Adaptive soft sensor for online prediction based on enhanced moving window GPR

Process nonlinearity and time-varying behavior of industrial systems are the main factors for poor performance of online soft sensors. To ensure high predictive accuracy, adaptive soft sensor is a common practice. In this paper, an adaptive soft sensor based on moving window Gaussian process regression (GPR) is presented. To make the moving window strategy more efficient, a just-in-time learning (JITL) algorithm is used to enhance the performance, which avoids the selection of a window size that original moving window approaches have to select . The effectiveness of the proposed method is demonstrated by an example concerning the H2S concentrations of tail gas in the sulfur recovery unit (SRU). Compared with other soft sensor methods, the proposed JITL based moving window GPR has higher accuracy.