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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
Meeting Spotlight
2025 ANS Annual Conference
June 15–18, 2025
Chicago, IL|Chicago Marriott Downtown
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Smarter waste strategies: Helping deliver on the promise of advanced nuclear
At COP28, held in Dubai in 2023, a clear consensus emerged: Nuclear energy must be a cornerstone of the global clean energy transition. With electricity demand projected to soar as we decarbonize not just power but also industry, transport, and heat, the case for new nuclear is compelling. More than 20 countries committed to tripling global nuclear capacity by 2050. In the United States alone, the Department of Energy forecasts that the country’s current nuclear capacity could more than triple, adding 200 GW of new nuclear to the existing 95 GW by mid-century.
James A. Smith, Vivek Agarwal, Ahmad Al Rashdan (INL)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 1667-1671
Data analytics should be at the center of strategic maintenance decision making. The diversity and quality of data collected provides key intuition that drives effective decisions on complicated topics. Online condition monitoring is used to reduce time based preventive maintenance and to enable predictive maintenance. Effective interpretation of data leads to information that plant operators can turn into decisions and actions that improve operations and maintenance activities. Data analytics is the primary technique used to facilitate effective data interpretation that will generate revolutionary results. The starting point is the data. Patterns in the data are noted and observed. The patterns observed while the plant is operating under preset conditions define process states. These patterns are mathematically manipulated to highlight changes when process changes are detected. The methods that detect state changes usually rely on correlation algorithms. Statistics are used to determine if the changes in the patterns are real or caused by plant noise and uncertainty levels. Integrated tools are used to implement algorithms that form the data analytics process and automate the decision making. Operations research is necessary to understand the operational context of the data. Machine learning algorithms provide dynamic mathematical means that can understand the present state and predict the next state with a degree of certainty. It is this prediction and the associated prediction certainty that allows plant operators to make effective decisions. This paper will discuss the approach to build a roadmap that will migrate data analytic techniques into production facilities.