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Precision control of Nano-fuel production

Storing hydrogen nanobubbles in liquid carriers to produce hydrogen nano-fuels offers significant benefits in terms of efficiency, safety and sustainability. Hydrogen nano-fuels have great potential to play a crucial role in the transition to a sustainable, low-carbon energy system.The stability and uniformity present major obstacles in nanobubble generation. Limited knowledge of nanobubble properties and bulk behaviour in various liquid carriers during the generation process makes it challenging to achieve uniform and stable nanobubbles.To address this challenge, this project will explore the impact of nano-fuel processing parameters on stability and uniformity by systematical design of experiments. Using data-driven models developed by machine/deep learning algorithms to predict the behaviour of hydrogen nanobubbles in liquid carriers.• This work will contribute towards the “emerging energy technologies” and “digital process engineering” research themes in the Department of Chemical Engineering.• This interdisciplinary work spans chemical engineering, data science and mechanical engineering disciplines.• This research will address complex global challenges related to both clean and sustainable energy and moving towards Industry 4.0. The aims of the research are aligned with the UK Industrial Strategy, UN SDG 7 (Clean and Affordable Energy) and UN SDG 9 (industry, innovation and infrastructure").• Academic impact: The hybrid models, which combine first-principle models with data-driven models, will provide significant insights into the formation and stability of nanobubbles. This approach will contribute to bridge the gap in understanding of nanobubble generation mechanism and bulk behaviour, which will enable precise process control and optimization.• Industrial impact: This work will accelerate the industrialisation and digitalisation progression of hydrogen nanofuels manufacturing, positioning UK as a global leader in the new era of net zero. The methodologies and digital twin framework can be adopted by industrial practitioners for other gas-species nanobubble generation systems.• Social impact: This work will help address the socio-environmental challenges faced by human and communities by reducing the reliance on fossil fuels and environmental burdens. This work will have a significant impact on the transition towards a renewable, sustainable energy to meet net zero target.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you would be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Yang Yang - Dr. Yang is a Lecturer in Chemical Engineering.  She has multidisciplinary background of BSc and MSc in Computer Science and PhD in Chemical Engineering.   Dr. Yang has twelve-years academic experience of applying her data science and machine learning knowledge for multi-field process modelling and analysis. In 2011, Dr. Yang received her PhD degree sponsored by Overseas Research Scholarships (ORS) and Tetley & Lupton Scholarships (TLS) from University of Leeds. During her PhD, she successfully applied data mining and machine learning techniques to identify the optimal composition of nano-photocatalyst (TiO2). Besides five high quality journal papers, the decisional tool designed and developed by Dr. Yang, which combined with Process analytical technology (PAT) and machine learning techniques, was sponsored and adopted by GlaxoSmithKline Pharmaceuticals (GSK) for its nanoparticle product line.    Dr. Yang joined Imperial College London and then University College London as a postdoctoral research associate. During this period, Dr. Yang accumulated great knowledge and experience in biopharmaceutical manufacturing process and personalised medicine and established collaborations with both academical and industrial partners. Collaborated with UCB and Eli Lily, the leaders of biopharmaceutical industries in UK, Dr. Yang established process models and Cost of Goods (CoGs) models of biomanufacturing process with discrete-event modelling and Monte Carlo simulation manufacturing facility fit analysis. A decision-support tool which combined the process models, CoGs models and machine learning models using decision tree algorithms had been greatly complimented by biopharmaceutical industry users. Supported by world leading pharmaceutical companies, Pall Corporation, Merck and Medimmune, Dr. Yang’s independent research of digital twins for continuous biomanufacturing process was awarded £5000 funding by Future targeted healthcare manufacturing hub in UCL. Dr. Yang has led a collaboration with Shanghai Pulmonary Hospital (China) to construct a decision-support tool with big data analysis for personalized diagnosis and treatment of lung cancer.