Application of feedforward neural networks and partial least squares regression for modelling Kappa number in a continuous Kamyr digester

In this paper we investigate the use of neural networks and the partial least squares regression method to build empirical models for Kappa number using historical data from an industrial, continuous Kamyr digester. The basic ideas behind the two approaches will be presented and their advantages and disadvantages discussed. The predictive abilities of the resulting models and their limitations are evaluated using additional data from the digester and compared to other modelling approaches.