Journal article Open Access

Applying the Computational Intelligence Paradigm to Nuclear Power Plant Operation: A Review (1990-2015).

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

In the guise of artificial neural networks (ANNs), genetic/evolutionary computation algorithms
(GAs/ECAs), fuzzy logic (FL) inference systems (FLIS) and their variants as well as combinations,
the computational intelligence (CI) paradigm has been applied to nuclear energy (NE) since the
late 1980s as a set of efficient and accurate, non-parametric, robust-to-noise as well as to-missinginformation,
non-invasive on-line tools for monitoring, predicting and overall controlling nuclear
(power) plant (N(P)P) operation. Since then, the resulting CI-based implementations have afforded
increasingly reliable as well as robust performance, demonstrating their potential as either stand-alone
tools, or - whenever more advantageous - combined with each other as well as with traditional signal
processing techniques. The present review is focused upon the application of CI methodologies to
the - generally acknowledged as - key-issues of N(P)P operation, namely: control, diagnostics and
fault detection, monitoring, N(P)P operations, proliferation and resistance applications, sensor and
component reliability, spectroscopy, fusion supporting operations, as these have been reported in the
relevant primary literature for the period 1990-2015. At one end, 1990 constitutes the beginning of
the actual implementation of innovative, and – at the same time – robust as well as practical, directly
implementable in H/W, CI-based solutions/tools which have proved to be significantly superior to
the traditional as well as the artificial-intelligence-(AI)derived methodologies in terms of operation
efficiency as well as robustness-to-noise and/or otherwise distorted/missing information. At the
other end, 2015 marks a paradigm shift in terms of the emergent (and, swiftly, ubiquitous) use of
deep neural networks (DNNs) over existing ANN architectures and FL problem representations, thus
dovetailing the increasing requirements of the era of complex - as well as Big - Data and forever
changing the means of ANN/neuro-fuzzy construction and application/performance. By exposing
the prevalent CI-based tools for each key-issue of N(P)P operation, overall as well as over time for
the given 1990-2015 period, the applicability and optimal use of CI tools to NE problems is revealed,
thus providing the necessary know-how concerning crucial decisions that need to be made for the
increasingly efficient as well as safe exploitation of NE.

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