Abstract
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-missing-information, 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.
Highlights
Nuclear energy (NE, Weinberg, 1994) amounts to the energy that is required in order for an atom to retain its stability, i.e. for the protons and neutrons that comprise the nucleus of the atom to remain bound to each other
The present review is focused upon the application of computational intelligence (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
Annals of Nuclear Energy (ANE) constitutes the preferred journal for artificial neural networks (ANNs)-related research in control, with both Progress in Nuclear Energy (PNE) and American Nuclear Society (ANS) being chosen for fuzzy logic (FL)-based implementations and applications; still, PNE remains - by far - the preferred means of publication for GA-based solutions to N(P)P control issues
Summary
Nuclear energy (NE, Weinberg, 1994) amounts to the energy that is required in order for an atom to retain its stability, i.e. for the protons and neutrons that comprise the nucleus of the atom to remain bound to each other. For the last 25 years, the resulting CI-based implementations and applications have afforded prompt, reliable as well as robust response to these key-issues, demonstrating the potential of CI either as a superior stand-alone tool, or - whenever advantageous - in combination with traditional signal and/or image processing/analysis techniques. Despite the superior performance (solutions and implementations) offered by CI-based implementations over more conventional signal processing methodologies (which has more often than not been demonstrated on real data) for the specific period, the resulting CI-based implementations are – as a rule - not translated into stand-alone practical/commercial tools. N(P)Ps produce around 15% percent of the world’s electricity, significantly surpassing the alternative - conventional (fossil fuels such as petroleum, coal and gas) as well as renewable (solar, wind, wave etc.) - means of energy generation, while further demonstrating significant advantages in terms of operational cost, efficiency and cleanliness. N(P)P monitoring and scheduling must be vigilantly managed, while – even more importantly - nuclear fuel (both operational and exhausted) must be carefully handled and guarded as it remains radioactive for long and may seriously damage the flora, the fauna and the environment if released or mishandled
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