文献类型：期刊文献

英文题名：Multiobjective Economic-Environmental-Selectivity Optimization of the Dry Gas Based Ethylbenzene Production Process

作者：Han, Xiao[1];Zhao, Liang[1];Ye, Zhencheng[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：2021

卷号：60

期号：43

起止页码：15679

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20214611141566);WOS:【SCI-EXPANDED(收录号:WOS:000715847300029)】；

基金：This work was supported by the National Natural Science Foundation of China (Basic Science Center Program: 61988101), the National Natural Science Foundation of China (61873092, 62073142), and the National Natural Science Fund for Distinguished Young Scholars (61725301).

语种：英文

外文关键词：Greenhouse effect - Multiobjective optimization - Computer software - Environmental impact - Decision making - Gas emissions - Environmental regulations - Genetic algorithms - Sensitivity analysis - Economic and social effects

摘要：The ethylbenzene production process from dry gas has been an energy-intensive process that will inevitably bring about massive carbon emissions. With increasingly stringent regulations to mitigate the effect of greenhouse gas emissions, decision-makers need to make a trade-off among the objectives of environmental impact, economic benefit, and process indicators. An integrated framework combining simulation and multiobjective optimization is proposed to address this issue. The model of the dry gas based ethylbenzene production process is developed by Aspen Plus. The multiobjective optimization (MOO) model, including the profit before tax (PBT), greenhouse gas emissions (GHGs), and alkylation reaction selectivity (S-AR), is then derived considering the operational flexibility and mass and energy balance constraints. The Nondominated Sorting Genetic Algorithm II (NSGA-II) is used to solve this problem, and the three-dimensional Pareto front is obtained. The TOPSIS method is employed to select a specific solution from the Pareto front. Then, the sensitivity analysis is performed to assess the effects of the decision variables on the objectives. Compared with the original operating conditions, a substantial reduction in GHGs (23%) and a slight increase in PBT (4.1%) are achieved at the expense of specific S-AR (5.3%).