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

Due to the lack of on-line sensors many important quality variables in pulp and paper manufacture are only measured infrequently and off-line in a quality control laboratory. Hence, there is a great incentive to build inferential models from plant data that are capable of predicting these quality variables on a more frequent basis. Such models can be used to monitor the process operation or, with suitable precautions, to build inferential controllers for these variables. In certain situations these models can also be used to improve our understanding of the effect of various process variables. Although the preferable way to collect process data for building such models is through statistically designed plant experiments, normal process operating records provide a good historical data base. In this paper we investigate the use of artificial neural networks and partial least squares regression to build empirical models for Kappa number using historical data from a 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.