Global Optimization Algorithm for Large-Scale Refinery Planning Models with Bilinear Terms

详细信息    查看全文

• 作者：Pedro Castillo Castillo ; Pedro M. CastroVladimir Mahalec
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2017
• 出版时间：January 18, 2017
• 年：2017
• 卷：56
• 期：2
• 页码：530-548
• 全文大小：847K
• ISSN：1520-5045

文摘

We propose a global optimization algorithm for mixed-integer nonlinear programming (MINLP) problems arising from oil refinery planning. It relies on tight mixed-integer linear programming (MILP) relaxations that discretize the bilinear terms dynamically using either piecewise McCormick (PMCR) or normalized multiparametric disaggregation (NMDT). Tight relaxations help finding a feasible solution of the original problem via a local nonlinear solver, with the novelty being the generation of multiple starting points from CPLEX’s solution pool and the parallel execution. We show that optimality-based bound tightening (OBBT) is essential for large-scale problems, even though it is computationally expensive. To reduce execution times, OBBT is implemented in parallel. The results for a refinery case study, featuring units with alternative operating modes, intermediate storage tanks, and single- and multiple-period supply and demand scenarios, show that the algorithm’s performance is comparable to commercial solvers BARON and ANTIGONE.