A Novel Approach for Prediction of Industrial Catalyst Deactivation Using Soft Sensor Modeling

Abstract: Soft sensors are used for fault detection and prediction of the process variables in chemical processing units, for which the online measurement is difficult. The present study addresses soft sensor design and identification for deactivation of zeolite catalyst in an industrial-scale fixed bed reactor based on the process data. The two main reactions are disproportionation (DP) and transalkylation (TA), which change toluene and C9 aromatics into xylenes and benzene. Two models are considered based on the mass conservation around the reactor. The model parameters are estimated by data-based modeling (DBM) philosophy and state dependent parameter (SDP) method. In the SDP method, the parameters are assumed to be a function of the system states. The results show that the catalyst activity during the period under study has approximately a monotonic trend. Identification of the system clearly shows that the xylene concentration has a determining role in the conversion of reactions. The activation energies for both DP and TA reactions are found to be 43.8 and 18 kJ/mol, respectively. The model prediction is in good agreement with the observed industrial data.