1384345

11608

In this paper, a data-based control method for reducing product quality variations in batch pulp digesters is presented. Com- pared to the existing techniques, the new technique uses more liquor measurements in predicting the ®nal pulp quality. The liquor measurements obtained at di€erent time instances during a cook are related to the ®nal pulp quality through a partial least squares (PLS) regression model. In using the PLS regression model for control, two approaches are proposed. In the ®rst approach, optimal control moves are computed directly using the PLS model, while the second approach employs a nonlinear H-factor model of which parameters are adapted using the prediction from the PLS model. The e€ectiveness of the prediction and control algorithms is examined through simulation studies. Experimental study is then performed on a lab-scale batch digester, to test the e€ectiveness of the prediction performance of the PLS model. The control algorithms will be tested on the experimental set-up in the future.

229

Quality prediction and control , Pulp digester , partial least squares

Studia Iranica

236

236