Solid Material Pyrolysis Kinetics Fitting by Mechanism Curve Matching
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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, thereby affecting efficiency.
Innovation Solution
A method involving collecting experimental curves, constructing a kinetics mechanism function library, traversing each function to generate simulation curves, calculating root mean square errors (RMSE), and sorting functions based on minimum RMSE to determine the most probable mechanism function, optimizing activation energy and pre-exponential factors using the quasi-Newton method to minimize errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
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 kinetics mechanism function library containing multiple candidate mechanism functions before the actual parameter determination process. This preliminary preparation allows the system to directly compare experimental data with pre-defined mechanisms, eliminating the need for repeated subjective inferences and verifications, thus improving both efficiency and accuracy simultaneously
Solution Approach 2:
The patent replaces the subjective mechanical inference process with an automated computational system that uses algorithms to objectively evaluate and select the most probable mechanism function based on quantitative comparison between experimental and simulated curves, eliminating human subjectivity and repeated verification cycles
2Measurement precision
If the kinetics mechanism function library is traversed sequentially with parameter optimization, then the accuracy of the most probable mechanism function is high, but the computational complexity increases
Solution Approach 1:
The patent segments the determination process into distinct phases: first traversing the pre-established kinetics mechanism function library to identify candidate mechanisms, then optimizing parameters (activation energy and pre-exponential factor) only for the selected mechanism. This segmentation avoids unnecessary computational effort by separating mechanism selection from parameter optimization, reducing overall computational complexity while maintaining high accuracy
3Manufacturing precision
If parameter optimization is performed for each kinetics mechanism function, then the error between simulation curve and experimental curve is minimized, but the time consumption increases
Solution Approach 1:
The patent performs preliminary preparation by pre-establishing the kinetics mechanism function library with defined mechanisms before the actual fitting process. This allows the system to quickly identify the most probable mechanism without exhaustive parameter optimization for all possible mechanisms, reducing time consumption while maintaining fitting accuracy through targeted optimization only on the selected mechanism
Solution Approach 2:
The patent employs the quasi-Newton method, which is an self-adaptive optimization algorithm that automatically adjusts the optimization path and step size based on the local curvature of the error function. This self-service capability allows the algorithm to converge to the optimal parameters faster without requiring extensive manual intervention or trial-and-error, reducing time consumption while achieving minimum error
Data Source
AI summary
A method and a system for obtaining a pyrolysis kinetics parameter of a solid material and a storage medium are provided. The method includes: collecting an experimental curve of a mass loss percentage of a pyrolysate sample along with change of temperature or time; constructing a kinetics mechanism function library and traversing each kinetics mechanism function to obtain a simulation curve of the mass loss percentage corresponding to each kinetics mechanism function along with change of temperature or time; respectively calculating a root mean square error (RMSE) of the simulation curve of each mass loss percentage along with change of temperature or time and the experimental curve of the mass loss percentage along with change of temperature or time; and, sorting each RMSE and taking the kinetics mechanism function corresponding to a minimum RMSE as the pyrolysis kinetics parameter of the solid material.