IT | ARTIFICIAL INTELLIGENCE


AI’s nuclear opportunities


In 2021 the IAEA hosted a Technical Meeting on Artificial Intelligence for Nuclear Technology and Applications to identify priorities for future activities and consider what support the IAEA can provide


MACHINE LEARNING REFERS TO TECHNOLOGIES that learn how to complete a particular task, based on large amounts of data. Artificial intelligence refers to systems capable of addressing complex problems in ways similar to human logic and reasoning. It can make use of huge amounts of complex data that would be impossible for humans using traditional techniques and it allows expertise to be captured and codified to support repeatable and explicable machine-led decisions. The use of such technologies in various nuclear sectors


was discussed in the publication Artificial Intelligence for Accelerating Nuclear Applications, Science and Technology, which was based on the IAEA’s meeting in 2021. A key chapter* (see box) discussed its application in the nuclear power industry. It considered that there are currently five main opportunities for AI to achieve a positive impact in the


nuclear power industry. They are: ● Automation ● Optimisation ● Analytics ● Prediction and prognostics ● Insight


Below: AI can support automated processes


Automation: increasing reliability and speeding up operations Nuclear plant staff can be placed in demanding situations to complete common tasks, making errors more likely and affecting safety. Other tasks may be repetitive and time consuming. Automation can help address these issues. The wide range of established machine learning


techniques allows automation of very different processes,


Optimisation: increasing efficiency and managing complex operations Optimising complex processes such as inventory management, outage scheduling and fuel cycle parameters will improve plant operation. This applies throughout the plant design life cycle, from initial engineering, construction, and operation to decommissioning. Elements of AI have already been used in building


information modelling (BIM) software. What holds up adoption is the lagging regulation for AI application in the plant design engineering process, machine-readable requirements, and data, methodologies and design algorithms that are accessible for AI. In operation, reactor controllers already take care of


some factors such as permitting operation only within given limits, shaping the power density distribution or operation economy. Machine learning can improve core- control methods and improve predictive control. Unlike human controllers, AI can address many different goals


26 | November 2022 | www.neimagazine.com


from automated analysis of complex process data to facilitating decision-making and improving work processes. Many of these applications are already well developed, such as in-service inspection. This is critical to safe operation, but analysing inspection data is laborious, time consuming and vulnerable to human errors. Automating data analysis with machine learning


improves reliability and efficiency and can extract additional information to improve predictability. For example, control rod drive mechanism (CRDM) coil currents are vulnerable to the build-up of metallic deposits (crud), which can mean the rods are dropped from their grippers, causing downtime. The crud shows up as a current anomaly, but identifying it requires hundreds of person-hours evaluating thousands of measurements. Machine learning can detect anomalies with 96% accuracy in near real-time. Machine learning can also detect data trends, such as


alerting operators to a growing anomaly when it is not visible to the operator and before it results in a significant change. Another application is combining it with drones, to reduce the need for operations staff to walk around the plant to inspect it and collect information. Human-computer interaction in the control room is the


basis for efficient operator perception and control of the operating status of the plant. Here, fast speech and gesture recognition can simplify the process and improve efficiency. Natural language processing can be coupled with machine learning to replicate the human decision-making processes of analysing event reports and generating outcomes. This area of research is rapidly advancing into task-specific tools.


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