Soft Sensor Development Based on the Hierarchical Ensemble of Gaussian Process Regression Models for Nonlinear and Non-Gaussian Chemical Processes

Chemical processes are often characterized by nonlinearity, non-Gaussianity, shifting modes, and inherent uncertainty that pose significant challenges for accurate quality prediction. Therefore, a novel soft sensor based on the hierarchical ensemble of Gaussian process regression models (HEGPR) is developed for the quality variable predication of nonlinear and non-Gaussian chemical processes. The method first creates a set of diverse input variable sets based on multiple random resampling data sets and a partial mutual information criterion. Then, a set of the sample partition based ensemble Gaussian process regression model (SP-EGPR) is built from different input variable sets and the corresponding subspace training data sets by the Gaussian mixture model. Next, those influential local SP-EGPR models obtained after partial least-squares (PLS) pruning are used for the first level of ensemble learning. Finally, the second level of ensemble learning is achieved by integrating the high-performance prediction...