文献类型：期刊文献

英文题名：Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

作者：Long, Jian[1,2];Deng, Kai[1];He, Renchu[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China

年份：2023

卷号：61

起止页码：43

外文期刊名：CHINESE JOURNAL OF CHEMICAL ENGINEERING

收录：;EI(收录号：20233614665508);WOS:【SCI-EXPANDED(收录号:WOS:001067011600001)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101), Shanghai Committee of Science and Technology (22DZ1101500), the National Natural Science Foundation of China (61973124, 62073142) and Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Blend; Optimization algorithm; Neural networks; Particle swarm optimization; Mixed integer programming

摘要：Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries. However, the optimization model is complex and difficult to build, which is a typical mixed integer nonlinear programming (MINLP) problem. Considering the large scale of the MINLP model, in order to improve the efficiency of the solution, the mixed integer linear programming -nonlinear pro-gramming (MILP-NLP) strategy is used to solve the problem. This paper uses the linear blending rules plus the blending effect correction to build the gasoline blending model, and a relaxed MILP model is con-structed on this basis. The particle swarm optimization algorithm with niche technology (NPSO) is pro-posed to optimize the solution, and the high-precision soft-sensor method is used to calculate the deviation of gasoline attributes, the blending effect is dynamically corrected to ensure the accuracy of the blending effect and optimization results, thus forming a prediction-verification-reprediction closed-loop scheduling optimization strategy suitable for engineering applications. The optimization result of the MILP model provides a good initial point. By fixing the integer variables to the MILP-optimal value, the approximate MINLP optimal solution can be obtained through a NLP solution. The above solution strategy has been successfully applied to the actual gasoline production case of a refinery (3.5 million tons per year), and the results show that the strategy is effective and feasible. The optimiza-tion results based on the closed-loop scheduling optimization strategy have higher reliability. Compared with the standard particle swarm optimization algorithm, NPSO algorithm improves the optimization ability and efficiency to a certain extent, effectively reduces the blending cost while ensuring the conver-gence speed.& COPY; 2023 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved.