Predictive control of batch processes using principal surface/space analysis and fuzzy clustering empirical models

Batch/semi-batch quality control has attracted many attentions among the chemical engineering researchers recently. In this work, a novel empirical model based quality control approach for batch processes is developed. The model implemented is based on independent nonlinear principal components obtained by principal surface analysis-a very hot topic in the area of statistical modeling. The partial initial conditions, intermediate measurements obtained from each batch are analyzed using this approach to extract the independent nonlinear principal components. An empirical model with the nonlinear principal components as inputs is built to predict product quality. Correcting actions based on the initial conditions and intermediate measurements are taken based on the predictions of the empirical model. In this work, the problem formulation and solution method are presented. The illustrative examples include a simulation example and experimental study of batch reactive distillation process. The simulation and experimental results show the proposed approach is highly promising