Abstract
Soft sensors have gained wide popularity in the industrial processes for online quality prediction in the recent years. In the case of online deployment, it is important to incorporate fewer input variables to improve the performance of the soft sensor. Therefore, the goal of this paper is to present an approach for the development of more efficient and less complex soft sensors in order to maximize the accuracy as well as to minimize the number of input soft sensing variables. The approach is based on multi-state-dependent parameter (MSDP) models, in which model parameters are estimated in a multivariable state space employing the Kalman filter and fixed interval smoothing algorithms. The proposed MSDP-based soft sensor is applied to an industrial sulfur recovery unit (SRU) in order to predict of H2S and SO2 concentrations. The model is consequently compared with the other soft sensing techniques, which are based on the same benchmark data set of the case study. The prediction results show that the designed MSDP-based soft sensors are more robust and exhibit higher predictive performance than other presented soft sensing methods based on the root mean square errors and Pearson correlation coefficient criterions while using fewer input variables.
Published Version
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