The principal component regression
(PCR) based soft sensor modeling technique has been widely used for
process quality prediction in the last decades. While most industrial
processes are characterized with nonlinearity and time variance, the
global linear PCR model is no longer applicable. Thus, its nonlinear
and adaptive forms should be adopted. In this paper, a just-in-time
learning (JITL) based locally weighted kernel principal component
regression (LWKPCR) is proposed to solve the nonlinear and time-variant
problems of the process. Soft sensing performance of the proposed
method is validated on an industrial debutanizer column and a simulated
fermentation process. Compared to the JITL-based PCR, KPCR, and LWPCR
soft sensing approaches, the root-mean-square errors (RMSE) of JITL-based
LWKPCR are the smallest and the prediction results match the best
with the actual outputs, which indicates that the proposed method
is more effective for quality prediction in nonlinear time-variant
processes.
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