Probability theory and mathematical statistics

Economics and finance of Molten Salt Reactors

The power start-up operation of a nuclear power plant (NPP) increases the reactor power to the full-power condition for electricity generation. Compared to full-power operation, the power-increase operation requires significantly more decision-making and therefore increases the potential for human errors. While previous studies have investigated the use of artificial intelligence (AI) techniques for NPP control, none of them have addressed making the relatively complicated power-increase operation fully autonomous. This study focused on developing an algorithm for converting all the currently manual activities in the NPP power-increase process to autonomous operations. An asynchronous advantage actor-critic, which is a type of deep reinforcement learning method, and a long short-term memory network were applied to the operator tasks for which establishing clear rules or logic was challenging, while a rule-based system was developed for those actions, which could be described by simple logic (such as if-then logic). The proposed autonomous power-increase control algorithm was trained and validated using a compact nuclear simulator (CNS). The simulation results were used to evaluate the algorithm’s ability to control the parameters within allowable limits, and the proposed power-increase control algorithm was proven capable of identifying an acceptable operation path for increasing the reactor power from 2% to 100% at a specified rate of power increase. In addition, the pattern of operation that resulted from the autonomous control simulation was found to be identical to that of the established operation strategy. These results demonstrate the potential feasibility of fully autonomous control of the NPP power-increase operation.

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Algorithm for Autonomous Power-Increase Operation Using Deep Reinforcement Learning and a Rule-Based System

Core power control of a space nuclear reactor based on a nonlinear model and fuzzy-PID controller

Wenjie Zeng

Papers

An IMC-PID controller with Particle Swarm Optimization algorithm for MSBR core power control

A fuzzy-PID composite controller for core power control of liquid molten salt reactor

A functional variable universe fuzzy PID controller for load following operation of PWR with the multiple model

Core power control of a space nuclear reactor based on a nonlinear model and fuzzy-PID controller

Study on switching control of PWR core power with a fuzzy multimodel