文献类型：期刊文献

英文题名：Optimization and Application of Gasoline Blending Multi-period Scheduling Considering Blending Effect

作者：Hua, Junjie[1]; Chen, Qianyang[2]; Liu, Wei[1]; He, Renchu[1]; Li, Qiuxue[2]; Zhu, Zipeng[2]

机构：[1] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, China; [2] East China University of Science and Technology, Shanghai, China

年份：2023

起止页码：66

外文期刊名：Proceedings - 2023 2nd International Conference on Advanced Sensing, Intelligent Manufacturing, ASIM 2023

收录：EI(收录号：20242116109269)

语种：英文

外文关键词：Blending - Integer programming - Refining

摘要：Gasoline, as the most crucial product for refining enterprises, influences the economic performance of the refinery directly. The current national standards for gasoline are becoming increasingly stringent, with a continuous elevation in various quality indicators. Influenced by nonlinear factors such as octane number blending effects, gasoline blending faces challenges of non-compliance, excess quality, and high blending costs. This paper, by analyzing the principles of molecular interactions, provides a theoretical basis for constructing a model for the octane number blending effects in gasoline. Leveraging the blending effects model, the paper establishes a mixed-integer nonlinear model for the optimization of gasoline blending scheduling. The objectives include octane number attribute constraints, maximizing blending benefits, and maximizing production, while considering information about product storage tanks in the model to provide more detailed scheduling plans. We have applied the proposed model in the actual gasoline blending production process at a refinery, offering feasible scheduling solutions for gasoline blending and enhancing the refinery's economic returns. ? 2023 IEEE.