Economic stochastic nonlinear model predictive control of a semi-batch polymerization reaction

Abstract

Batch processes are ubiquitous in the chemical industry and difficult to control, such that nonlinear model predictive control is one of the few promising control techniques. Many chemical process models however are affected by various uncertainties, which can lower the performance and lead to constraint violations. In this paper we propose a framework for output feedback stochastic nonlinear model predictive control (SNMPC) to consider the uncertainties explicitly, which are assumed to follow known probability distributions. Polynomial chaos expansions are employed both for the formulation of the SNMPC algorithm and a nonlinear filter for the estimation of the uncertain parameters online given noisy measurements. The effectiveness of the proposed SNMPC scheme was verified on an extensive case study involving the production of the polymer polypropylene glycol in a semi-batch reactor.