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Diagnostics-Oriented Modelling of Micro Gas Turbines for Fleet Monitoring and Maintenance Optimization
Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. (SOFIA)ORCID-id: 0000-0003-3610-4680
Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. (SOFIA)ORCID-id: 0000-0001-6101-2863
Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. (SOFIA)
Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. (SOFIA)ORCID-id: 0000-0002-8466-356X
2018 (engelsk)Inngår i: Processes, ISSN 2227-9717, E-ISSN 1099-5862, Vol. 6, nr 11Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The market for the small-scale micro gas turbine is expected to grow rapidly in the coming years. Especially, utilization of commercial off-the-shelf components is rapidly reducing the cost of ownership and maintenance, which is paving the way for vast adoption of such units. However, to meet the high-reliability requirements of power generators, there is an acute need of a real-time monitoring system that will be able to detect faults and performance degradation, and thus allow preventive maintenance of these units to decrease downtime. In this paper, a micro gas turbine based combined heat and power system is modelled and used for development of physics-based diagnostic approaches. Different diagnostic schemes for performance monitoring of micro gas turbines are investigated.

sted, utgiver, år, opplag, sider
2018. Vol. 6, nr 11
Emneord [en]
micro gas turbine; modelling; diagnostics, gas path analysis, analysis by synthesis
HSV kategori
Forskningsprogram
energi- och miljöteknik
Identifikatorer
URN: urn:nbn:se:mdh:diva-41514DOI: 10.3390/pr6110216ISI: 000451530400012Scopus ID: 2-s2.0-85057867402OAI: oai:DiVA.org:mdh-41514DiVA, id: diva2:1268726
Prosjekter
FUDIPOTilgjengelig fra: 2018-12-06 Laget: 2018-12-06 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: 2019-09-16bibliografisk kontrollert

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