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Tools for Reconciliation of Measurement Data for Processes at Steady-State
Mälardalen University, Department of Public Technology.
2004 (English)Licentiate thesis, comprehensive summary (Other scientific)
Place, publisher, year, edition, pages
Institutionen för Samhällsteknik , 2004. , p. 56
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 34
Keywords [en]
Technology
Keywords [sv]
TEKNIKVETENSKAP
National Category
Engineering and Technology
Research subject
Energi- och miljöteknik
Identifiers
URN: urn:nbn:se:mdh:diva-13ISBN: 91-88834-60-3 (print)OAI: oai:DiVA.org:mdh-13DiVA, id: diva2:120488
Presentation
2004-09-27, Zeta, Västerås, 10:00
Available from: 2005-01-01 Created: 2005-01-01
List of papers
1. Data Reconciliation and Gross Error Detaction for Flue Gas Train in Heat and Power Plant, USA
Open this publication in new window or tab >>Data Reconciliation and Gross Error Detaction for Flue Gas Train in Heat and Power Plant, USA
(English)Manuscript (Other academic)
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-3984 (URN)
Available from: 2005-01-01 Created: 2005-01-01 Last updated: 2017-04-05Bibliographically approved
2. Bayesian Network for Decision Support on Soot Blowing Superheaters in a Biomass Fuelled Boiler
Open this publication in new window or tab >>Bayesian Network for Decision Support on Soot Blowing Superheaters in a Biomass Fuelled Boiler
2004 (English)In: 2004 International Conference on Probabilistic Methods Applied to Power Systems, 2004, p. 212-217Conference paper, Published paper (Other academic)
Abstract [en]

In a process for combined heat and power generation there is a need for fault detection, decision support and risk assessment to prevent operational disturbances and reduction in performance. A method to achieve decision support is to use Bayesian networks, where knowledge about the process is combined with operational experience. The network covers the convectional superheaters in the flue gas train, which is a major problem domain in biomass-fuelled boilers. The superheaters are exposed to fouling from flue gases. Fouling reduces the heat transfer and result in a decreased power plant performance. The Bayesian network is constructed to give decision support on preventive action to reduce abnormal fouling. Validation of the Bayesian network show that the prediction of hard fouling works well under uncertainty.

National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-3985 (URN)2-s2.0-14544274479 (Scopus ID)9780976131915 (ISBN)
Conference
2004 International Conference on Probabilistic Methods Applied to Power Systems; Ames, IA; United States; 12 September 2004 through 16 September 2004
Available from: 2005-01-01 Created: 2005-01-01 Last updated: 2017-03-02Bibliographically approved
3. Gross Error Isolation by Optimisation with Penalty Function
Open this publication in new window or tab >>Gross Error Isolation by Optimisation with Penalty Function
(English)In: Computers and Chemical EngineeringArticle in journal (Refereed) Submitted
National Category
Natural Sciences
Identifiers
urn:nbn:se:mdh:diva-3986 (URN)
Available from: 2005-01-01 Created: 2005-01-01 Last updated: 2014-01-10Bibliographically approved
4. Process and sensor diagnostics: Data reconciliation for a flue gas channel
Open this publication in new window or tab >>Process and sensor diagnostics: Data reconciliation for a flue gas channel
2003 (Swedish)Report (Other academic)
Place, publisher, year, edition, pages
Värmeforsk Service AB, 2003
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-3987 (URN)
Available from: 2005-01-01 Created: 2005-01-01 Last updated: 2015-07-29Bibliographically approved

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Citation style
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  • ieee
  • modern-language-association-8th-edition
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  • Other style
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  • de-DE
  • en-GB
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  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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