A multivariate statistical controller for on-line quality improvement

Producing good quality products is an important process control objective. However, achieving this objective can be very difficult in a continuous process, especially when quality measurements are not available on-line or they have long time delays. In this paper, a control approach using multivariate statistical models is presented tc achieve this objective. The goal of the control approach is to decrease variations in product quality wihout real time quality measurements. A PCA model which incorporates time lagged variables is used, and the control objective is expressed in the score space of this PCA model. A controller is designed in the model predictive control (MPC) framework, and it is used to control the equivalent score space repres~ntation of the process. The score predictive model for the MPC algorithm is built using partial least squares (PLS). The proposed controller can be developed from and implemented on top of existing PI]) control systems, and it is demonstrated in two case studies, which involve a binary distillation column c.nd the Tennessee Eastman process.