基于遗传算法和最小二乘法的冶金焦非等温气化随机孔隙模型参数优化

IF 0.5 Q4 ENGINEERING, CHEMICAL

Coke and Chemistry Pub Date : 2025-07-04 DOI:10.3103/S1068364X25600307

Hanlu Song, Zhongsuo Liu

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引用次数: 0

摘要

用热重法对冶金焦非等温气化过程进行了动力学研究。采用随机孔隙模型（RPM）来描述气化动力学行为。采用单独使用遗传算法的方法和遗传算法与最小二乘法相结合的方法对转速参数进行优化。与前一种方法相比，后一种方法得到的参数更精确。结合遗传算法和最小二乘法得到的理论曲线与实验曲线吻合较好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Optimization of Random Pore Model Parameters of Metallurgical Coke Nonisothermal Gasification via Genetic Algorithm and Least Squares

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Optimization of Random Pore Model Parameters of Metallurgical Coke Nonisothermal Gasification via Genetic Algorithm and Least Squares

This paper kinetically studied the metallurgical coke nonisothermal gasification by thermogravimetry. The random pore model (RPM) was made use of to describe gasification kinetic behavior. The RPM parameters were optimized using two methods, viz. the method using genetic algorithm alone and the method combining genetic algorithm and least squares. By comparison with the former method, the latter yielded more accurate parameters. Theoretical curves obtained from the method combining genetic algorithm and least squares matched experimental ones well.

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来源期刊

Coke and Chemistry ENGINEERING, CHEMICAL-

CiteScore

0.70

自引率

50.00%

发文量

期刊介绍： The journal publishes scientific developments and applications in the field of coal beneficiation and preparation for coking, coking processes, design of coking ovens and equipment, by-product recovery, automation of technological processes, ecology and economics. It also presents indispensable information on the scientific events devoted to thermal rectification, use of smokeless coal as an energy source, and manufacture of different liquid and solid chemical products.