A Survey of Artificial Intelligence Applications in Nuclear Power Plants

Abstract

Nuclear power plants (NPPs) rely on critical, complex systems that require continuous monitoring to ensure safe operation under both normal and abnormal conditions. Despite the potential of artificial intelligence (AI) to enhance predictive capabilities in these systems, limited research has been conducted on the application of AI algorithms within NPPs. This presents a knowledge gap in the integration of AI for improving safety, reliability, and decision making in NPP. In this study, we explore the use of AI methods, including machine learning and real-time data analytics, applied to NPP components to address the nonlinearity and dynamic behavior inherent in reactor operations. Through the implementation of AI and Internet of Things (IoT) devices, we propose a system that enables early warning and real-time data transmission to regulatory authorities and decision-makers, ensuring better coordination during incidents. Lessons from past nuclear accidents, such as Chernobyl, emphasize the importance of timely information dissemination to mitigate risks. However, this integration also presents challenges, including cybersecurity risks and the need for updated regulations to address AI use in safety-critical environments. The results of this study highlight the urgent need for further research on the application of AI in NPPs, with a particular focus on addressing these challenges to ensure safe implementation.