A soft sensor development for the estimation of benzene content in catalytic reformate

As vehicle emission standards become more stringent, there is an increasing need for continual monitoring of benzene content in gasoline. Since the on-line analyzers are often unavailable, and laboratory analyses are infrequently obtained, soft sensors for the estimation of benzene content of light reformate are developed. Soft sensors are developed using system identification methods. Experimental data is acquired from the refinery distributed control system (DCS) and include continuously measured variables and analyzer assays available on-line. In the present work, the development of a Finite Impulse Response (FIR) model, an Output Error (OE) model and an Auto-Regressive Model with Exogenous Inputs (ARX) model are presented. To overcome the problem of selecting the best model parameters by trial and error, genetic algorithm was used. Based on developed soft sensors, it is possible to entirely replace on-line analyzers with soft sensors by embedding the model in a DCS on-site.