Data-driven soft sensor approach for online quality prediction using state dependent parameter models

Abstract The goal of this paper is to design and implementation of a new data-driven soft sensor that uses state dependent parameter (SDP) models to improve product quality monitoring. The SDP model parameters assumed to be function of the system states, which are estimated by data-based modeling philosophy and SDP method. Soft sensing performance of the proposed method is validated on a simulated continuous stirred tank reactor and an industrial debutanizer column. A comparative study of different soft sensing methods for online monitoring of debutanizer column is also carried out. The results show that the process non-linearity can also be addressed under this modeling method and the change of the process is also well tracked when missing data exist in the observed data. The results indicate that the new model is much more robust and reliable with less model parameters, which make it useful for industrial applications. In addition, the performance indexes show the superiority of the proposed model over other conventional soft sensing methods.

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