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

• New soft sensor based on RFN_SVR is proposed.
• Parameters of RFN_SVR is tuned with PSO algorithm and 5-fold cross validation.
• Another new soft sensor is designed based on combination of Fuzzy C-Means (FCM) with RFN_SVR (FCM_RFN_SVR).
• This soft sensors are used to estimate the bottom product concentration of 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.