Soft sensors are inferential estimators when the employment of hardware sensors is
inapplicable, expensive, or difficult in industrial plant processes. Currently, a simple soft sensor, namely
locally weighted partial least squares (LW-PLS), which can cope with the nonlinearity of the process,
has been developed. However, LW-PLS exhibits the disadvantages of handling strong nonlinear process
data. To address this problem, Kernel functions are integrated into LW-PLS to form locally weighted
Kernel partial least squares (LW-KPLS). Notice that a minimal study was carried out on the impact of
different kernel functions that have not been integrated with the LW-KPLS, in which this model has the
potential to be applied to different chemical-related nonlinear processes. Thus, this study investigates
the predictive performance of LW-KPLS with several different Kernel functions using three nonlinear
case studies. As the results, the predictive performances of LW-KPLS with Polynomial Kernel are better
than other Kernel functions. The values of root-mean-square errors (RMSE) and error of approximation
(Ea) for the training and testing dataset by utilizing this Kernel function are the lowest in their respective
case studies, which are 34.60% to 95.39% lower for RMSEs values and 68.20% to 95.49% smaller for
Ea values
