Reducing batch-to-batch variability of pulp quality through model-based estimation

Abstract

The objective of this work is to reduce batch-to-batch variations of kappa number in batch pulping plants. We propose a model-based estimation approach that combines a nonlinear process model with on-line liquor measurements for estimation of key pulping states in the face of unknown feedstock variations. We show what measurements are needed and how the estimation problem must be formulated in order to achieve sufficiently fast recovery from the initial state/parameter errors. Simulation results indicate that, with the proposed estimator, target kappa numbers can indeed be met very closely and significant reduction in the batch-to-batch variability can be achieved.