Yiran Liu

Postdoctoral Fellow

University of New South Wales

Dr Yiran Liu is currently a Postdoctoral Fellow in the School of Chemical Engineering at the University of New South Wales (UNSW). Her research focuses on the combustion of biomass and coal in the ironmaking blast furnace toward decarbonisation, by means of numerical simulation, machine learning and experiments. She received her BEng. (2014) and MEng. (2017) degrees from the University of Science and Technology Beijing, and PhD degree from UNSW in 2021. She has published over 20 papers in leading journals and conference proceedings and has been involved in several research projects funded by the Australian government and/or industry. She is a member of the Youth Editorial Board of "Journal of Iron and Steel Research International", and the reviewers of several prestigious journals, e.g. “Metallurgical and Materials Transactions B”. 

Enable carbon-neutral and intelligent ironmaking

Comprehensive research work has been done on transforming the current energy-intensive and carbonintensive ironmaking industry into the next-generation low-carbon intelligent ironmaking industry. The research methodology has been expanded from Computational Fluid Dynamics (CFD) modelling, Heat and Mass Balance (HMB) modelling to the Data-driven approach. The implementation of renewable carbon-neutral or carbon-free fuels is considered as an imperative and essential countermeasure for sustainable ironmaking. Biomass and hydrogen are regarded as promising renewable fuels to mitigate CO2 emissions of ironmaking blast furnaces (BFs), and low-rank coal is regarded as flexible fuel to maintain the supply chain. Firstly, an HMB model has been developed, calibrated and then subjected to the design of the feasible operating windows for the injection and combustion of biomass, hydrogen, semicoke or coals in various BFs. Secondly, several 3-dimensional industrial-scale CFD models have been developed to describe flow and thermochemical behaviours related to single/double injection of the fuels into different BFs within the operating windows. Finally, an machine learning based Data-driven model has been developed for fast prediction of in-furnace phenomena of fuel combustion within BFs. The response time of this approach is up to 16,000 times shorter than the CFD simulations while achieving similar accuracy. These models provide time- and cost-effective tools for design, description and fast prediction of in-furnace phenomena of BFs. The modelling result has been successfully implemented in a 49-day plant test. More than 20 peerreviewed journal papers have been published, including one review paper on biomass combustion in BFs in Progress in Energy and Combustion Science, and presentations have been selected and done at 7 international conferences.