Reliability and Safety Assessment of Isolation Condenser System in BWRX-300

Abstract

The BWRX-300, an innovative Small Modular Reactor (SMR), leverages over 60 years of experience in boiling water reactor technology. As the 10th generation, it evolves from the Economic Simplified Boiling Water Reactor (ESBWR) to reduce capital costs while ensuring high safety. Its versatile design supports various applications, including electricity and hydrogen production, emphasizing efficiency and fewer staffing needs. A hallmark of the BWRX-300 is its utilization of Passive Safety Systems (PSSs) that leverage natural forces for safety measures, eliminating the dependence on active systems and manual interventions. Despite the proven effectiveness of PSSs, their reliability assessment poses challenges due to the lack of operational data and complexity of their functions. This research introduces an Artificial Neural Network (ANN) based methodology for evaluating the reliability of such systems, specifically focusing on the BWRX-300's Isolation Condenser Systems (ICS). By employing ANN alongside traditional Probabilistic Safety Assessment (PSA) techniques, this study aims to refine the reliability and safety assessments of PSS in SMRs, enhancing the safety evaluations and supporting the sustainable and secure advancement of nuclear energy technology. The integration of ANN model demonstrates the potential of artificial intelligence tools in advancing nuclear safety assessments, thereby contributing to the global efforts towards safer and more sustainable nuclear energy solutions.