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On Energy Optimization of a Pulp and Paper Refiner based on Model Predictive Control
University of Patras.
Department of Electrical and Computer Science, University of Patras.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Department of Electrical and Computer Engineering, University of Patras.
2015 (English)Conference paper, Presentation (Refereed)
Abstract [en]

The aim of this article is to investigate and examine the modeling and control problem of the papermaking’s sub-process also known as pulp refining. The existing modeling approaches developed for the pulp and paper refining process are being investigated, while the most important model approximations, extracted from the existing related literature, are being simulated and examined in detail. The main goal of the article is to determine whether the modeling approaches of the pulp and paper refining process can be successfully controlled via a Model Predictive Control (MPC) based structure and at which extend this could lead in a better control scheme and in an overall energy optimization. Extensive simulation trials are being carried out, where the MPC parameters are being fine-tuned through trial-and-error sequences in order for examining the overall performance in controlling the various modeling approaches of the process. In order to further evaluate the efficacy of the proposed control scheme, the MPC related results are being compared to experimental data extracted from a real refining system that utilized a generic industrial controller.

Place, publisher, year, edition, pages
Keyword [en]
Information technology - Automatic control
Keyword [sv]
Informationsteknik - Reglerteknik
Research subject
Control Engineering
URN: urn:nbn:se:ltu:diva-40645Local ID: fd6c1822-c5a1-476f-b362-c06d8cc09dbbOAI: diva2:1014166
Mediterranean Conference on Control and Automation : 16/06/2015 - 19/06/2015
Godkänd; 2015; 20150419 (geonik)Available from: 2016-10-03 Created: 2016-10-03Bibliographically approved

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