Model predictive statistical process control of chemical plants

Statistical process control aims at improving process operation by distinguishing abnormal process conditions from common cause variations. Improved process operation then results from correcting the abnormal conditions. In this paper an alternative, feedback based approach to process quality improvement is discussed. The goal is to use existing process measurements to help reduce the variability of product quality when its online measurement is not feasible. The approach is model based and it uses PCA to compress selected process measurements into scores. One or more manipulated setpoints are chosen and varied to counteract the effect of stochastic process disturbances on product quality. The methodology is illustrated on the Tennessee Eastman process where a 44% reduction in product variation is achieved.