Intelligent data analytics to optimise energy utilisation in industrial processing

Lead Research Organisation: University of Nottingham
Department Name: Faculty of Engineering

Abstract

This Resilient Decarbonised Fuel Energy Systems PhD project will investigate the use of sensor measurements and machine learning to predict and optimise either the energy production or energy utilisation for a range of industrial relevant case studies. The PhD project will be sponsored by the industrial partner Intelligent Plant, who focus on the analysis and visualisation of industrial data. Although the project will focus on different case studies. The aim is to determine suitable data collection and analysis strategies that can be applied to a variety of different industrial systems. The first and second case studies will focus on energy production systems and the third and fourth on the energy efficiency of industrial processes. An objective of this project is to develop appropriate data analysis and visualisation methods, which contribute to the UK's, net zero ambition

Planned Impact

The proposed Centre will benefit the following groups

1. Students - develop their professional skills, a broad technical and societal knowledge of the sector and a wider appreciation of the role decarbonised fuel systems will play in the UK and internationally. They will develop a strong network of peers who they can draw on in their professional careers. We will continue to offer our training to other Research Council PhD students and cross-fertilise our training with that offered under other CDT programmes, and similar initiatives where that develops mutual benefit. We will further enhance this offering by encouraging industrialists to undertake some of our training as Professional Development ensuring a broadening of the training cohort beyond academe. Students will be very employable due to their knowledge, skills and broad industrial understanding.
2. Industrial partners - Companies identify research priorities that underpin their long-term business goals and can access state of the art facilities within the HEIs involved to support that research. They do not need to pre-define the scope of their work at the outset, so that the Centre can remain responsive to their developing research needs. They may develop new products, services or models and have access to a potential employee cohort, with an advanced skill base. We have already established a track record in our predecessor CDTs, with graduates now acting as research managers and project supervisors within industry
3. Academic partners - accelerating research within the Energy research community in each HEI. We will develop the next generation of researchers and research leaders with a broader perspective than traditional PhD research and create a bedrock of research expertise within each HEI, developing supervisory skills across a broad range of topics and faculties and supporting HEIs' goals of high quality publications leading to research impacts and an informed group of educators within each HEI. .
4. Government and regulators - we will liaise with national and regional regulators and policy makers. We will conduct research directly aligned with the Government's Clean Growth Strategy, Mission Innovation and with the Industrial Strategy Challenge Fund's theme Prosper from the Energy Revolution, to help meet emission, energy security and affordability targets and we will seek to inform developing energy policy through new findings and impartial scientific advice. We will help to provide the skills base and future innovators to enable growth in the decarbonised energy sector.
5. Wider society and the publics - developing technologies to reduce carbon emissions and reduce the cost of a transition to a low carbon economy. Need to ascertain the publics' views on the proposed new technologies to ensure we are aligned with their views and that there will be general acceptance of the new technologies. Public engagement will be a two-way conversation where researchers will listen to the views of different publics, acknowledging that there are many publics and not just one uniform group. We will actively engage with public from including schools, our local communities and the 'interested' public, seeking to be honest providers of unbiased technical information in a way that is correct yet accessible.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022996/1 01/10/2019 31/03/2028
2284079 Studentship EP/S022996/1 01/10/2019 30/12/2023 Khivishta Boodhoo
 
Description This work helps the maintenance team to perform maintenance on offshore wind turbines without disrupting the power output of the wind turbine when doing so. By using artificial intelligence as a tool to develop a model capable of predicting power outputs, wind turbines can be turned off when the power output is minimum during a certain period for maintenance. This helps optimisation of the power output of offshore wind turbines. Future case studies will be conducted to use the same framework to optimise their energy usage or production.
Exploitation Route The framework being developed to apply to different case studies in systems that produce or use energy can be used in different scenarios to optimise the energy being used or produced. This framework would help in saving and optimising energy in multiple scenarios.
Sectors Energy,Environment

 
Description Intelligent data analytics to optimise energy utilisation in industrial processing
Amount £75,036 (GBP)
Funding ID 2284079 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 10/2019 
End 09/2023
 
Title MLP 
Description This deep learning neural network model was developed and deployed to make power predictions for offshore wind turbines. Cross-validation, grid search and hyperparameter tuning were used to produce the best model. 
Type Of Material Computer model/algorithm 
Year Produced 2022 
Provided To Others? No  
Impact The deep learning model developed can outperform others on a large number of datasets. This can be successfully used whenever very large amount of data is available. 
 
Title SCADA and MET dataset 
Description This dataset is obtained from the SCADA of the ORE catapult wind turbine and consists of 30 million data points. This dataset was further joined with the MET dataset from the weather office containing around 27 million data points. These datasets were also available in an already compressed format on the Company Intelligent Plant servers who is also sponsoring this project. The framework developed to analyse such a large amount of data and relevant steps to preprocess the datasets which were prone to sensor errors, shutdowns and wrong recordings have been applied starting from loading such a massive dataset, visualisation of potential problems in the dataset, replacing missing values, removing data points with brake conditions, abnormal pitch angle and negative values of power. The next step in the framework was to identify the outliers using a specific method of clustering and afterwards removing an excessive number of repetitive values. 
Type Of Material Data analysis technique 
Year Produced 2022 
Provided To Others? No  
Impact The framework being developed could be applied to any SCADA datasets to clean and preprocess the data before any modelling is undertaken for offshore wind turbines around the world. 
 
Title An application to predict low power output for wind turbines 
Description This application is still being developed and will be available on the Intelligent Plant industrial app store. The app will inform the maintenance team of wind turbines when is the most suitable time to go and perform maintenance on the wind turbine without disrupting the power output of the wind turbine. This is being done by predicting low power outputs in four hours blocks to allow the maintenance team enough time to do maintenance when the power output of the wind turbine is predicted to be low. This will optimise the overall wind turbine performance. 
Type Of Technology Webtool/Application 
Year Produced 2022 
Impact The app will be sold to operators of wind turbines to plan maintenance for offshore wind turbines. 
 
Description Presentation 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Industry/Business
Results and Impact The PowerPoint presentation was a summarised version of the research methodology and results that I had obtained during the year. The audience was composed of different individuals working at Intelligent Plant (CEO, graphic designers, engineers, marketing teams etc...). This presentation enabled the individuals to understand and develop a recorded presentation with subtitles from the live presentation and use the latter to market the app being developed at Intelligent Plant.
Year(s) Of Engagement Activity 2020