Multivariate Statistical Monitoring of an Industrial SBR Process. Part I: Prediction of Quality Variables

GODOY, J.L.; MINARI, R.J.; VEGA, J.R.; MARCHETTI, J.L.

Rosario

Congreso; RPIC 2009 - XIII Reunión de Trabajo en Procesamiento de la Información y Control; 2009

This work investigates the production of Styrene-Butadiene Rubber (SBR) in an industrial continuous 7-reactor train operated under steady-state conditions. This work aims at developing a soft-sensor for on-line estimation of quality variables and statistical monitoring techniques capable of detecting and isolating physical sensor faults and no-measurable disturbances. In this first part, the pro-posed soft-sensor based on partial least squared is found, validated and compared with a previous de-velopment based on neural networks. The soft-sensor was developed on the basis of dense multi-variate sampling of steady-state (SS) simulations using a detailed model to on-line predicts the SS out-puts. The achieved results demonstrate that the mul-tivariate statistical approach provides a satisfactory sensitivity for anticipating changes in production and quality levels.