341241 Effective Global Optimization Methods for Total Refinery Planning Operations

Thursday, November 7, 2013: 5:15 PM
Continental 8 (Hilton)
Jie Li1,2, Ruth Misener1, Xin Xiao2 and Christodoulos A. Floudas1, (1)Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, (2)Institute of Process Engineering, Beijing, China

In the last twenty years, the petroleum industry, which is the largest sources of energy products in the world, has succeeded by creating markets and supplying them with suitable products. Tighter competition, strict environmental regulations, and lower-margin profits, drive the petroleum refinery to apply new technologies to improve their planning operations.

The entire refinery operations can be divided into three sections including crude oil unloading and blending, production unit operations, and the product blending and distribution[1-2]. The refinery planning addresses the entire refinery operations involving crude purchase, processing amounts for production units, their production mode, flow connections between production units, and pooling and blending operations to satisfy quality requirements of production units, intermediates and final products. Mathematical modeling of production units, pooling and blending operations introduces bilinear, trilinear terms, and higher order terms, which transform the entire problem into non-convex nonlinear optimization problem.

The refinery planning problem has received considerable attention since the introduction of linear programming in 1950s.  They focused on developing different models and algorithms to solve large-scale industrial problems. Some commercial software such as RPMS (Refinery and Petrochemical Modeling System),[3] PIMS (Process Industry Modeling System),[4] and GRTMPS (Haverly Systems)[5] have been developed. However, inaccuracy caused by nonrigorous linear models and approximate algorithm may reduce the overall profitability or sacrifice product quality. Moreover, no global optimality is guaranteed. Pinto and Moro[6] developed a nonlinear planning model for production planning which allows the implementation of nonlinear process models as well as blending relations. Li et al.[7] presented a refinery planning model that utilizes simplified empirical nonlinear process models with considerations for crude characteristics, product yields, and qualities, etc. Alhajri et al.[8] developed a nonlinear model to address refinery planning problem. Alattas et al.[9] developed a fractionation index based nonlinear model for crude distillation unit and integrated it into the linear refinery planning model. They solved their NLP models with NLP solvers without guaranteeing global optimality. The review on refinery planning can be found in Shah et al.[10] It can be concluded that there is no existing global optimization method to address the total refinery planning problems.

In this presentation, we first present the entire nonlinear optimization model for the total refinery planning operations. The model involves several bilinear and trilinear terms arising from the intermediate pooling and product blending operations. Then, we propose an optimization-based procedure to obtain the tightest lower and upper bounds for variables especially the variables involving in bilinear and trilinear terms. Then, we incorporate those tightest lower and upper bounds into commercial solver GloMIQO[11-12] to obtain e-global optimality. A large-scale industrial case study is solved to illustrate the efficiency of our developed global optimization approach. The computational results show that our developed optimization-based procedure greatly improves the lower and upper bounds of all variables especially those variables existing in the bilinear and trilinear terms without much computational effort. Furthermore, we can obtain 8%-global optimal solution for this example, which is 2.5% to 3.8% improvement compared to those from PIMS.

References

[1] Pinto, J. M.; Joly, M.; Moro, L. F. L. Planning and Scheduling Models for Refinery Operations. Computers and Chemical Engineering 2000, 24, 2259-2276.

[2] Shah, N. K.; Ierapetritou, M. G. Short-Term Scheduling of a Large-Scale Oil-Refinery Operaitons: Incorporating Logistics Details. AIChE Journal 2011, 57, 1570-1584.

[3] RPMS (Refinery and Petrochemical Modeling Systems): A System Description; Bonner and Moore: Houston, TX, 1979.

[4] Aspen PIMS System Reference (v7.2), Aspen Technology Inc.; Burlington, MA, 2010.

[5] GRTMPS (Haverly Systems), http://www.haverly.com/main-products/13-products/9-grtmps.

[6] Pinto, J. M.; Moro, L. F. L. A Planning Model for Petroleum Refineries. Braz. J. Chem. Eng. 2000, 17, 575-585.

[7] Li, W. K.; Hui, C. W.; Li, A. X. Integrating CDU, FCC, and Product Blending Models into Refinery Planning. Computers and Chemical Engineering 2005, 29, 2010-2028.

[8] Alhajri, I.; Elkamel, A.; Albahri, T.; Douglas, P. L. A Nonlinear Programming Model for Refinery Planning and Optimization with Rigorous Process Models and Product Quality Specifications. International Journal of Oil, Gas, and Coal Technology 2008, 1, 283-307.

[9] Alattas, A. M.; Grossmann, I. E.; Palou-Rivera, I. Integration of Nonlinear Crude Distillation Unit Models in Refinery Planning Optimization. Industrial and Engineering Chemistry Research 2011, 50, 6860-6870.

[10] Shah, N. K.; Li, Zu; Ierapetritou, M. G. Petroleum Refining Operations: Key Issues, Advances, and Opportunities, Industrial and Engineering Chemistry Research, 2011, 50, 1161-1170.

[11] Misener, R.; Floudas, C. A. Global Optimization of Mixed-Integer Quadratically-Constrained Quadratic Programs (MIQCQP) through Piecewise-Linear and Edge-Concave Relaxations. Mathematical Programming  Series B. 2012, 136, 155-182.

[12] Misener, R.; Floudas, C. A. GloMIQO: Global Mixed-Integer Quadratic Optimizer. Journal of Global Optimization, 2012, In press. DOI: 10.1007/s10898-012-9874-7.


Extended Abstract: File Not Uploaded
See more of this Session: Planning and Scheduling II
See more of this Group/Topical: Computing and Systems Technology Division