Online prediction of pulp brightness using fuzzy logic models

Authors:
Sofiane Achiche;Luc Baron;Marek Balazinski;Mokhtar Benaoudia
Affiliations:
Department of Mechanical Engineering, ícole Polytechnique de Montréal, C.P. 6079, succ. CV, Montréal, Qué., Canada H3C 3A7;Department of Mechanical Engineering, ícole Polytechnique de Montréal, C.P. 6079, succ. CV, Montréal, Qué., Canada H3C 3A7;Department of Mechanical Engineering, ícole Polytechnique de Montréal, C.P. 6079, succ. CV, Montréal, Qué., Canada H3C 3A7;Centre de Recherche Industrielle du Québec, 333, rue Franquet Sainte-Foy, Qué., Canada G1P 4C7
Venue:
Engineering Applications of Artificial Intelligence
Year:
2007

Citing 3
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Abstract

The quality of thermomechanical pulp (TMP) is influenced by a large number of variables. To control the pulp and paper process, the operator has to manually choose the influencing variables, which can change significantly depending on the quality of the raw material (wood chips). Very little knowledge exists about the relationships between the quality of the pulp obtained by the TMP process and wood chip properties. The research proposed in this paper uses genetically generated knowledge bases to model these relationships while using measurements of wood chip quality, process parameter data and properties of raw material such as bleaching agents. The rule base of the knowledge bases will provide a better understanding of the relationships between the different influencing variables (input and outputs).