Dynamic optimization of a mma with vac copolymerization

Abstract

Increasing worldwide market competitiveness and reduced profit margins are pressing chemical and process industries to move towards a predictive control approach, based on first-principles mathematical models, as well as plant dynamic optimization. In this perspective, the paper focuses on the development of a nonlinear model predictive control (NMPC) to manage the copolymerization process of methyl methacrylate (MMA) with vinyl acetate (VAc), consisting of a jacketed continuous stirred tank reactor, a separator, and a recycle loop. This system presents a highly complex behavior, thus making difficult the success of controllers based on linear models. A detailed differential and algebraic mathematical model consists of 53 equations and is implemented in Fortran 90/95 to simulate the plant and setup the NMPC. The numerical solution is performed by using IMSL library. NMPC is proved to be superior to a linear model predictive control approach and appears to hold a considerable promise for such a reactor system.