SUPPLY CHAIN OPTIMIZATION OF PETROLEUM REFINERY COMPLEXES

Production planning is a very important step in managing the operation of a single refinery and their complexes. Despite its significance, few contributions have been found in literature and the existing ones rely on linear models. Pinto et al. (Comp. Chem. Engng. 24, 2259-2276, 2000), presented a superstructure that represents a general topology and allows the implementation of nonlinear process models as well as blending relations. The main objective of the present work is to extend the single refinery model to a corporate planning model that contains multiple refineries, which can be connected by supply pipelines in common. Intermediate streams also interconnect refineries in order to take advantage of each plant infrastructure. The model is optimized along a planning horizon resulting in a large scale Mixed Integer Nonlinear Program (MINLP). The non-linearity arises from blending equations and physical properties. The objective function maximizes net present value under raw material and product inventory level constraints as well as mass balance and operating constraints in each refinery. Finally, detailed analysis for different crude oil types and product demand scenarios is incorporated in the model. A real-world application is developed for a refinery network composed of three refineries. Different petroleum types are supplied to the refineries from a single oil terminal. Results show that the optimization of the supply chain presents clear advantages of the corporate planning with respect to multiple one-site refinery production planning.

[1]  K. Tan.  A framework of supply chain management literature , 2001 .

[2]  Richard Lamming,et al.  Squaring lean supply with supply chain management , 1996 .

[3]  Jose M. Pinto,et al.  PLANNING AND SCHEDULING MODELS FOR REFINERY OPERATIONS , 2000 .

[4]  Ignacio E. Grossmann,et al.  Integrating complex economic objectives with the design and planning of offshore oilfield infrastructures , 2000 .

[5]  Sunwon Park,et al.  Robust investment model for long-range capacity expansion of chemical processing networks under uncertain demand forecast scenarios , 1998 .

[6]  Ignacio E. Grossmann,et al.  An Iterative Aggregation/Disaggregation Approach for the Solution of a Mixed-Integer Nonlinear Oilfield Infrastructure Planning Model , 2000 .

[7]  I. Grossmann,et al.  Logic-based MINLP algorithms for the optimal synthesis of process networks , 1996 .

[8]  I. Grossmann,et al.  Modeling issues and implementation of language for disjunctive programming , 2000 .

[9]  Jose M. Pinto,et al.  A planning model for petroleum refineries , 2000 .

[10]  Douglas J. Thomas,et al.  Coordinated supply chain management , 1996 .

[11]  Efstratios N. Pistikopoulos,et al.  A Mixed Integer Optimization Strategy for Integrated Gas/Oil Production , 2002 .

[12]  Kumaraswamy Ponnambalam,et al.  An interior point method implementation for solving large planning problems in the oil refinery industry , 1992 .

[13]  Jose M. Pinto,et al.  A Mixed-Integer Optimization Strategy for Oil Supply in Distribution Complexes , 2003 .

[14]  Christodoulos A. Floudas,et al.  Optimal location of vertical wells: Decomposition approach , 1999 .

[15]  Sunwon Park,et al.  Supply chain optimization in continuous flexible process networks , 2000 .

[16]  Supply Chain Optimization Involving Long-Term Decision-Making , 2002 .

[17]  Lúcia Valéria Ramos de Arruda,et al.  Modelling Liquefied Petroleum Gas Storage and Distribution , 2002 .

[18]  David Kendrick,et al.  GAMS, a user's guide , 1988, SGNM.

[19]  B. Beamon.  Supply chain design and analysis:: Models and methods , 1998 .

[20]  I. Grossmann,et al.  A combined penalty function and outer-approximation method for MINLP optimization : applications to distillation column design , 1989 .

[21]  Ignacio E. Grossmann,et al.  Dynamic modeling and classical control theory for supply chain management , 2000 .

[22]  Ignacio E. Grossmann,et al.  Disjunctive multiperiod optimization methods for design and planning of chemical process systems , 1999 .

[23]  Lúcia Valéria Ramos de Arruda,et al.  A Mixed Integer Programming Approach for Scheduling Commodities in a Pipeline , 2002 .

[24]  T. N. Sear,et al.  Logistics Planning in the Downstream Oil Industry , 1993 .

[25]  Ben Hua,et al.  Supply chain optimization of continuous process industries with sustainability considerations , 2000 .

[26]  Arne Stolbjerg Drud,et al.  CONOPT - A Large-Scale GRG Code , 1994, INFORMS J. Comput..

[27]  Anthony D. Ross,et al.  Performance-based strategic resource allocation in supply networks , 2000 .

[28]  Marc Goetschalckx,et al.  Strategic production-distribution models: A critical review with emphasis on global supply chain models , 1997 .

[29]  Laureano F. Escudero,et al.  CORO, a modeling and an algorithmic framework for oil supply, transformation and distribution optimization under uncertainty , 1999, Eur. J. Oper. Res..

[30]  Eleftherios Iakovou,et al.  An interactive multiobjective model for the strategic maritime transportation of petroleum products: risk analysis and routing , 2001 .

[31]  Patrick Linke,et al.  Optimisation of Oilfield Development Production Capacity , 2002 .

[32]  Michael A. H. Dempster,et al.  Planning logistics operations in the oil industry , 2000, J. Oper. Res. Soc..

[33]  A. S. Cullick,et al.  Optimal Planning and Scheduling of Offshore Oil Field Infrastructure Investment and Operations , 1998 .

[34]  Nikolaos V. Sahinidis,et al.  Process planning in a fuzzy environment , 1997, Eur. J. Oper. Res..

[35]  Ignacio E. Grossmann,et al.  Dynamic Modeling and Decentralized Control of Supply Chains , 2001 .