5 papers
Accelerating Nonlinear MPC by Behavior Cloning with Separated Timescales: Application to Nuclear Reactor Load-Following
Perceval Beja-Battais, Alain Grosset{ê}te, Nicolas Vayatis
Behavior Cloning (BC) of expert controllers for multiscale dynamical systems is challenging: state variables evolving at widely separated timescales induce highly nonlinear couplin…
Structured Representation Learning for Behavior Cloning: How can we learn to safely control a nuclear power plant?
Perceval Beja-Battais, Alain Grosset{ê}te, Nicolas Vayatis
Learned models for industrial control are usually judged by aggregate accuracy, but accuracy at the component level does not guarantee safety once it is embedded in the system it i…
The OAPS solution: a real-time predictive system for flexible PWR operation
Guillaume Dupré, Alain Grossetête
This paper presents an innovative solution designed to facilitate safe and flexible operation of nuclear power plants. The purpose of this new device, named OAPS system, is to prov…
Accelerating Full-Scale Nonlinear Model Predictive Control via Surrogate Dynamics Optimization
Perceval Beja-Battais, Guillaume Dupré, Alain Grossetête +1
Driven by advances in hardware and software technologies, nonlinear model predictive control (NMPC) has gained increasing adoption in both industry and academia over the past decad…
Enhancing Nuclear Reactor Core Simulation through Data-Based Surrogate Models
Perceval Beja-Battais, Alain Grossetête, Nicolas Vayatis
In recent years, there has been an increasing need for Nuclear Power Plants (NPPs) to improve flexibility in order to match the rapid growth of renewable energies. The Operator Ass…