Numerical Demonstration of Multiple Actuator Constraint Enforcement Algorithm for a Molten Salt Loop
This work addresses the problem of autonomous control for next-generation nuclear reactors to improve safety and economics, but it is incremental as it builds on existing data-driven methods without addressing robustness.
The paper tackled optimal control for autonomous operation of a molten salt loop in nuclear power plants by developing an interpretable and adaptable data-driven machine learning approach, demonstrating it through a numerical experiment for constraint enforcement during a load-follow transient.
To advance the paradigm of autonomous operation for nuclear power plants, a data-driven machine learning approach to control is sought. Autonomous operation for next-generation reactor designs is anticipated to bolster safety and improve economics. However, any algorithms that are utilized need to be interpretable, adaptable, and robust. In this work, we focus on the specific problem of optimal control during autonomous operation. We will demonstrate an interpretable and adaptable data-driven machine learning approach to autonomous control of a molten salt loop. To address interpretability, we utilize a data-driven algorithm to identify system dynamics in state-space representation. To address adaptability, a control algorithm will be utilized to modify actuator setpoints while enforcing constant, and time-dependent constraints. Robustness is not addressed in this work, and is part of future work. To demonstrate the approach, we designed a numerical experiment requiring intervention to enforce constraints during a load-follow type transient.