Multi-kernel Gaussian process regression and Bayesian model averaging based nonlinear state estimation and quality prediction of multiphase batch processes

Batch processes are characterized by inherent nonlinearity, multiplicity of operating phases, between-phase transient dynamics and batch-to-batch uncertainty that pose significant challenges for accurate state estimation and quality prediction. Conventional multi-model strategies, however, may be ill-suited for multiphase batch processes because the localized models do not specifically characterize the complex transient dynamics between two consecutive operating phases. In this study, a novel Bayesian model averaging based multi-kernel Gaussian process regression (BMA-MKGPR) approach is proposed for state estimation and quality prediction of nonlinear batch processes with multiple operating phases and between-phase transient dynamics. The new approach is applied to a simulated batch polymerization process and the result comparison shows that it can effective handle multiple nonlinear operating phases, between-phase transient dynamics and process uncertainty with high prediction accuracies.