Inferential estimation and optimal control of a batch polymerisation reactor using stacked neural networks

Inferential estimation and optimal control of a batch polymerisation reactor using bootstrap aggregated neural networks are presented in this contribution. In responsive agile manufacturing, the frequent change in product designs makes it less feasible to develop mechanistic model based estimation and control strategies. Techniques for developing robust empirical models from a limited data set have to be capitalised. The bootstrap aggregated neural network approach to nonlinear empirical modelling is very effective in building empirical models from a limited data set. It can also provide model prediction confidence bounds, thus, provide process operators with additional indications on how confident a particular prediction is. Robust neural network based techniques for inferential estimation of polymer quality, estimation of the amount of reactive impurities and reactor fouling during an early stage of a batch, and optimal control of batch polymerisation process are studied in this contribution. The effectiveness of these techniques is demonstrated by simulation studies.