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 of the approach 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 principal component analysis to compress the selected process measurements into scores, which are controlled. One or more manipulated setpoints are chosen and varied to counteract the effect of stochastic process disturbances on product quality. The approach discussed assumes that the selected process measurements correlate with product quality and that the stochastic disturbances that cause product variability are stationary. The methodology is illustrated on the Tennessee-Eastman process, where a 44% reduction in product variation is achieved. When nonstationary upsets such as steps occur, the score control setpoints need to be adjusted. A steady-state model predictive controller is discussed which overcomes the problem caused by step upsets. The application presented in the paper involves one manipulated variable, but the methodology is multivariable in nature.