Soft sensor design based on fuzzy C-Means and RFN_SVR for a stripper column

Soft sensors have been extensively employed in the dynamic setting of industrial factories. In general, a soft sensor is a computer program used for estimating the variables, which are impossible or very hard to be acquired in real time by using the easily accessible process measurements. In the present research, a soft sensor by incorporating the Fuzzy C-Means clustering with the Recursive Finite Newton algorithm for training the Support Vector Regression (FCM_RFN_SVR) is proposed. In this technique, the samples are partitioned into smaller partitions and with the aid of the RFN_SVR, a local model for each partition is adjusted. The presented method is applied to a stripper column in order to estimate the concentration of the bottom product H2S. The gained results were compared with a typical SVR method, where the findings confirmed that the presented technique is stronger and relatively more capable in enhancing the generalizability of the soft sensor. (C) 2015 Elsevier B.V. All rights reserved.