Optimization Techniques for Blending and Scheduling of Oil-refinery Operations

This paper presents a novel MILP formulation that addresses the simultaneous optimization of the short-term scheduling and blending 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 large 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. The proposed optimization approach is oriented towards providing an effective and integrated solution for both the scheduling and the blending problem. In order to provide convenient solutions for all circumstances, different alternatives to cope with infeasible problems are presented in detail. The new method is illustrated by solving several real world problems with very low computational requirements.