Adaptive soft sensor method based on online selective ensemble of partial least squares for quality prediction of chemical process

For accurate state prediction of chemical process characterized by connatural features of non-linearity and time variation, a novel online selective ensemble partial least squares learning paradigm, referred to as OSE-PLS, is proposed in this paper. First, the process states are differentiated to diverse local regions by just-in-time learning-based localization method to handle process non-linearity. Then, support vector domain description algorithm is applied to identify and remove redundant ones. Consequently, a variety of local PLS models are established for representing diverse process states. Inspired by the local outlier probability algorithm in outlier detection field, the combination weights of each PLS model are adaptively distinguished by definitely quantifying the probability that query data belong to an outlier in each local region. Finally, the prediction for query data is achieved through selective ensemble learning strategy. To effectively address the inherent time-varying characteristics of process, the OSE-PLS technique is equipped with adaptation mechanism of local model updating and online local model extraction, which enables OSE-PLS to acquire the capability of addressing both gradual and abrupt changes in process simultaneously. An industrial bisphenol-A distillation process is employed to demonstrate the superiorities of novel online selective ensemble partial least squares (OSE-PLS) approach.