文献类型：期刊文献

英文题名：Molecular management in continuous catalytic reforming operations by enhanced aromatics production through transformer-driven entropy maximization reconstruction

作者：Shi, Yi[1];Zhong, Weimin[1,2];Peng, Xin[1,4];He, Kaixun[3];Xue, Dong[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Meilong Rd 130, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Meilong Rd 130, Shanghai 200237, Peoples R China;[3]Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China;[4]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai, Peoples R China

年份：2025

卷号：309

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20250917968941);WOS:【SCI-EXPANDED(收录号:WOS:001436754500001)】；

基金：This work was supported by National Natural Science Fund for Distinguished Young Scholars (61925305) , National Natural Science Foundation of China (62173147, 62173145, 62303186) , Major Science and Technology Project of Xinjiang (No. 2022A01006-4) , the Program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Molecular management; Continuous catalytic reforming; Entropy maximization; Multi-objective optimization; Naphtha

摘要：Catalytic Reforming (CCR) units are pivotal in refining industries for hydrogen and aromatics production. Traditional CCR optimization, focusing on operational conditions and lumping kinetics, fails to capture molecular interactions and feedstock variability, leading to inefficiencies. This research introduces a CCR optimization framework leveraging molecular reconstruction adaptable to minor feedstock changes. We propose the Transformer-driven Entropy Maximization (TREM) method, integrating historical data to improve naphtha composition accuracy. A molecular-level CCR kinetic model is then developed, linked to the TREM method, within a multi-objective optimization (MOO) framework for enhanced production adaptability. Furthermore, we introduce the Online Synchronized Learning Multi-Objective Optimization (OSLMOO) method, applying online learning to a meta-heuristic algorithm, reducing computational complexity and enhancing adaptability to property and market fluctuations. These methods collectively enhance the efficiency and profitability of CCR operations by overcoming the limitations of traditional optimization approaches.