Implementing Machine Learning Algorithms to Enhance Safety Systems in Nuclear Power Plants

1. Introduction 1.1 Problem Statement 1.2 Objectives of the Study 1.3 Research Methodology 1.4 Structure of the Thesis 2. Background on Nuclear Safety Systems 2.1 Overview of Current Safety Systems 2.2 Historical Incidents and Lessons Learned 2.3 Regulatory Frameworks in Nuclear Safety 3. Fundamentals of Machine Learning 3.1 Definition and Scope of Machine Learning 3.2 Types of Machine Learning Algorithms 3.3 Applications in Various Sectors 4. Integration of Machine Learning in Safety Systems 4.1 Feasibility and Requirements 4.2 Challenges and Limitations 4.3 Case Studies of Initial Implementations 5. Machine Learning Algorithms for Nuclear Safety 5.1 Supervised Learning Applications 5.2 Unsupervised Learning Techniques 5.3 Reinforcement Learning for Adaptive Systems 6. Algorithm Evaluation and Optimization 6.1 Criteria for Algorithm Selection 6.2 Performance Metrics in Safety Contexts 6.3 Techniques for Algorithm Enhancement 7. Case Study: Simulation and Results 7.1 Setup of Model Environment 7.2 Analysis of Simulation Data 7.3 Implications for Safety Improvements 8. Conclusion and Future Work 8.1 Summary of Key Findings 8.2 Recommendations for Implementation 8.3 Potential Directions for Future Research