A novel soft sensor modelling method based on kernel PLS

Abstract

A novel soft sensor modeling method based on kernel partial least squares (kernel PLS, KPLS) was proposed. Kernel PLS is a promising regression method for tackling nonlinear problems because it can efficiently compute regression coefficients in high-dimensional feature space by means of nonlinear kernel function. Application results to the real data in a fluid catalytic cracking unit (FCCU) process show that the proposed method can effectively capture nonlinear relationship among variables and have better estimation performance than PLS and other linear approaches.