Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes

Batch processes are characterized by inherent nonlinearity, multiple phases and time-varying behavior that pose great challenges for accurate state estimation. A multiphase just-in-time (MJIT) learning based kernel partial least squares (KPLS) method is proposed for multiphase batch processes. Gaussian mixture model is estimated to identify different operating phases where various JIT-KPLS frameworks are built. By applying Bayesian inference strategy, the query data is classified into a particular phase with the maximal posterior probability, and thus the corresponding JIT-KPLS framework is chosen for online prediction. To further improve the predictive accuracy of the MJIT-KPLS algorithm, a hybrid similarity measure and an adaptive selection strategy are proposed for selecting local modeling samples. Moreover, maximal similarity replacement rule is proposed to update database. A procedure of input variable selection based on partial mutual information is also presented. The effectiveness of the MJIT-KPLS algorithm is demonstrated through application to industrial fed-batch chlortetracycline fermentation process.