View in EDS

Development and multi-utility of an ANN model for an industrial gas turbine

Saved in:
Bibliographic Details
Title: Development and multi-utility of an ANN model for an industrial gas turbine
Authors: Fast, M.1 magnus.fast@energy.lth.se, Assadi, M.1 mohsen.assadi@energy.lth.se, De, S.2 de_sudipta@rediffmail.com
Source: Applied Energy. Jan2009, Vol. 86 Issue 1, p9-17. 9p.
Subjects: Electric power, Electric generators, Natural gas, Turbines
Abstract: Abstract: Demonstration of different utilities for industrial use of an artificial neural network (ANN) model for a gas turbine has been reported in this paper. The ANN model was constructed with the multi-layer feed-forward network type and trained with operational data using back-propagation. The results showed that operational and performance parameters of the gas turbine, including identification of anti-icing mode, can be predicted with good accuracy for varying local ambient conditions. Different possible applications of this ANN model were also demonstrated. These include instantaneous gas turbine performance estimation through a graphical user interface and extrapolation beyond the range of training data. [Copyright &y& Elsevier]
Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 34092948
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Development and multi-utility of an ANN model for an industrial gas turbine
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Fast%2C+M%2E%22">Fast, M.</searchLink><relatesTo>1</relatesTo><i> magnus.fast@energy.lth.se</i><br /><searchLink fieldCode="AR" term="%22Assadi%2C+M%2E%22">Assadi, M.</searchLink><relatesTo>1</relatesTo><i> mohsen.assadi@energy.lth.se</i><br /><searchLink fieldCode="AR" term="%22De%2C+S%2E%22">De, S.</searchLink><relatesTo>2</relatesTo><i> de_sudipta@rediffmail.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Applied+Energy%22">Applied Energy</searchLink>. Jan2009, Vol. 86 Issue 1, p9-17. 9p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Electric+power%22">Electric power</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+generators%22">Electric generators</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+gas%22">Natural gas</searchLink><br /><searchLink fieldCode="DE" term="%22Turbines%22">Turbines</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Abstract: Demonstration of different utilities for industrial use of an artificial neural network (ANN) model for a gas turbine has been reported in this paper. The ANN model was constructed with the multi-layer feed-forward network type and trained with operational data using back-propagation. The results showed that operational and performance parameters of the gas turbine, including identification of anti-icing mode, can be predicted with good accuracy for varying local ambient conditions. Different possible applications of this ANN model were also demonstrated. These include instantaneous gas turbine performance estimation through a graphical user interface and extrapolation beyond the range of training data. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=34092948
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.apenergy.2008.03.018
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 9
    Subjects:
      – SubjectFull: Electric power
        Type: general
      – SubjectFull: Electric generators
        Type: general
      – SubjectFull: Natural gas
        Type: general
      – SubjectFull: Turbines
        Type: general
    Titles:
      – TitleFull: Development and multi-utility of an ANN model for an industrial gas turbine
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Fast, M.
      – PersonEntity:
          Name:
            NameFull: Assadi, M.
      – PersonEntity:
          Name:
            NameFull: De, S.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Text: Jan2009
              Type: published
              Y: 2009
          Identifiers:
            – Type: issn-print
              Value: 03062619
          Numbering:
            – Type: volume
              Value: 86
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Applied Energy
              Type: main
ResultId 1