Inferential Estimation of Polymer Melt Index Using Sequentially Trained Bootstrap Aggregated Neural Networks

The inferential estimation of a polymer melt index in an industrial polymerization process using aggregated neural networks is presented in this paper. The difficult-to-measure polymer melt index is estimated from easy-to-measure process variables, and their relationship is estimated using aggregated neural networks. The individual networks are trained on bootstrap re-samples of the original training data by a sequential training algorithm. In this training method, individual networks, within a bootstrap aggregated neural network model, are trained sequentially. The first network is trained to minimize its prediction error on the training data. In the training of subsequent networks, the training objective is not only to minimize the individual networks' prediction errors but also to minimize the correlation among the individual networks. Training is terminated when the aggregated network prediction performance on the training and testing data cannot be further improved. Application to real industrial data demonstrates that the polymer melt index can be successfully estimated using an aggregated neural network.