Smart on-line monitoring for nuclear power plants (SMART)

dfcab6fa-59c5-4606-9b8e-1ee08186bd96

Closed

EPSRC
EPSRC
EPSRC
EPSRC
EPSRC

£1,505,005

Dec. 17, 2015

Sept. 28, 2018

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.

Potential Impact:
The nature of the research is such that the outcomes of the project will be very relevant to the nuclear energy industry. The project aims to produce an effective tool to enhance the safety of nuclear power plants. The tool, software and underlying models that will form the end-result will be suitable for incorporation into the control room of power plant, or as separate modules for use to improve the safety of nuclear power plants. Some of the models to be developed could also be of use to the UK atomic energy and environmental authorities for monitoring the safety of the power plants and for predicting radioactive releases which could adversely affect humans and/or the environment.
In addition to the tool envisaged, the advanced signal processing method for verification and validation, the online modeling technique for the prediction of plume dispersion, the hybrid methods of neural network techniques and the signal processing methods to be used to develop the tool are innovative, and will be of interest to others in this field. Firstly, the interest would be in research and development, so would be expected to be primarily within the academic community. However, there would be some longer-term impact expected, as online modelling techniques, neural networks and signal processing can be used in many diverse applications, and the innovative techniques could be applied to many areas e.g. medical instrumentation.
A further impact is that it will help reduce radioactive releases to the environment with ensuing benefits to human health by managing the accidents better in nuclear plants. The methods to ensure the impact to both academics and industry will be:
1. Communications and engagement
The results will be presented at different conferences: Conference on Decision and Control, SAFEPROCESS, American Nuclear Society Winter Meeting, ICNESE, ESREL, and RAMS conferences, all of which are of relevance to the project. Those conference will attract the major players in the field, from both the academic and industrial world, and also those with safety-related interests.
A workshop will also be hosted at the University towards the end of the grant, to disseminate results to both academic and industrial colleagues.
A project website will also be launched, where a summary of progress will be maintained, and progress reports written at the end of each phase will be available to download.
The results will also be published on the journals that enjoy a wide circulation in the nuclear industry (principally IEEE Transactions on Nuclear Sciences, Reliability Engineering and Systems). But publications will also be written for journals dedicated to the fields of modeling, and of safety. At least one publication will be aimed at an open-access journal, to ensure a wide circulation.
Additionally, the PIs will be involved in public engagement via presenting at public open days hosted by the University, schools visits, Women's Engineering Society and popular science articles (e.g. New Scientist).
2. Collaboration
EDF energy will provide some practical advice, which could improve marketability. The PIs will look for potential partners by attending the conference, searching in the internet, publicizing the results through the project website.
3. Exploitation and application
The methods and tools to be developed will have the potential to be commercially exploitable. It addresses a problem experienced by industry, and can be best put to use by incorporation into commercial products.
4. Capability
The PI will be involved in impact activities, especially as they are likely to continue after the end of the award (e.g. commercialisation, Phase 4 UK-India Nuclear collaboration). However, she will ensure that both the RA and the student have some involvement in these activities, as part of their career development. All will be involved in producing reports and ensuring the website is up to date.