AI-Driven approach for enhancing nuclear reactor safety predictive anomaly detection and risk assessment - Qureshi Sethu Russell; Nichols Peng Linzi (Institute of Computer Science (IOCS), 2023)
Nuclear power plays a vital role in meeting global energy demands, but ensuring the safety of nuclear reactors remains a paramount challenge. In recent years, the emergence of artificial intelligence (AI) technologies has opened new avenues to significantly enhance nuclear reactor safety through predictive anomaly detection and risk assessment. This research proposes an innovative AI-driven approach that integrates machine learning techniques and data analytics to monitor, detect, and assess potential anomalies in nuclear reactors. The research begins with a comprehensive literature review on nuclear reactor safety and the application of AI in various industrial domains, emphasizing predictive maintenance and anomaly detection. It highlights the need for an AI-driven approach to enhance nuclear reactor safety proactively. In conclusion, this research establishes the transformative potential of AI in enhancing nuclear reactor safety. The proposed AI-driven approach empowers operators with powerful tools to ensure the safe and efficient operation of nuclear power plants. As AI technologies continue to advance, the research opens doors for further exploration and development, paving the way for a more sustainable and secure future in nuclear energy production.