Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization

TL;DR

This paper develops a constrained nonlinear model predictive control strategy for an MMA polymerization process, utilizing evolutionary optimization to handle input constraints and improve thermal trajectory tracking in a batch reactor.

Contribution

It introduces a novel approach combining trajectory linearization, a multiple model adaptive predictive controller, and genetic algorithms for constrained optimization in polymerization control.

Findings

Effective thermal trajectory tracking achieved

Input power constraints successfully enforced

Genetic algorithms optimized control inputs in real-time

Abstract

In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.