A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations

Abstract

This paper presents a novel MILP-based method that addresses the simultaneous optimization of the off-line blending and the short-term scheduling problem in oil-refinery applications. Depending on the problem characteristics as well as the required flexibility in the solution, the model can be based on either a discrete or a continuous time domain representation. In order to preserve the model’s linearity, an iterative procedure is proposed to effectively deal with non-linear gasoline properties and variable recipes for different product grades. Thus, the solution of a very complex MINLP formulation is replaced by a sequential MILP approximation. Instead of predefining fixed component concentrations for products, preferred blend recipes can be forced to apply whenever it is possible. Also, different alternatives for coping with infeasible problems are presented. Sufficient conditions for convergence for the proposed approach are presented as well as a comparison with NLP and MINLP solvers to demonstrate that the method provides an effective integrated solution method for the blending and scheduling of large-scale problems. The new method is illustrated with several real world problems requiring very low computational requirements.