详细信息

Kinetic modeling and multi-objective optimization of an industrial hydrocracking process with an improved SPEA2-PE algorithm  ( SCI-EXPANDED收录 EI收录)  

文献类型:期刊文献

英文题名:Kinetic modeling and multi-objective optimization of an industrial hydrocracking process with an improved SPEA2-PE algorithm

作者:Fan, Chen[1];Wang, Xindong[1];Li, Gaochao[1];Long, Jian[1]

机构:[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份:2025

卷号:80

起止页码:130

外文期刊名:CHINESE JOURNAL OF CHEMICAL ENGINEERING

收录:;EI(收录号:20251418180118);WOS:【SCI-EXPANDED(收录号:WOS:001464537600001)】;

基金:This work was supported by National Key Research and Development Program of China (2023YFB330780 0) , National Natural Science Foundation of China (Key Program: 62136003, 62373155) , Major Science and Technology Project of Xinjiang (No. 2022A01006-4) , and the Fundamental Research Funds for the Central Universities.

语种:英文

外文关键词:Hydrocracking; Multi-objective optimization; Improved SPEA2; Kinetic modeling

摘要:Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases, naphtha, diesel, and other products through cracking reactions. Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield, or to increase product yield while reducing energy consumption. This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm, which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm. The reactor model used in this article is simulated based on a twenty-five lumped kinetic model. Through model and test function verification, the proposed optimization scheme exhibits significant advantages in the multi-objective optimization process of hydrocracking. (c) 2025 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press Co., Ltd. (CIP). Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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