Mixed-Integer Programming Models for the Scheduling and Operational Optimization of Multiproduct Continuous Plants

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Abstract

This paper addresses the problem of the simultaneous scheduling and optimization of the operating conditions of continuous multistage multiproduct plants with intermediate storage. First, an MINLP model, called TSPFLOW, that is based on the TSP formulation for product sequencing is proposed for the scheduling of the operation of such plants. TSPFLOW leads to a significant reduction in the number of constraints and continuous and binary variables from a formulation previously reported (Pinto, J. M.; Grossmann, I. E. Comput. Chem. Eng. 1994, 18, 797) and, consequently, a one-order-of-magnitude reduction in CPU time. Moreover, TSPFLOW solves instances two times as large as those formerly reported. Second, production rates and yields are introduced as additional optimization variables to state the simultaneous problem of scheduling with operational optimization. A linearization approach that employs the discretization of nonlinear variables is presented and compared to the direct solution of the original MINLP. Results show that nonlinear restrictions are more effective than linear discrete restrictions from the points of view of both optimality and computational effort. The tradeoffs involved are very complex, and the development of a straightforward method (general rule) for optimal scheduling of the operation seems to be less effective than the proposed approach.

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