Veröffentlicht: von

The operation of nuclear power plants (NPPs) is one of the most safety-critical tasks in industry. Prior to using AI methods in this area, it should be thoroughly investigated and evaluated via simulations, whether AI can learn (e.g.´, by reinforcment learning, RL) to power up and shut down a nuclear reactor and how well such an approach meets the safety requirements.  This was exactly the task of Niklas Fabig's master thesis which he conducted under the supervision of Prof. Dr. Wolfgang Konen and PhD-candidate Raphael Engelhardt as part of our (RL)^3-project as part of The works uses as a starting point a Java-based NPP simulation tool from Prof. Dr. Benjamin Weyers, University Trier (screenshot example in image). Niklas Fabig constructed first a Java-Python bridge and then conducted over 2000 RL simulation experiments under various settings. He could show that RL algorithms can learn the power-up procedure yielding high returns, but much more research is needed to reliably meet the safety requirements.

The investigation carried out by Niklas Fabig constitutes very interesting and brand-new research in this field, which has now led to winning the 3rd place in the Steinmüller Engineering Award 2023. His supervisor Wolfgang Konen was deeply impressed by the solid, comprehensive and innovative work done by Niklas Fabig and congratulates him warmly. It should be noted, that the master thesis was conducted in the Corona years 2021 - 2022 and so the supervision had to be fully online. Nevertheless, the result of the work and the motivation of Niklas Fabig was by no means less than if the supervision had taken place in presence.