Adaptive Soft Sensor Development for Non-Gaussian and Nonlinear Processes

Just-in-time (JIT) adaptive soft sensors have been widely used in chemical processes because they can deal with slow-varying processes, abrupt process changes, and outliers. However, these traditional JIT algorithms including locally weighted partial least square (LW-PLS) have limitations in dealing with non-Gaussian distributed and nonlinear data. To address these issues, a modified LW-PLS-based JIT algorithm, namely, ensemble locally weighted independent component kernel partial least square (E-LW-IC-KPLS) is proposed. Its predictive performances were tested using the data generated from a numerical example and two simulated plants. Then, the results were compared to the ones resulted from LW-PLS, locally weighted kernel partial least square (LW-KPLS), and locally weighted independent component kernel partial least square (LW-IC-KPLS) algorithms. From these comparative studies, it is evident that E-LW-IC-KPLS is superior compared to its traditional counterparts concerning predictive performances. The predictive errors for E-LW-IC-KPLS are lower by 8-94% than those of LW-PLS, LW-KPLS, and LW-IC-KPLS.