to a minimum RMSE as the pyrolysis kinetics parameter of the solid material.