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Global optimization for scheduling refinery crude oil operations |
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| Authors: | Ramkumar Karuppiah Kevin C Furman Ignacio E Grossmann |
| Affiliation: | 1. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA;2. ExxonMobil Research and Engineering, Annandale, NJ 08801, USA;1. Department of Management Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark;2. TUM School of Management, Technische Universität München, Munich, Germany;1. Department of Chemical Engineering, University of Sistan and Baluchestan, P.O. Box 98164-161, Zahedan, Iran;2. Department of Mechanic Engineering, University of Sistan and Baluchestan, Zahedan, Iran;3. Department of Chemical Engineering, International Campus, University of Sistan and Baluchestan, Chabahar, Iran;1. Process systems Engineering Group, School of Engineering, Cranfield University, MK43 0AL, UK;2. Warri Refining and Petrochemical Company Limited, P.M.B. 44, Effurun, Nigeria;3. Department of Engineering, University of Hull, Hull HU6 7RX, UK |
| Abstract: | In this work we present an outer-approximation algorithm to obtain the global optimum of a nonconvex mixed-integer nonlinear programming (MINLP) model that is used to represent the scheduling of crude oil movement at the front-end of a petroleum refinery. The model relies on a continuous time representation making use of transfer events. The proposed algorithm focuses on effectively solving a mixed-integer linear programming (MILP) relaxation of the nonconvex MINLP to obtain a rigorous lower bound (LB) on the global optimum. Cutting planes derived by spatially decomposing the network are added to the MILP relaxation of the original nonconvex MINLP in order to reduce the solution time for the MILP relaxation. The solution of this relaxation is used as a heuristic to obtain a feasible solution to the MINLP which serves as an upper bound (UB). The lower and upper bounds are made to converge to within a specified tolerance in the proposed outer-approximation algorithm. On applying the proposed technique to test examples, significant savings are realized in the computational effort required to obtain provably global optimal solutions. |
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