Model predictive quality control of polymethyl methacrylate

Abstract

This Chapter considers the problem of quality control for the production of Polymethyl methacrylate (PMMA) to achieve prescribed number and weight average molecular weights. To this end, with a detailed first-principles model used to simulate the process, a dynamic multiple-model based approach is implemented to capture the process dynamics from past batch data. Subsequently, the multiple-model is integrated with a quality model to enable predicting the end quality based on initial conditions and candidate control input (jacket temperature) moves. A data-driven model predictive controller is then designed to achieve the desired product quality while satisfying input constraint, a lower bound on the conversion, as well as additional constraints that enforce the validity of data-driven models for the range of chosen input moves. Simulation results demonstrate the superior performance (10.3% and 7.4%) relative error in number average and weight average molecular weight compared to 20.4% and 19.0%) of the controller over traditional trajectory tracking approaches.