Optimizing scheduling of refinery operations based on piecewise linear models

Abstract

Optimizing scheduling is an effective way to improve the profit of refineries; it usually requires accurate models to describe the complex and nonlinear refining processes. However, conventional nonlinear models will result in a complex mixed integer nonlinear programming (MINLP) problem for scheduling. This paper presents a piecewise linear (PWL) modeling approach, which can describe global nonlinearity with locally linear functions, to refinery scheduling. Specifically, a high level canonical PWL representation is adopted to give a simple yet effective partition of the domain of decision variables. Furthermore, a unified partitioning strategy is proposed to model multiple response functions defined on the same domain. Based on the proposed PWL partitioning and modeling strategy, the original MINLP can be replaced by mixed integer linear programming (MILP), which can be readily solved using standard optimization algorithms. The effectiveness of the proposed strategy is demonstrated by a case study originated from a refinery in China.