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

Lead Research Organisation: Leeds Metropolitan University
Department Name: Built Environment and Engineering


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.

Planned 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.
Description Our partner Bhabha Atomic Research Centre has problems in predicting big breaks in the pipes of nuclear power plant. We have achieved so far to predict the big break size of the pipes as accurately as the small break size. We also have been fine tuning the big break size prediction results since the visit from Bhabha Atomtic Research Centre in May 2017.
Exploitation Route In May 2017, Bharbhar Atomic Research Centre researcher came to Leeds Beckett and implemented the prediction model of this big break size to their nuclear safety system.
Sectors Construction,Energy

Description The findings will have impact on the perception of nuclear safety. our results for big break size is more accurate.Bhabha Atomic Research Centre used our results in their safety systems after their visit in May 2017. habha Atomic Research Centre will address this impact to India government.
First Year Of Impact 2018
Sector Energy
Impact Types Cultural,Societal

Description Fault tolerant control for increased safety and security of nuclear power plants
Amount £220,000 (GBP)
Funding ID EP/R021961/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Academic/University
Country United Kingdom
Start 10/2018 
End 03/2021
Description PhD studentship
Amount £60,000 (GBP)
Organisation Leeds Beckett University 
Sector Academic/University
Country United Kingdom
Start 01/2017 
End 01/2021
Title Interpolation data based modelling 
Description 1. Optimal neural network structure searched 2. Data used for building neural network model was interpolated 3. Big break size prediction performance is largely improved by this method 
Type Of Material Improvements to research infrastructure 
Year Produced 2017 
Provided To Others? Yes  
Impact Collecting data is very expensive. Using interpolation data, the cost is significantly saved, yet better performance has been achieved for big break size of nuclear power plants. 
Title Large break size model 
Description 1. This model could predict the large break size accurately. 2. Database is generated by using linear interpolation method. 
Type Of Material Computer model/algorithm 
Year Produced 2017 
Provided To Others? Yes  
Impact Academic impact will be: Improving the accuracy of large break size Other impact will be improving the safety of nuclear power plant 
Description Bhabha Atomic Research Centre (BARC) 
Organisation Bhabbha Atomic Research Centre
Country India 
Sector Public 
PI Contribution 1. Regular video conference meeting 2. Improve the model performance of BARC 3. provide better model to update BARC safety system
Collaborator Contribution 1. Provide data for model building 2. explanation of their test facility 3. regular video conference meeting
Impact 1. Better break size prediction model has been obtained from this collaboration
Start Year 2015
Title Breaksize generation based on RELAP5 
Description This software could be used to generate pipe break size, so that neural network model could be built based on the data using this software 
Type Of Technology Software 
Year Produced 2017 
Open Source License? Yes  
Impact AFter using this software, neural network models are more accurate 
Description 2017 Summer School (Leeds Beckett) 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This is a summer school, which is funded by Horizon 2020.There are large group of people. The purpose of this presentation in the summer school is to articulate the importance of the EPSRC research of " Smart online monitoring of nuclear power plants". The result has been demonstarted.
Year(s) Of Engagement Activity 2017
Description 2018 Summer School 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This is a summer school, which is funded by Horizon 2020.There are large group of people. The purpose of this presentation in the summer school is to articulate the importance of the EPSRC research of " Smart online monitoring of nuclear power plants". The result has been demonstrated.
Year(s) Of Engagement Activity 2018
Description Final Project workshop 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Professional Practitioners
Results and Impact 20 researchers attended the workshop, which sparked questions and discussion afterwards
Year(s) Of Engagement Activity 2008
Description Research feature magazine 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact The activity is trying to inform general audiences about our work .
Year(s) Of Engagement Activity 2018