Global Optimization of Nonlinear Blend-Scheduling Problems  

作　　者：Pedro A.Castillo Castillo Pedro M.Castro Vladimir Mahalec 

机构地区：[1]Department of Chemical Engineering,McMaster University,Hamilton,ON L8S 4L8,Canada [2]Center for Mathematics,Fundamental Applications and Operations Research,Faculty of Sciences,University of Lisbon,Lisbon 1749-016,Portugal

出　　处：《Engineering》2017年第2期188-201,共14页工程（英文）

基　　金：Support by Ontario Research Foundation;Mc Master Advanced Control Consortium;Fundacao para a Ciência e Tecnologia(Investigador FCT 2013 program and project UID/MAT/04561/2013)

摘　　要：The scheduling of gasoline-blending operations is an important problem in the oil refining industry. Thisproblem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but alsonon-convex nonlinear behavior, due to the blending of various materials with different quality properties.In this work, a global optimization algorithm is proposed to solve a previously published continuous-timemixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimi-zation, the distribution problem, and several important operational features and constraints. The algorithmemploys piecewise McCormick relaxation （PMCR） and normalized multiparametric disaggregation tech-nique （NMDT） to compute estimates of the global optimum. These techniques partition the domain of oneof the variables in a bilinear term and generate convex relaxations for each partition. By increasing the num-ber of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates ofthe global solution. The algorithm is compared to two commercial global solvers and two heuristic methodsby solving four examples from the literature. Results show that the proposed global optimization algorithmperforms on par with commercial solvers but is not as fast as heuristic approaches.The scheduling of gasoline-blending operations is an important problem in the oil refining industry. This problem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but also non-convex nonlinear behavior, due to the blending of various materials with different quality properties. In this work, a global optimization algorithm is proposed to solve a previously published continuous-time mixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimization, the distribution problem, and several important operational features and constraints. The algorithm employs piecewise Mc Cormick relaxation(PMCR) and normalized multiparametric disaggregation technique(NMDT) to compute estimates of the global optimum. These techniques partition the domain of one of the variables in a bilinear term and generate convex relaxations for each partition. By increasing the number of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates of the global solution. The algorithm is compared to two commercial global solvers and two heuristic methods by solving four examples from the literature. Results show that the proposed global optimization algorithm performs on par with commercial solvers but is not as fast as heuristic approaches.

关 键 词：Global optimization Nonlinear gasoline blending Continuous-time scheduling model Piecewise linear relaxations 

分 类 号：N[自然科学总论]