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Nuclear Power Plant Safety

Smart Online Monitoring of Nuclear Power Plants

Smart Online Monitoring of Nuclear Power Plants is a two year project funded by the Engineering and Physical Sciences Research Council (EPSCR). The project is led by Professor Jaimei Deng at Leeds Beckett University with partners from the University of Portsmouth, The University of Liverpool and the Bhaba Atomic Research Centre (BARC) in India. 

Background

Nuclear power has great potential as a future global power source with a small carbon footprint. 

Designers and operators of nuclear power plants strive to improve safety, maintain availability and reduce the cost of operation and maintenance. However plant life extensions and power updates increase the demand for new tools for diagnosing the health of nuclear power plants.

Monitoring the status of nuclear power plants by diverse means is necessary for their safe operation. However, current diagnostic approaches depend heavily on human operator judgement based on information available in the plant’s control room, with decisions on accident progression made through human verification and validation, a process which is not always reliable.

Method

The project will use artificial intelligence and signal processing tools to monitor nuclear power plants and to predict the dispersion of radioactivity in time and space following an accident. Mathematical algorithms which emulate biological intelligence will be used to solve difficult modelling and classification problems in order to aid decision making. This will involve optimising the number of inputs to the models, finding the minimum data requirement for accurate prediction of possible untoward events, and designing experiments to maximize the information content of the data.

Outputs

The tools being developed in this project will act as early warning devices in order to facilitate emergency preparedness, prevent accidents from occurring, predict potential loss of coolant accidents, pinpoint their specific locations and predict possible radioactive release and dispersion patterns for different accident scenarios. They will also monitor accident progression at nuclear power plants and predict the onset and evolution of an accident, supporting operators in their decision-making process. It is expected that the tools will contribute towards improving safety, maintaining plant availability and reducing accident handling costs in nuclear power plants.

Academic team

Professor Jaimei Deng - Professor of Artificial Intelligence, Control and Energy. Project Coordinator and Principal Investigator for Leeds Becket University. Link to full profile

Professor Victor M Becerra Lecturer in Nuclear Power Engineering - Professor of Power Systems Engineering. Principal Investigator for the University of Portsmouth. Link to full profile

Dr Edoardo Patelli - Principal Investigator for the University of Liverpool.  Link to full profile

Dr Gopika Vinod, Bhaba Atomic Research Centre. Link to full profile

Research Assistants:

Dr Xiange Tian, University of Portsmouth.

Dr Giuseppe Colantuono, Leeds Beckett University

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