REDUCING BATCH-TO-BATCH VARIABILITY OF PULP QUALITY THROUGH MODEL-BASED ESTIMATION

The objective of this work is to reduce batch-to-batch variations of k appa number in batch pulping plants. We propose a model-based estimati on approach that combines a nonlinear process model with on-line liquo r measurements for estimation of key pulping states in the face of unk nown feedstock variations. We show what measurements are needed and ho w the estimation problem must be formulated in order to achieve suffic iently fast recovery from the initial state/parameter errors. Simulati on results indicate that, with the proposed estimator, target kappa nu mbers can indeed be met very closely and significant reduction in the batch-to-batch variability can be achieved.