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An Approach For Feedforward Model Predictive Control For Pulp and Paper Applications: Challenges And The Way Forward
Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. (Simulation and Optimization for Future Industrial Applications (SOFIA))ORCID-id: 0000-0003-3610-4680
Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. (Simulation and Optimization for Future Industrial Applications (SOFIA))ORCID-id: 0000-0001-8191-4901
Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. (Simulation and Optimization for Future Industrial Applications (SOFIA))ORCID-id: 0000-0002-8466-356X
Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.ORCID-id: 0000-0002-7233-6916
2017 (engelsk)Inngår i: Paper Conference and Trade Show, PaperCon 2017: Renew, Rethink, Redefine the Future, Volume 3, TAPPI Press, 2017, Vol. 10, s. 1441-1450Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Due to the naturally varying feedstock, significant residence time, insufficient measurements and complex nature of the delignification process, producing pulp with consistent quality i.e. stable kappa number with sufficiently high yield is a challenging task that requires multi-variable process control. A wide variety of control structures, ranging from classical concepts like cascade control, feedforward, ratio control, and parallel control to more modern concepts like model-based predictive control, is used in pulp and paper industries all over the world. In this paper, a survey of model-based predictive control will be presented along with the control challenges that lie within the chemical pulping process. The potential of this control concept for overcoming the aforementioned technical challenges will also be discussed in the second part of the paper. Particular focus will be given on the use of near-infrared spectroscopy based soft-sensors coupled with dynamic process models as an enabler for feedforward model-based predictive control. Overall, the proposed control concept is expected to significantly improve process performance, in the presence of measurement noise and various complex chemical process uncertainties common in pulp and paper applications.

sted, utgiver, år, opplag, sider
TAPPI Press, 2017. Vol. 10, s. 1441-1450
Emneord [en]
Pulp and paper, Pump Digester, MPC, Advanced control, Process modelling, NIR
HSV kategori
Forskningsprogram
energi- och miljöteknik
Identifikatorer
URN: urn:nbn:se:mdh:diva-35839Scopus ID: 2-s2.0-85041499426ISBN: 9781510847286 (tryckt)OAI: oai:DiVA.org:mdh-35839DiVA, id: diva2:1111737
Konferanse
PaperCon 2017, April 23 - 26, 2017 Minneapolis, Minnesota, USA
Prosjekter
FUDIPO
Forskningsfinansiär
EU, Horizon 2020Tilgjengelig fra: 2017-06-19 Laget: 2017-06-19 Sist oppdatert: 2019-09-16bibliografisk kontrollert
Inngår i avhandling
1. Towards a learning system for process and energy industry: Enabling optimal control, diagnostics and decision support
Åpne denne publikasjonen i ny fane eller vindu >>Towards a learning system for process and energy industry: Enabling optimal control, diagnostics and decision support
2019 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Driven by intense competition, increasing operational cost and strict environmental regulations, the modern process and energy industry needs to find the best possible way to adapt to maintain profitability. Optimization of control and operation of the industrial systems is essential to satisfy the contradicting objectives of improving product quality and process efficiency while reducing production cost and plant downtime. Use of optimization not only improves the control and monitoring of assets but also offers better coordination among different assets. Thus, it can lead to considerable savings in energy and resource consumption, and consequently offer a reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks that can be integrated with the existing industrial automation platforms to benefit from optimal control and operation. The main focus of this dissertation is the use of different process models, soft sensors and optimization techniques to improve the control, diagnostics and decision support for the process and energy industry. A generic architecture for an optimal control, diagnostics and decision support system, referred to here as a learning system, is proposed. The research is centred around an investigation of different components of the proposed learning system. Two very different case studies within the energy-intensive pulp and paper industry and the promising micro-combined heat and power (CHP) industry are selected to demonstrate the learning system. One of the main challenges in this research arises from the marked differences between the case studies in terms of size, functions, quantity and structure of the existing automation systems. Typically, only a few pulp digesters are found in a Kraft pulping mill, but there may be hundreds of units in a micro-CHP fleet. The main argument behind the selection of these two case studies is that if the proposed learning system architecture can be adapted for these significantly different cases, it can be adapted for many other energy and process industrial cases. Within the scope of this thesis, mathematical modelling, model adaptation, model predictive control and diagnostics methods are studied for continuous pulp digesters, whereas mathematical modelling, model adaptation and diagnostics techniques are explored for the micro-CHP fleet.

sted, utgiver, år, opplag, sider
Västerås: Mälardalen University, 2019. s. 178
Serie
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 282
Emneord
Learning system, Supervisory system, Pulp and paper, Micro gas turbine, Process modelling, Model-based control, Diagnostics, Decision support, Anomaly detection, Fault detection
HSV kategori
Forskningsprogram
energi- och miljöteknik
Identifikatorer
urn:nbn:se:mdh:diva-45219 (URN)978-91-7485-438-1 (ISBN)
Presentation
2019-10-30, Pi, Mälardalen University, Västerås, 13:00 (engelsk)
Opponent
Veileder
Prosjekter
FUDIPO – FUture DIrections for Process industry Optimization
Forskningsfinansiär
EU, Horizon 2020, 723523
Tilgjengelig fra: 2019-09-16 Laget: 2019-09-16 Sist oppdatert: 2019-09-24bibliografisk kontrollert

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