Machine-Learning Algorithms for Remote-Control and Autonomous Operation of the Very-Small, Long-Life, Modular (VSLLIM) Microreactor

This work investigated machine-learning algorithms for remote-control and autonomous operation of the Very-Small, Long-Life, Modular (VSLLIM) microreactor. This walk-away safe reactor can continuously generate 1.0–10 MW of thermal power for 92 and 5.6 full power years, respectively, is cooled by natural circulation of in-vessel liquid sodium, does not require on-site storage of either fresh or spent nuclear fuel, and offers redundant means of control and passive decay heat removal. The two ML algorithms investigated are Supervised Learning with Long Short-Term Memory networks (SL-LSTM) and Soft-Actor Critic with Feedforward Neural Networks (SAC-FNN). They are trained to manage the movement of the control rods in the reactor core during various transients including startup, shutdown, and to change the reactor steady state power up to 10 MW. The trained algorithms are incorporated into a Programmable Logic Controller (PLC) coupled to a digital twin dynamic model of the VSLLIM microreactor. Although the SL-LSTM algorithms demonstrate high prediction accuracy of up to 99.95%, they demonstrate inferior performance when incorporated into the PLC. Conversely, the PLC with SAC-FNN algorithm accurately adjusts the control rods positions during the reactor startup transients to within ±1.6% of target values.