Using RBF neural networks and a fuzzy logic controller to stabilize wood pulp freeness

The quality of paper produced in a papermaking process is largely dependent on the properties of the wood pulp used. One important property is pulp freeness. Ideally, a constant, predetermined level of freeness is desired to achieve the highest quality of paper possible. The focus of this paper is on developing a system to control the wood pulp freeness. A radial basis function (RBF) artificial neural network was used to model the freeness and a fuzzy logic controller was used to control the input parameters to maintain a desired level of freeness. Ideally, the controller will reduce pulp freeness fluctuations in order to improve overall paper sheet quality and production.