Partial least squares (PLS) based monitoring and control of batch digesters

In this paper, a data-based control method for reducing product quality var iations in batch pulp digesters is presented. Compared to the existing tech niques, the new technique uses more liquor measurements in predicting the f inal pulp quality. The liquor measurements obtained at different time insta nces during a cook are related to the final pulp quality through a partial least squares (PLS) regression model. In using the PLS regression model for control, two approaches an proposed. In the first approach, optimal contro l moves are computed directly using the PLS model, while the second approac h employs a nonlinear H-factor model of which parameters are adapted using the prediction from the PLS model. The effectiveness of the prediction and control algorithms is examined through simulation studies. Experimental stu dy is then performed on a lab-scale batch digester, to test the effectivene ss of the prediction performance of the PLS model. The control algorithms w ill be tested on the experimental set-up in the future. (C) 2000 IFAC. Publ ished by Elsevier Science Ltd. All rights reserved.