Predictive control of quality in batch polymerization using hybrid ANN models

Two issues involving the methodology used for on‐line control of product quality in batch manufacturing processes are addressed: the generation of fast, data‐driven process models and the use of such process models for on‐line feedback control of product quality. The methodology is investigated using the example of the control of dispersity and molecular weight distribution in a batch reactor for emulsion polymerization of vinyl acetate. An artifical neural network (ANN) is used as a model to predict the quality as a function of the manipulated variables and on‐line measurements. This model is constructed using an augmented dataset that integrates experimental information and knowledge from a mathematical model. The proposed model is compared with other types such as a theoretical model whose key parameters are fitted to experimental data. The hybrid ANN is superior to the parameter‐fitting approach for this case. Experimental and simulation studies confirm the advantage of using the proposed model and the predictive control algorithm.