Journal article Open Access
Tatiana Tambouratzis T., Giannatsis J., Kyriazis A., and Siotropos P.
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <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"><p>In the guise of artificial neural networks (ANNs), genetic/evolutionary computation algorithms<br> (GAs/ECAs), fuzzy logic (FL) inference systems (FLIS) and their variants as well as combinations,<br> the computational intelligence (CI) paradigm has been applied to nuclear energy (NE) since the<br> late 1980s as a set of efficient and accurate, non-parametric, robust-to-noise as well as to-missinginformation,<br> non-invasive on-line tools for monitoring, predicting and overall controlling nuclear<br> (power) plant (N(P)P) operation. Since then, the resulting CI-based implementations have afforded<br> increasingly reliable as well as robust performance, demonstrating their potential as either stand-alone<br> tools, or - whenever more advantageous - combined with each other as well as with traditional signal<br> processing techniques. The present review is focused upon the application of CI methodologies to<br> the - generally acknowledged as - key-issues of N(P)P operation, namely: control, diagnostics and<br> fault detection, monitoring, N(P)P operations, proliferation and resistance applications, sensor and<br> component reliability, spectroscopy, fusion supporting operations, as these have been reported in the<br> relevant primary literature for the period 1990-2015. At one end, 1990 constitutes the beginning of<br> the actual implementation of innovative, and &ndash; at the same time &ndash; robust as well as practical, directly<br> implementable in H/W, CI-based solutions/tools which have proved to be significantly superior to<br> the traditional as well as the artificial-intelligence-(AI)derived methodologies in terms of operation<br> efficiency as well as robustness-to-noise and/or otherwise distorted/missing information. At the<br> other end, 2015 marks a paradigm shift in terms of the emergent (and, swiftly, ubiquitous) use of<br> deep neural networks (DNNs) over existing ANN architectures and FL problem representations, thus<br> dovetailing the increasing requirements of the era of complex - as well as Big - Data and forever<br> changing the means of ANN/neuro-fuzzy construction and application/performance. By exposing<br> the prevalent CI-based tools for each key-issue of N(P)P operation, overall as well as over time for<br> the given 1990-2015 period, the applicability and optimal use of CI tools to NE problems is revealed,<br> thus providing the necessary know-how concerning crucial decisions that need to be made for the<br> increasingly efficient as well as safe exploitation of NE.</p></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> </resource>