Abstract
Gasoline is one of the most valuable products in an oil refinery and can account for as much as 60–70% of total profit.
Optimal integrated scheduling of gasoline blending and order delivery operations can significantly increase profit by
avoiding ship demurrage, improving customer satisfaction, minimizing quality give-aways, reducing costly transitions
and slop generation, exploiting low-quality cuts, and reducing inventory costs. In this article, we first introduce a new unit-specific event-based continuous-time formulation for the integrated treatment of recipes, blending, and scheduling of gasoline blending and order delivery operations. Many operational features are included such as nonidentical parallel blenders, constant blending rate, minimum blend length and amount, blender transition times, multipurpose product tanks, changeovers, and piecewise constant profiles for blend component qualities and feed rates. To address the nonconvexities arising from forcing constant blending rates during a run, we propose a hybrid global optimization approach incorporating a schedule adjustment procedure, iteratively via a mixed-integer programming and nonlinear programming scheme, and a rigorous deterministic global optimization approach. The computational results demonstrate that our proposed formulation does improve the mixed-integer linear programming relaxation of Li and Karimi, Ind. Eng. Chem. Res., 2011, 50, 9156–9174. All examples are solved to be 1%-global optimality with modest computational
effort.
Optimal integrated scheduling of gasoline blending and order delivery operations can significantly increase profit by
avoiding ship demurrage, improving customer satisfaction, minimizing quality give-aways, reducing costly transitions
and slop generation, exploiting low-quality cuts, and reducing inventory costs. In this article, we first introduce a new unit-specific event-based continuous-time formulation for the integrated treatment of recipes, blending, and scheduling of gasoline blending and order delivery operations. Many operational features are included such as nonidentical parallel blenders, constant blending rate, minimum blend length and amount, blender transition times, multipurpose product tanks, changeovers, and piecewise constant profiles for blend component qualities and feed rates. To address the nonconvexities arising from forcing constant blending rates during a run, we propose a hybrid global optimization approach incorporating a schedule adjustment procedure, iteratively via a mixed-integer programming and nonlinear programming scheme, and a rigorous deterministic global optimization approach. The computational results demonstrate that our proposed formulation does improve the mixed-integer linear programming relaxation of Li and Karimi, Ind. Eng. Chem. Res., 2011, 50, 9156–9174. All examples are solved to be 1%-global optimality with modest computational
effort.
Original language | English |
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Pages (from-to) | 2043-2070 |
Journal | AIChE Journal |
Volume | 62 |
Issue number | 6 |
Early online date | 20 Apr 2016 |
DOIs | |
Publication status | Published - 20 Apr 2016 |