Development of inferential models for fractionation reformate unit

Abstract

Industrial facilities show an increasing need for continuous measurement and monitoring a large number of process variables due to strict product quality requirements, environmental laws and for advanced process control application. On-line analyzers typically suffer from long measurement delays not desirable in continuous control. Suitable alternative are soft sensors and inferential control. In this paper the development of soft sensor models for the estimation of light reformate benzene content is carried out. Linear dynamical autoregressive model with external inputs (ARX), autoregressive moving average model with exogenous inputs (ARMAX) and Box-Jenkins (BJ) models are developed. For the regression vector optimization usually performed by trial and error, Genetic Algorithm (GA) and Simulated Annealing (SA) methods have been applied. The results indicate that the GA and SA as global optimization methods are suitable for the regressor order estimation of linear dynamical models with multiple inputs. Based on developed soft sensors, it is possible to apply advanced process control schemes.