Abstract
Uncertainty in component quality in gasoline blending due to measurement errors and variation in operation leads to planned blends which may not meet quality specifications and re-blending is required. Formulating gasoline blending as chance constrained programming enables a decision maker to decide what percentage of blends will be guaranteed to meet the specifications and balance the increased cost of blends vs. the cost of having to re-blend the off-spec blends. Chance constrained formulation makes the gasoline blend problem nonlinear and nonconvex. In this work, we employ a supply-demand pinch based algorithm to optimize gasoline blend planning with uncertainty in components qualities and examine its performance vs. full-space model. The supply-demand pinch algorithm decomposes the problem into two sub-problems, top-level (NLP) computes optimal blend recipes and the bottom-level (MILP) computes an optimal production plan using the recipes computed at the top-level. Computational efficiency of the algorithm is verified by case studies.
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