Numerical Demonstration of Multiple Actuator Constraint Enforcement Algorithm for a Molten Salt Loop

TL;DR

This paper presents a data-driven, interpretable control algorithm for a molten salt loop, capable of enforcing actuator constraints during transient operations, advancing autonomous nuclear plant control.

Contribution

It introduces an interpretable, adaptable control method that enforces actuator constraints in a molten salt loop, demonstrating its effectiveness through numerical experiments.

Findings

Successful enforcement of actuator constraints during load-follow transients

Demonstrated adaptability of control algorithm to changing conditions

Validated approach through numerical simulation

Abstract

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.