Plant-level decarbonization pathway in global oil refineries
Project ID: 2228cd1255 (You will need this ID for your application)
Research Theme: Energy and Decarbonisation
UCL Lead department: Bartlett School of Sustainable Construction
Lead Supervisor: Dabo Guan
Project Summary:
The oil refining sector, responsible for 5% of global CO2 emissions in 2019, is a critical role in the pathways towards achieving net-zero emissions. Against the backdrop of growing pressure to reduce GHG emissions and the increasing demands of the oil refining industry, it is necessary to explore low-carbon emission reduction paths for global refineries. Moreover, the heterogeneity of refineries necessitates of bespoke mitigation system for each refinery, which provides sufficient evidence to support carbon mitigation for the global oil refining industry.
This project will leverage the previous work of the team on the carbon emissions inventories of around 5,000 oil refineries to identify the most economical decarbonization routes at the plant level, considering economic costs and policy implications. The student will be trained in data science and modelling techniques.
This research will:
1) Identify the potential mitigation technologies and potential costs in the oil refining sector. This work will include extensive data collection and expert elicitation.
2). Forecast the future costs and mitigation potential of the promising technologies for zero-carbon refinery operations, considering regional disparities and dynamic cost projections. The cost forecast will use learning curve methods and expert elicitation.
3).Develop a bottom-up optimization model to elucidate the lowest-cost decarbonization pathways for individual refineries worldwide.
The student will be based at The Bartlett School of Sustainable Construction, University College London, and supervised by Prof. Dabo Guan and Prof. Jing Meng. The student will leverage the carbon emissions inventory developed by the group and have the opportunity to work alongside our wide network of collaborators across UCL, academia, and industry. We are looking for a student with a strong quantitative background, coding skills (at least one of MATLAB, R and Python), and ideally knowledge/experience in technology innovation, carbon accounting, and cost-benefit analysis.