A novel continuous time representation for the scheduling of pipeline systems with pumping yield rate constraints

Pipeline systems play a key role in the petroleum business. These operational systems provide connection between ports and/or oil fields and refineries (upstream), as well as between these and consumer markets (downstream). The purpose of this work is to propose a novel MINLP formulation based on a continuous time representation for the scheduling of multiproduct pipeline systems that must supply multiple consumer markets. Moreover, it also considers that the pipeline operates intermittently and that the pumping costs depend on the booster stations yield rates, which in turn may generate different flow rates. The proposed continuous time representation is compared with a previously developed discrete time representation [Rejowski, R., Jr., & Pinto, J. M. (2004). Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling. Computers and Chemical Engineering, 28, 1511] in terms of solution quality and computational performance. The influence of the number of time intervals that represents the transfer operation is studied and several configurations for the booster stations are tested. Finally, the proposed formulation is applied to a larger case, in which several booster configurations with different numbers of stages are tested.

[1]  H. D. Ratliff,et al.  Sequencing inputs to multi-commodity pipelines , 1995, Ann. Oper. Res..

[2]  J. M. Pinto,et al.  Mixed-Integer Programming Approach for Short-Term Crude Oil Scheduling , 2004 .

[3]  Flávio Neves,et al.  A mixed integer programming approach for scheduling commodities in a pipeline , 2004, Comput. Chem. Eng..

[4]  José M. Pinto,et al.  Scheduling of a multiproduct pipeline system , 2003, Comput. Chem. Eng..

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

[6]  Hong Yan,et al.  Logic cuts for processing networks with fixed charges , 1994, Comput. Oper. Res..

[7]  I. Karimi,et al.  Agent-based supply chain management—1: framework , 2002 .

[8]  Rajagopalan Srinivasan,et al.  A new continuous-time formulation for scheduling crude oil operations , 2004 .

[9]  Rajagopalan Srinivasan,et al.  Agent-based supply chain management—2: a refinery application , 2002 .

[10]  Ignacio E. Grossmann,et al.  A strategy for the integration of production planning and reactive scheduling in the optimization of a hydrogen supply network , 2003, Comput. Chem. Eng..

[11]  Peter Stone,et al.  Agent-based supply chain management , 2004, AAMAS 2004.

[12]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

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

[14]  Marianthi G. Ierapetritou,et al.  Efficient short-term scheduling of refinery operations based on a continuous time formulation , 2004, Comput. Chem. Eng..

[15]  R. Raman,et al.  Modelling and computational techniques for logic based integer programming , 1994 .

[16]  J. D. Kelly,et al.  Crude oil blend scheduling optimization: an application with multimillion dollar benefits. Part 2 , 2003 .

[17]  Nilay Shah,et al.  Mathematical programming techniques for crude oil scheduling , 1996 .

[18]  José M. Pinto,et al.  Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling , 2004, Comput. Chem. Eng..

[19]  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..

[20]  Rajagopalan Srinivasan,et al.  Novel Solution Approach for Optimizing Crude Oil Operations , 2004 .

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

[22]  Ignacio E. Grossmann,et al.  New general continuous-time state-task network formulation for short-term scheduling of multipurpose batch plants , 2003 .

[23]  Jaime Cerdá,et al.  Optimal scheduling of multiproduct pipeline systems using a non-discrete MILP formulation , 2004, Comput. Chem. Eng..

[24]  José M. Pinto,et al.  A general modeling framework for the operational planning of petroleum supply chains , 2004, Comput. Chem. Eng..

[25]  S. Ramani,et al.  PIPES: A heuristic search model for pipeline schedule generation , 1997, Knowl. Based Syst..

[26]  Aldo R. Vecchietti,et al.  Modeling of discrete/continuous optimization problems: characterization and formulation of disjunctions and their relaxations , 2003, Comput. Chem. Eng..