Applying the Computational Intelligence Paradigm to Nuclear Power Plant Operation

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.

Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks

“…Computational intelligence techniques have been broadly used in NPP operation for many years [ 3 ]. For example, artificial neural networks (ANNs) were tested successfully in locating a control rod perturbation from in-core self powered neutron detector (SPND) spectra recorded at Paks-2, a commercial VVER-type pressurized water reactor (PWR) in Hungary [ 4 , 5 ].…”

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

“…The issue ought to be handled with guarantee in both physical systems and cyber systems, indicating the bare fact that NPPs need more reliable physical sensors/processors and more intelligent cyber systems. In recent years, AI and big data have gradually been applied to the operation and management of nuclear power [48]. Traditional decision-making systems and data intelligence functions are based on static models and algorithms.…”

A Study on the Minimum Requirements for the On-Line, Efficient and Robust Validation of Neutron Detector Operation and Monitoring of Neutron Noise Signals using Harmony Theory Networks

“…Essential information on instrument calibration and verification, as well as on instrumentation/equipment/plant condition monitoring, can be found in [4][5][6][7][8][9]. Additionally, two relevantcomplementary to one another, yet each comprehensive in its own focus and domain of interestreviews of the literature on human/operational and computational intelligence (CI)based OLM appear in [10] and [11], respectively.…”