Source-based discrete and continuous-time formulations for the crude oil pooling problem

•Focus on crude oil scheduling problem with linear blending rules.•Simpler formulation handles both discrete- and continuous-time.•MILP-NLP solution strategy solves non-convex MINLP.•Competitive to commercial global optimization solvers for some instances.•Best performer varies with time representat...

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Bibliographic Details
Published in:Computers & chemical engineering Vol. 93; pp. 382 - 401
Main Author: Castro, Pedro M.
Format: Journal Article
Language:English
Published: Elsevier Ltd 04.10.2016
Subjects:
Algorithms
Constraint modelling
Crude oil
Global optimization
Logistics
Mathematical modeling
Mathematical models
Mixed-integer linear relaxations
Mixed-integer quadratically constrained problem
Nonlinear programming
Optimization
Tanks
Global optimization
Mathematical modeling
Mixed-integer quadratically constrained problem
Mixed-integer linear relaxations
ISSN:0098-1354, 1873-4375
Online Access:Get full text
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Summary:•Focus on crude oil scheduling problem with linear blending rules.•Simpler formulation handles both discrete- and continuous-time.•MILP-NLP solution strategy solves non-convex MINLP.•Competitive to commercial global optimization solvers for some instances.•Best performer varies with time representation and objective function. The optimization of crude oil operations in refineries is a challenging scheduling problem due to the need to model tanks of varying composition with nonconvex bilinear terms, and complicating logistic constraints. Following recent work for multiperiod pooling problems of refined petroleum products, a source-based mixed-integer nonlinear programming formulation is proposed for discrete and continuous representations of time. Logistic constraints are modeled through Generalized Disjunctive Programming while a specialized algorithm featuring relaxations from multiparametric disaggregation handles the bilinear terms. Results over a set of test problems from the literature show that the discrete-time approach finds better solutions when minimizing cost (avoids source of bilinear terms). In contrast, solution quality is slightly better for the continuous-time formulation when maximizing gross margin. The results also show that the specialized global optimization algorithm can lead to lower optimality gaps for fixed CPU, but overall, the performance of commercial solvers BARON and GloMIQO are better.
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ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2016.06.016