Autonomous control model for emergency operation of small modular reactor

Abstract

Advancements in computer performance and Artificial Intelligence, especially Deep Neural Networks and reinforcement learning, have caught the attention of many researchers. Specifically, the potential for achieving autonomy (a higher cognitive concept than legacy automation systems) has garnered significant interest. Thus, many autonomous systems like self-driving cars, are in the research/ development phase. Despite its numerous advantages, the adoption of autonomous control for Nuclear Power Plants is deficient mainly due to stringent safety concerns. Especially, autonomous control for Small Modular Reactors is a desirous capability to accrue its full potential. This study proposes an AI-based model for autonomous control of IP-200 NPP, a Small Modular Reactor (SMR) under development. The model adopts a modular architecture for performing different functions of monitoring/ diagnosis, strategy formulation and assessment. Despite following knowledge-based rules, the network decisions are optimized through supervised learning and fine-tuned through reinforcement learning. The results demonstrate that the proposed model can handle plant emergencies while ensuring plant safety at optimal performance.