Post-Optimality Analysis of Optimal Refinery Production

The aim of refinery optimization is to find the best operation conditions that can maximize production of oil fractions and minimize operating costs with respecting operational and environmental constraints. Uncertainty or variations in some parameters (e.g., demand and supply of each processing unit or products' price) can seriously affect the optimization results and lead to inefficient operation. Many techniques of stochastic programming were proposed to handle the effect of variations in process parameters in order to determine a robust operation conditions; however, applications of post-optimality analysis have received less attention especially in refinery industry. In this work, post-optimality analysis is used to study the effect of such variations on the optimal solution of refinery process that is simplified and formulated as a LP model. The general objective of this study is to try to bridge the gap between the theory and practice of the post-optimality analysis in process engineering. This work attempts to use a modified method of post-optimality analysis that jointly use sensitivity relations and stability region calculations to provide the decision maker in the refinery with valuable and easy-to-use information that helps in handling the effect of variation in process parameters. The results of this study can help the decision maker to identify sensitive parameters that need accurate estimate or intensive monitoring; and compute their stability limits, i.e., allowable variation ranges, within which the operation remains optimum. Independent and simultaneous variations in the objective function coefficients (e.g., products' price) and in the RHS of the constraints (e.g., product specifications, demand and supply) are considered in this study.