Optimizing LPG production under quality specifications by preprocessing MILP with Pareto dominance and DEA

作     者：Eirinakis, Pavlos Koronakos, Gregory Plitsos, Stathis 

作者机构：Univ Piraeus Dept Ind Management & Technol Piraeus 18534 Greece Univ Piraeus Dept Informat Piraeus Greece 

出 版 物：《INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH》 (Int J Prod Res)

年 卷 期：2024年第62卷第24期

页      面：8609-8632页

核心收录：

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0802[工学-机械工程] 

基　　金：H2020 FACTLOG project from the European Union's Horizon 2020 programme 

主　　题：LPG production industrial process operational conditions mathematical programming pareto optimality data envelopment analysis 

摘      要：Liquified Petroleum Gas (LPG) is an oil refinery product that must adhere to quality specifications with respect to certain impurities. Refineries apply an LPG purification process that consists of a flow network with several process units (PUs). Current methods focus on optimising the performance of each PU separately;there exists no known approach for identifying the whole process optimum. In this paper, we present an approach for optimising the LPG purification process as a whole. We utilise operational scenarios to model the non-linear transformations of each PU. These scenarios enable us to devise a Mixed Integer Linear Program (MILP) that minimises energy consumption. The obtained solution is an approximation of the optimum but offers actionable support to refinery engineers. To enable the applicability of our approach at large scale, we propose two filters based on Pareto dominance and Data Envelopment Analysis (DEA) to identify the Pareto optimal and the Best Practice set of scenarios, respectively. By filtering out the rest, we reduce the solution space of the corresponding MILP. Further, we provide computational evidence that the application of these filters vastly improves solution time and enables applicability under real production conditions.