Multiobjective optimization of the continuous casting process for poly (methyl methacrylate) using adapted genetic algorithm

Abstract

The nondominated sorting genetic algorithm (NSGA) has been used to optimize the operation of the continuous casting of a film of poly (methyl methacrylate). This process involves two reactors, namely, an isothermal plug flow tubular reactor (PFTR) followed by a nonisothermal film reactor. Two objective functions have been used in this study: the cross-section average value of the monomer conversion, x̄mf, of the product is maximized, and the length, zf, of the film reactor is minimized. Simultaneously, the cross-section average value of the number-average molecular weight of the product is forced to have a certain prescribed (desired) value. It is also ensured that the temperature at any location in the film being produced lies below a certain value, to avoid degradation reactions. Seven decision variables are used in this study: the temperature of the isothermal PFTR, the flow rate of the initiator in the feed to the PFTR (for a specified feed flow rate of the monomer), the film thickness, the monomer conversion at the output of the PFTR, and three coefficients describing the wall temperature to be used in the film reactor. Sets of nondominating (equally good) optimal solutions (Pareto sets) have been obtained due to the conflicting requirements for the several conditions studied. It is interesting to observe that under optimal conditions, the exothermicity of the reactions drives them to completion near the center of the film, while heat conduction and higher wall temperature help to achieve this in the outer regions.