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Artificial Neural Network-informed Process Modelling in Optimisation of Post-combustion Carbon Capture

In June 2019, the UK government declared a climate emergency and announced a target of net zero greenhouse gas (GHG) emissions compared to the 1990 emission levels by the year 2050. Carbon dioxide is the main GHG contributing to climate change, emitted mainly by the power generation sector. Therefore, capturing CO2 from the flue gasses of fossil-fuel power plants and its subsequent geological storage, known as post-combustion CO2 capture and storage (CCS), is a key approach to mitigating carbon emissions.

The Intergovernmental Panel on Climate Change has stated that CCS is essential to achieving net zero emissions. CCS; however, is associated with high operational costs. Optimisation of CCS processes can pinpoint the optimum operating conditions for optimum capture efficiency, capital and operating costs. Artificial Neural Networks (ANN) are multifaceted mathematical tools effectively used to model and predict various complex and highly non-linear processes.

The use of ANN is a practical method in effective optimisation of CO2 capture processes which are highly non-linear in nature. This research aims to develop and experimentally validate a robust ANN-informed post-combustion adsorption-based carbon capture process model. Process modelling and ANN training will be conducted based on the available experimental data for novel adsorbents developed in our lab. This project will be a comprehensive extension to existing experimental research in our group on developing cost-effective adsorbents.

The research will therefore, comprise the following objectives/tasks:

  1. To develop a detailed model of a post-combustion adsorption-based CO2 capture process based on the existing experimental data gathered in our research group;
  2. To develop, train and optimise a robust ANN model based on the existing experimental data, and by conducting additional experimental trials if/when necessary;
  3. To experimentally validate the optimum operating conditions identified in part (2);
  4. To evaluate the adsorption-based carbon process model developed in (1), and informed by (1)-(3).

This work will, therefore, strive to pave the way for accelerated deployment of CCS by helping the UK to meet its recently-announced target of zero greenhouse gas emissions in the country by 2050.

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 woold 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: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. 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%.