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Details of Grant 

EPSRC Reference: EP/M018717/1
Title: Smart on-line monitoring for nuclear power plants (SMART)
Principal Investigator: Deng, Dr J
Other Investigators:
Sahinkaya, Professor MN Gorse, Professor C
Researcher Co-Investigators:
Project Partners:
Department: Built Environment and Engineering
Organisation: Leeds Beckett University
Scheme: Standard Research - NR1
Starts: 17 December 2015 Ends: 28 September 2018 Value (£): 301,001
EPSRC Research Topic Classifications:
Energy - Nuclear
EPSRC Industrial Sector Classifications:
Energy
Related Grants:
EP/M018709/1 EP/M018415/1
Panel History:
Panel DatePanel NameOutcome
28 Jan 2015 UK India Civil Nuclear Energy 3 Announced
Summary on Grant Application Form
Nuclear power has great potential as a future global power source with a small carbon footprint. To realise this potential, safety (and also the public perception of safety) is of the utmost importance, and both existing and new design nuclear power plants strive to improve safety, maintain availability and reduce the cost of operation and maintenance. Moreover, plant life extensions and power updates push the demand for the new tools for diagnosing and prognosing the health of nuclear power plants. Monitoring the status of plants by diverse means has become a norm. Current approaches for diagnosis and prognosis, which rely heavily on operator judgement on the basis of online monitoring of key variables, are not always reliable. This project will bring together three UK Universities and an Indian nuclear power plant to directly address the modelling, validation and verification changes in developing online monitoring tools for nuclear power plant.

The project will use artificial intelligence tools, where mathematical algorithms that emulate biological intelligence are used to solve difficult modelling, decision making and classification problems. This will involve optimizing 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. We will then use the optimised system to predict potential loss of coolant accidents and pinpoint their specific locations, after which we will progress to prediction of possible radioactive release for various accident scenarios, and, in order to facilitate emergency preparedness, the post release phase will be modelled to predict the dispersion pattern for the scenarios under consideration. Finally, all of the models will be validated, verified and integrated into a tool that can be used to monitor and act as an early warning device to prevent such scenarios from occurring.

Key Findings
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Potential use in non-academic contexts
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Summary
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Further Information:  
Organisation Website: http://www.leedsmet.ac.uk