A fuzzy approach to using expert knowledge for tuning paper machines


Paper machines are very complex production systems, but their scope is simple: they consume materials and resources, called factors, to produce paper, which in turn can be described by its characteristics. In this paper, a decision support system is developed in cooperation with an industrial partner to help them with operational decision making when tuning a paper machine. The decision support system was developed in two phases. Firstly, the knowledge of experts is collected and stored in the form of a fuzzy ontology. Secondly, this knowledge is made usable so that a user of the decision support system can specify what characteristics of the produced paper to increase or to decrease and be returned with a recommendation on what factors to change. In this paper, we will work out the optimization problems on which the system is based. Additionally to a basic goal programming model, two extensions are explored, accounting for uncertainty and non-linearity, respectively.

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Corresponding author

Correspondence to József Mezei.

Additional information

A preliminary version of this paper was presented at the 46th Hawaii International Conference on Systems Sciences: Carlsson C., Brunelli M. and Mezei J. (2013), A soft computing approach to mastering paper machines, HICSS 2013, pp. 1394–1401.

Appendix: List of factors and characteristics

Appendix: List of factors and characteristics

The following are the lists of factors and characteristics used in Figure 1:

  • Factors: Deinked pulp (DIP), Thermomechanical pulp (TMP), Pressurized groundwood pulp (PGW), Clay (kaolin), Calcium carbonate, CaCO3, Talc, Refining, DIP refining, Absorbency, Flow rate, Water in blade section, Retention, Loading of nip, Steam amount, Press draw, Initial dryness, Initial drying rate, Hood covers, Soft nips, Stack nip loadings, Steam amount in calendering, Temperature in calendering, Reeling load, Winding

  • Characteristics: Runnability, Energy, Tensile, Permeability, Scott bond, Formation, Bulk, Smoothness, Brightness, Opacity, Density, Gloss, Absorption, Two sided, Dimensional stability, Friction, Linting

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Mezei, J., Brunelli, M. & Carlsson, C. A fuzzy approach to using expert knowledge for tuning paper machines. J Oper Res Soc 68, 605–616 (2017). https://doi.org/10.1057/s41274-016-0105-3

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  • paper machines
  • fuzzy ontology
  • multiobjective optimization
  • goal programming
  • possibilistic chance programming
  • soft computing