Optimizing Control of a Continuous Polymerization Reactor

Abstract

In this contribution we study the application of non-linear model predictive control to a continuous polymerization of acrylic acid in tubular reactors with multiple side injections of monomer. The background of this work is to transfer the polymerization from semi-batch to continuous operation. Model Predictive Control (MPC) is the obvious candidate to control such a multi-input system. The reactor configuration and polymerization reaction make the application of MPC very challenging. The controller employs a discretized dynamic pde model of the process and optimizes the productivity of the plant online while keeping the product quality parameters within the predefined constraints. The spatial domain of the model is discretized by applying the Weighted Essentially Non Oscillatory (WENO) scheme. Besides testing the controller for a nominal case in which the control model is identical to the existing plant, the controller has been simulated for two model-plant mismatch cases, caused by fouling and feed impurities. For the both cases, a moving window estimation scheme is applied to estimate the unknown parameters and to update the model used by NMPC. The results show that the controller can increase the product throughput considerably and has a robust performance in the presence of the model-plant mismatch. Moreover, the effect of formulating the quality constraints as soft constraints is studied.