Plant-Level Technology Pathways for Cost-Effective Decarbonization in Global Iron and Steel Plants

Project ID: 2228cd1254 (You will need this ID for your application)

Research Theme: Energy and Decarbonisation

UCL Lead department: Bartlett School of Sustainable Construction

Department Website

Lead Supervisor: Jing Meng

Project Summary:

The iron and steel sector contributing 7% of the world’s CO2 emissions plays a pivotal role in the global transition toward net-zero emissions. The sector’s strong reliance on fossil fuel technologies makes it notably difficult to decarbonize and contributes to air pollution. The policies to achieve net zero present an opportunity to choose win-win for climate change mitigation and clean air.

This project will leverage the previous work of the team about the carbon emissions inventory and technology pathway of around 5000 iron and steel sector (Lei et al., 2023), to identify not only the technologically feasible, but also economically and socially feasible mitigation pathway. This project aims to develop plant-level decarbonisation pathways that maximise the air pollution related health co-benefits, with details of the technology roadmap, mitigation costs and plant-level contribution to air quality. The objectives are: 1)To forecast the future costs of promising technologies for zero-carbon steel production, taking into account regional differences and time-sensitive cost projections. It will be based on learning curves and expert elicitation. 2)To assess the mitigation costs and the potential health co-benefits at the plant level, building on the atmospheric model and health impact model. 3)To develop a bottom-up optimization model to identify the economically and socially feasible mitigation pathway.

The student will be based at The Bartlett School of Sustainable Construction and supervised by Prof. Jing Meng and Prof. Dabo Guan. 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, atmospheric modelling and cost-benefit analysis.