文献类型：期刊文献

英文题名：Improved Integrated Optimization Method of Gasoline Blend Planning and Real-Time Blend Recipes

作者：He, Kaixun[1,2];Qian, Feng[1];Cheng, Hui[1];Du, Wenli[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2016

卷号：55

期号：16

起止页码：4632

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20162002384928);WOS:【SCI-EXPANDED(收录号:WOS:000375244900025)】；

基金：The authors thank the anonymous reviewers for their comments and suggestions, which greatly improved the contents of this article. This work was supported by the Major State Basic Research Development Program of China (2012CB720500), the National Natural Science Foundation of China (61333010, 61222303, and 61422303) and the Natural Science Foundation of Shanghai (16ZR1407300).

语种：英文

外文关键词：Nonlinear systems - Production control - Gasoline - Natural language processing systems

摘要：An innovative integrated optimization strategy for gasoline blend planning is proposed, and an improved method to achieve online optimization of real-time blend recipes is described. The proposed strategy can calculate a rough blend and delivery sequence of gasoline and then adapt to process changes by using a three-level discrete-time algorithm. Only one blender is considered in this study. A single-period nonlinear model (NLP) is solved at the top level of the algorithm to check the feasibility of a long-term production plan. A multi-period mixed-integer nonlinear model is formulated and solved at the middle level of the algorithm to compute a short-term blend plan. Finally, a single-period NLP is solved circularly at the lowest level of the algorithm to optimize blend recipes to consider the changes in the quality of blend components. The initial plan is modified if the top-level model is not feasible. The middle-level model is resolved if an unexpected event occurs during blending. The proposed approach is advantageous because the initial planning and blending recipes can be modified online, remarkably minimizing quality giveaway and increasing the blending success rate. The performance of the proposed strategy is illustrated through its industrial application in real-world gasoline blending.