December 16, 2019 Journal article Open Access

Tatiana Tambouratzis T., Giannatsis J., Kyriazis A., and Siotropos P.

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  <identifier identifierType="URL">https://zenodo.org/record/3579177</identifier>
  <creators>
    <creator>
      <creatorName>Tatiana Tambouratzis T., Giannatsis J., Kyriazis A., and Siotropos P.</creatorName>
      <affiliation>University of Piraeus</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Applying the Computational Intelligence Paradigm to Nuclear Power Plant Operation: A Review (1990-2015).</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-12-16</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3579177</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.4018/IJEOE.2020010102</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In the guise of artificial neural networks (ANNs), genetic/evolutionary computation algorithms&lt;br&gt;
(GAs/ECAs), fuzzy logic (FL) inference systems (FLIS) and their variants as well as combinations,&lt;br&gt;
the computational intelligence (CI) paradigm has been applied to nuclear energy (NE) since the&lt;br&gt;
late 1980s as a set of efficient and accurate, non-parametric, robust-to-noise as well as to-missinginformation,&lt;br&gt;
non-invasive on-line tools for monitoring, predicting and overall controlling nuclear&lt;br&gt;
(power) plant (N(P)P) operation. Since then, the resulting CI-based implementations have afforded&lt;br&gt;
increasingly reliable as well as robust performance, demonstrating their potential as either stand-alone&lt;br&gt;
tools, or - whenever more advantageous - combined with each other as well as with traditional signal&lt;br&gt;
processing techniques. The present review is focused upon the application of CI methodologies to&lt;br&gt;
the - generally acknowledged as - key-issues of N(P)P operation, namely: control, diagnostics and&lt;br&gt;
fault detection, monitoring, N(P)P operations, proliferation and resistance applications, sensor and&lt;br&gt;
component reliability, spectroscopy, fusion supporting operations, as these have been reported in the&lt;br&gt;
relevant primary literature for the period 1990-2015. At one end, 1990 constitutes the beginning of&lt;br&gt;
the actual implementation of innovative, and &amp;ndash; at the same time &amp;ndash; robust as well as practical, directly&lt;br&gt;
implementable in H/W, CI-based solutions/tools which have proved to be significantly superior to&lt;br&gt;
the traditional as well as the artificial-intelligence-(AI)derived methodologies in terms of operation&lt;br&gt;
efficiency as well as robustness-to-noise and/or otherwise distorted/missing information. At the&lt;br&gt;
other end, 2015 marks a paradigm shift in terms of the emergent (and, swiftly, ubiquitous) use of&lt;br&gt;
deep neural networks (DNNs) over existing ANN architectures and FL problem representations, thus&lt;br&gt;
dovetailing the increasing requirements of the era of complex - as well as Big - Data and forever&lt;br&gt;
changing the means of ANN/neuro-fuzzy construction and application/performance. By exposing&lt;br&gt;
the prevalent CI-based tools for each key-issue of N(P)P operation, overall as well as over time for&lt;br&gt;
the given 1990-2015 period, the applicability and optimal use of CI tools to NE problems is revealed,&lt;br&gt;
thus providing the necessary know-how concerning crucial decisions that need to be made for the&lt;br&gt;
increasingly efficient as well as safe exploitation of NE.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/754316/">754316</awardNumber>
      <awardTitle>Core monitoring techniques and experimental validation and demonstration</awardTitle>
    </fundingReference>
  </fundingReferences>
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