Prodiag--a hybrid artificial intelligence based reactor diagnostic system for process faults

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Commonwealth Research Corporation (CRC) and Argonne National Laboratory (ANL) are collaborating on a DOE-sponsored Cooperative Research and Development Agreement (CRADA), project to perform feasibility studies on a novel approach to Artificial Intelligence (Al) based diagnostics for component faults in nuclear power plants. Investigations are being performed in the construction of a first-principles physics-based plant level process diagnostic expert system (ES) and the identification of component-level fault patterns through operating component characteristics using artificial neural networks (ANNs). The purpose of the proof-of-concept project is to develop a computer-based system using this Al approach to assist process plant operators during off-normal plant ... continued below

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9 p.

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Reifman, J.; Wei, T.Y.C.; Vitela, J.E.; Applequist, C. A. & Chasensky, T.M. March 1, 1996.

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Description

Commonwealth Research Corporation (CRC) and Argonne National Laboratory (ANL) are collaborating on a DOE-sponsored Cooperative Research and Development Agreement (CRADA), project to perform feasibility studies on a novel approach to Artificial Intelligence (Al) based diagnostics for component faults in nuclear power plants. Investigations are being performed in the construction of a first-principles physics-based plant level process diagnostic expert system (ES) and the identification of component-level fault patterns through operating component characteristics using artificial neural networks (ANNs). The purpose of the proof-of-concept project is to develop a computer-based system using this Al approach to assist process plant operators during off-normal plant conditions. The proposed computer-based system will use thermal hydraulic (T-H) signals complemented by other non-T-H signals available in the data stream to provide the process operator with the component which most likely caused the observed process disturbance.To demonstrate the scale-up feasibility of the proposed diagnostic system it is being developed for use with the Chemical Volume Control System (CVCS) of a nuclear power plant. A full-scope operator training simulator representing the Commonwealth Edison Braidwood nuclear power plant is being used both as the source of development data and as the means to evaluate the advantages of the proposed diagnostic system. This is an ongoing multi-year project and this paper presents the results to date of the CRADA phase.

Physical Description

9 p.

Notes

OSTI as TI96007069

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  • ICONE 4: ASME/JSME international conference on nuclear engineering, New Orleans, LA (United States), 10-13 Mar 1996

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  • Other: TI96007069
  • Report No.: ANL/RE/CP--86859
  • Report No.: CONF-960306--23
  • Grant Number: W-31109-ENG-38
  • DOI: 10.2172/224950 | External Link
  • Office of Scientific & Technical Information Report Number: 224950
  • Archival Resource Key: ark:/67531/metadc666580

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Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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Creation Date

  • March 1, 1996

Added to The UNT Digital Library

  • June 29, 2015, 9:42 p.m.

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  • Dec. 15, 2015, 6:45 p.m.

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Reifman, J.; Wei, T.Y.C.; Vitela, J.E.; Applequist, C. A. & Chasensky, T.M. Prodiag--a hybrid artificial intelligence based reactor diagnostic system for process faults, report, March 1, 1996; Illinois. (digital.library.unt.edu/ark:/67531/metadc666580/: accessed September 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.