Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

Why keeping reactors healthy matters

Modern nuclear reactors promise cleaner power, but only if their key components stay healthy and reliable. One of the most vulnerable pieces is the heat exchanger, the metal “radiator” that moves heat from the reactor’s hot fluid into a secondary loop that eventually spins a turbine. If passages inside this device begin to clog and freeze, performance drops and safety margins can shrink—yet the early warning signs are so subtle that traditional monitoring often misses them. This article explores how a new sensor-rich heat exchanger design, paired with explainable artificial intelligence, could spot those faint danger signals in time for operators to act.

A new kind of nuclear radiator

The study focuses on next‑generation molten salt–cooled reactors, which use liquid salts instead of water to carry heat. These salts run very hot but at low pressure, offering efficiency and safety advantages. Their downside is that they can partially solidify and clog small channels inside heat exchangers. Today’s plants mainly watch temperatures and pressures at the inlet and outlet of the equipment—like only checking a person’s temperature at the wrist and ankle to judge their overall health. Early clogs, affecting just a few channels, barely change these bulk readings and therefore slip past standard monitoring.

Listening to heat with light

To overcome this blind spot, the authors propose a compact “matrix” heat exchanger built from tightly packed arrays of parallel tubes separated by thin metal plates. Along the edges of these plates they envision threading fiber‑optic cables that act as hundreds of tiny thermometers. Light pulses sent down the fibers scatter in ways that reveal temperature every few millimeters along their length. This distributed temperature sensing turns the heat exchanger’s surface into a detailed thermal map, so that a partially blocked channel leaves a small but detectable warm or cool fingerprint on the neighboring metal.

Teaching machines to spot faint trouble

Because this concept is still being developed, the team used high‑fidelity computer simulations to mimic how the heat exchanger behaves in normal operation and in dozens of fault scenarios. They modeled different degrees of channel plugging—mild, moderate, and severe—and added realistic measurement noise drawn from real sensor experiments. Crucially, only about 3% of the simulated cases contained faults, reflecting the rarity of real problems and creating a strongly imbalanced dataset. In many early‑fault cases, the temperature patterns for healthy and unhealthy channels almost completely overlapped, making them hard to distinguish even for advanced algorithms.

Finding the best digital watchdog

The researchers compared eight common machine‑learning methods, from simple logistic regression to neural networks and advanced tree‑based “ensemble” models. They evaluated not just how often each model was right, but how well it handled the rare fault cases without flooding operators with false alarms. Extreme Gradient Boosting, or XGBoost, emerged as the most reliable watchdog. It was especially strong at recognizing severe blockages and distinguishing them from normal behavior, while still performing better than rivals on the trickiest mild clogs. Importantly, its predictions were fast enough to run in real time, fitting within the update cycles of industrial control systems.

Opening the black box for safety

Because nuclear systems are safety‑critical, the team went beyond raw accuracy to ask why the model made each decision. They combined two tools: Shapley values, which measure how much each input (such as a particular temperature reading or sensor position) pushes a prediction toward “normal” or “faulty,” and partially ordered sets, which group features when their influence is too similar to rank confidently. This hybrid approach revealed that one specific distributed outlet temperature measurement was consistently the most informative clue, but also showed when multiple sensors needed to be considered together for early, subtle faults. By clearly marking both the strongest signals and the uncertain gray areas, the method helps operators trust the model without giving it blind authority.

What this means for future reactors

In plain terms, the work shows that combining finely grained fiber‑optic temperature sensing with carefully chosen, explainable machine‑learning models can catch the earliest signs of clogging inside advanced nuclear heat exchangers. Instead of waiting for big, obvious drops in performance, operators could be alerted when just a few channels begin to misbehave, and even see which parts of the device are most suspect and which sensor readings drove that conclusion. If realized in hardware, this approach could lower maintenance costs, reduce unplanned outages, and add another layer of protection to the next generation of nuclear power plants.

Citation: Prantikos, K., Lee, T., Hua, T.Q. et al. Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions. Sci Rep 16, 8293 (2026). https://doi.org/10.1038/s41598-025-27112-8

Keywords: molten salt reactors, heat exchanger monitoring, anomaly detection, fiber optic temperature sensing, explainable machine learning