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

Inferential estimation of polymer melt index in an industrial polymerisation process using aggregated neural networks is presented in this paper. The difficult-to-measure polymer melt index is estimated from the 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 minimise its prediction error on the training data. In the training of subsequent networks, the training objective is not only to minimise the individual networks’ prediction errors but also to minimise 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 polymer melt index can be successfully estimated using an aggregated neural network.