Staff Profile

Dr Yongliang (Harry) Yan is a research associate in the School of Engineering working on scaling up a novel chemical looping concept for low-carbon hydrogen production.

Dr Yan received an MSc in Mechanical Engineering from the University of Lincoln focusing on the CO₂ mitigation technologies in Combined Cycle Gas Turbine (CCGT) power plants and went on to complete a PhD in Energy and Power at Cranfield University in 2020. In 2019, he visited the Chalmers University of Technology, Sweden working as a research associate in applying machine learning in oxygen-carrying materials development. During his PhD, he worked as a part-time research assistant in the HyPer project (Demonstration of 1.5 MWth bulk hydrogen production plant by sorption enhanced steam reforming) and teaching assistant in the Energy and Power theme.

Dr Yan is an Associate Member of the Institute of Chemical Engineers (AMIChemE), the Royal Society of Chemistry (AMRSC), and the Early Career Researcher (ECR) of the UKCCS Research Centre.

Dr Yan’s research interest is firmly in CO₂ capture technologies, clean hydrogen production, cost-effective energy storage, probabilistic techno-economic performance assessment and machine learning in materials science and energy engineering.

He is currently working on scaling up a novel chemical looping concept for efficient low-carbon hydrogen production in the MatCoRE group (Led by Prof Ian Metcalfe).

• Yan Y, Mattisson T, Moldenhauer P, Anthony EJ, Clough PT. Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes. Chemical Engineering Journal 2020, 387, 124072.
• Yan Y, Manovic V, Anthony EJ, Clough PT. Techno-economic analysis of low-carbon hydrogen production by sorption enhanced steam methane reforming (SE-SMR) processes. Energy Conversion and Management 2020, 226, 113530.
• Yan Y, Thanganadar T, Clough PT, Mukherjee S, Patchigolla K, Manovic V, Anthony EJ. Process simulations of blue hydrogen production by upgraded sorption enhanced steam methane reforming (SE-SMR) processes. Energy Conversion and Management 2020, 222, 113144.
• Yan Y, Borhani TN, Clough PT. Machine Learning Applications in Chemical Engineering. In: Cartwright, H, ed. Machine Learning in Chemistry: The Impact of Artificial Intelligence. Cambridge: Royal Society of Chemistry, 2020, pp.340-371.
• Nkulikiyinka P, Yan Y, Güleç F, Manovic V, Clough P. Prediction of sorption enhanced steam methane reforming products from machine learning based soft-sensor models. Energy and AI 2020, 2, 100037.
• Yan Y, Wang K, Clough PT, Anthony EJ. Developments in calcium/chemical looping and metal oxide redox cycles for high-temperature thermochemical energy storage: A review. Fuel Processing Technology 2020, 199, 106280.
• Yan Y, Clough PT, Anthony EJ. Investigation of the apparent kinetics of air and oxy-fuel biomass combustion in a spout fluidised-bed reactor. Chemical Engineering Research and Design 2020, 153, 276-283.