Reactor Monitoring: Powerful AI Catches Hidden Blockages
Argonne National Laboratory researchers have published a design that pairs distributed fibre-optic temperature sensing with explainable machine learning to detect coolant channels freezing shut inside a molten-salt reactor heat exchanger, while inlet and outlet readings still look normal. Eight classifiers were benchmarked on 25,704 simulated instances; XGBoost won with 36 missed plugs and an F1 of 0.96 at 60% flow reduction, falling to 0.51 at 20%. This article covers the sensor design, the class imbalance, the SHAP-plus-POSET explainability layer and the four caveats that stand between it and a licensed product.
