Elman Network in Kappa Number Prediction

AbstractVariations in the pulp quality are mainly due to variations in the quality of wood chips used for pulping. Moisture content of chips, chip size and chip composition vary, and the measurements to reflect these variations do not always exist. Kappa number is the usual way of describing the pulp quality and it measures the residual lignin content in the pulp. In batch digesting, the main difficulty in maintaining consistent Kappa number is the lack of on-line Kappa number measurement. The most common way is to use the calculated H-factor to define the cooking end-point. Kappa number cannot be measured on-line in batch digesters, but there are many approaches to model, or predict, it from the existing measurement, and use the predicted Kappa to complement the H-factor control. Both mechanistic and data-based model are in use, but due to the complexity of fundamental models, current industrial practices rely almost exclusively on simple empirical or semi-empirical models. Several intelligent methods (neural networks and fuzzy logic) together with advanced controls have found applications also on this arena. This paper concerns with using recurrent Elman network together with alkali measurement and H-factor in Kappa number modelling.