Large-scale dynamic paper machine models

J. Balderud, C. Haag, D. Wilson

Research output: Contribution to conferencePaper


Optimising industrial operations, building soft-sensors and model-based controllers and even fine-tuning controllers is much easier with a good plant model. Karlstad University in collaboration with Stora Enso have initiated a project to develop a series of dynamic models for two 5-ply paperboard machines at Skoghall, Sweden. One aim is to better predict paper properties which requires modelling the entire machine, the other is to model only the short circulation with the intention of closer control of fines and faster grade changes. Because the full machine models quickly become unwieldy, smaller ‘mini-models’ were constructed for control-type investigations. To reduce the computation burden further, analytical solutions for the pressure-flow balance are proposed. All models were validated against almost one year of plant operating data. This paper highlights the difficulties in modelling a large industrial system with widely varying time constants, dubious transducers and the ubiquitous noise. We also contrast different simulation tools used at the various levels of model hierarchy and finally the paper demonstrates the use of simplified models to improve the operation of the board production.
Original languageEnglish
Number of pages13
Publication statusPublished - 2001


  • industrial operations
  • soft-sensors
  • model-based controllers
  • large-scale
  • dynamic
  • paper machine models

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