Data‐driven mathematical modeling and global optimization framework for entire petrochemical planning operations. Issue 9 (27th March 2016)

• Record Type: Journal Article
• Authors: Li, Jie; Xiao, Xin; Boukouvala, Fani; Floudas, Christodoulos A.; Zhao, Baoguo; Du, Guangming; Su, Xin; Liu, Hongwei
• Is Part Of: AIChE journal. Volume 62:Issue 9(2016)
• Page Start: 3020
• Page End: 3040
• Publication Date: 2016-03-27
• Subjects: refinery -- petrochemical -- planning -- Big‐Data -- global optimization; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.15220
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract : In this work we develop a novel modeling and global optimization‐based planning formulation, which predicts product yields and properties for all of the production units within a highly integrated refinery‐petrochemical complex. Distillation is modeled using swing‐cut theory, while data‐based nonlinear models are developed for other processing units. The parameters of the postulated models are globally optimized based on a large data set of daily production. Property indices in blending units are linearly additive and they are calculated on a weight or volume basis. Binary variables are introduced to denote unit and operation modes selection. The planning model is a large‐scale non‐convex mixed integer nonlinear optimization model, which is solved to ε‐global optimality. Computational results for multiple case studies indicate that we achieve a significant profit increase (37–65%) using the proposed data‐driven global optimization framework. Finally, a user‐friendly interface is presented which enables automated updating of demand, specification, and cost parameters. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3020–3040, 2016