Enterprise-wide optimization of integrated planning and scheduling for refinery-petrochemical complex with heuristic algorithm  

作　　者：Lifeng Zhang Haoyang Hu Zhiquan Wang Zhihong Yuan Bingzhen Chen 

机构地区：[1]State Key Laboratory of Chemical Engineering,Department of Chemical Engineering,Tsinghua University,Beijing 100084,China [2]Department of Chemical Engineering,Tsinghua University,Beijing 100084,China

出　　处：《Frontiers of Chemical Science and Engineering》2023年第10期1516-1532,共17页化学科学与工程前沿（英文版）

基　　金：The authors gratefully acknowledge the financial support from the National Key Research and Development Program of China(Grant No.2018AAA0101602).

摘　　要：This paper focuses on the integrated problem of long-term planning and short-term scheduling in a largescale refinery-petrochemical complex,and considers the overall manufacturing process from the upstream refinery to the downstream petrochemical site.Different time scales are incorporated from the planning and scheduling subproblems.At the end of each discrete time period,additional constraints are imposed to ensure material balance between different time scales.Discrete time representation is applied to the planning subproblem,while continuous time is applied to the scheduling of ethylene cracking and polymerization processes in the petrochemical site.An enterprise-wide mathematical model is formulated through mixed integer nonlinear programming.To solve the problem efficiently,a heuristic algorithm combined with a convolutional neural network(CNN),is proposed.Binary variables are used as the CNN input,leading to the integration of a data-driven approach and classical optimization by which a heuristic algorithm is established.The results do not only illustrate the detailed operations in a refinery and petrochemical complex under planning and scheduling,but also confirm the high efficiency of the proposed algorithm for solving large-scale problems.

关 键 词：PLANNING SCHEDULING refinery-petrochemical convolutional neural network heuristic algorithm 

分 类 号：O17[理学—数学]