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Diagnostics-Oriented Modelling of Micro Gas Turbines for Fleet Monitoring and Maintenance Optimization
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. (SOFIA)ORCID iD: 0000-0003-3610-4680
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. (SOFIA)ORCID iD: 0000-0001-6101-2863
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. (SOFIA)
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. (SOFIA)ORCID iD: 0000-0002-8466-356X
2018 (English)In: Processes, ISSN 2227-9717, E-ISSN 1099-5862, Vol. 6, no 11Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2018. Vol. 6, no 11
Keywords [en]
micro gas turbine; modelling; diagnostics, gas path analysis, analysis by synthesis
National Category
Energy Systems
Research subject
Energy- and Environmental Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-41514DOI: 10.3390/pr6110216ISI: 000451530400012Scopus ID: 2-s2.0-85057867402OAI: oai:DiVA.org:mdh-41514DiVA, id: diva2:1268726
Projects
FUDIPOAvailable from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-09-16Bibliographically approved
In thesis
1. Towards a learning system for process and energy industry: Enabling optimal control, diagnostics and decision support
Open this publication in new window or tab >>Towards a learning system for process and energy industry: Enabling optimal control, diagnostics and decision support
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019. p. 178
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 282
Keywords
Learning system, Supervisory system, Pulp and paper, Micro gas turbine, Process modelling, Model-based control, Diagnostics, Decision support, Anomaly detection, Fault detection
National Category
Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-45219 (URN)978-91-7485-438-1 (ISBN)
Presentation
2019-10-30, Pi, Mälardalen University, Västerås, 13:00 (English)
Opponent
Supervisors
Projects
FUDIPO – FUture DIrections for Process industry Optimization
Funder
EU, Horizon 2020, 723523
Available from: 2019-09-16 Created: 2019-09-16 Last updated: 2019-09-16Bibliographically approved

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Zaccaria, ValentinaXin, ZhaoKyprianidis, Konstantinos

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