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.