Professor Jin Xuan
Academic and research departments
Faculty of Engineering and Physical Sciences, School of Chemistry and Chemical Engineering.About
Biography
Professor Jin Xuan joined the University of Surrey as the Associate Dean of Research and Innovation for the Faculty of Engineering and Physical Sciences in September 2022. He also holds a Chair in Sustainable Processes and a prestigious EPSRC Open Fellowship at the School of Chemistry and Chemical Engineering. Before moving to Surrey, he was the Head of the Department of Chemical Engineering at Loughborough University.
Professor Xuan has won a number of prizes and awards in recognition of his research excellence. He is the recipient of the Philip Leverhulme Prize of Engineering in 2022 for his pioneering research on Energy and AI, and the Beilby Medal and Prize, jointly from the Society of Chemical Industry (SCI), the Royal Society of Chemistry (RSC), and the Institute of Materials, Minerals and Mining (IOM3) in 2020 for his work that ‘has exceptional practical significance in chemical engineering, applied materials science, energy efficiency or a related field’.
Professor Xuan actively takes leadership in the wider research community. He is an EPSRC Strategic Advisory Committee (SAC) member for the cross-council Energy Programme, and a member of the Research, Innovation & Knowledge Transfer Committee of the Engineering Professors’ Council.
Prof Xuan contributed to the establishment of the emerging Energy and AI research community globally. He is the Founding Editor-in-Chief of Digital Chemical Engineering (IChemE), the Founding Editor of Energy and AI (Elsevier), and the co-chair of the International Conference of Energy and AI series (Tianjin 2020, London 2021, Belfort 2022). In his Editorial of Energy and AI, he provided a strategic overview in this interdisciplinary research area including wider societal issues such as ethics, morality, policy and law.
Professor Xuan is an advocate for Responsible Assessment. He was recently interviewed by Nature as part of a news item China bans cash rewards for publishing papers, where he provided his expert view on policy development for proper use of journal metrics in research evaluation. He also actively promotes EDI in the wider research community. He serves as the EDI Champion in the EPSRC Energy SAC, and is the leading author of The equality, diversity and inclusion in energy and AI: Call for actions.
University roles and responsibilities
- Associate Dean (Research and Innovation) for the Faculty of Engineering and Physical Sciences
- Professor of Sustainable Processes
- Member of Council
- Member of Senate
News
ResearchResearch interests
Professor Xuan’s research focuses on net zero energy, circular economy and sustainable development via digital and engineering innovations. He is leading the development of bespoke AI and digital solutions to enable next generation energy and chemical materials, devices, processes and systems. His research has significantly influenced the early development of Energy and AI as a young, interdisciplinary field internationally.
The future sustainable development of novel energy generation and materials manufacturing relies on radical innovations in chemical processes with highly embedded functionality, integration and multi-physics interactions. Prof Xuan has led the development of novel multiscale predictive models beyond the state of art for complex chemical and energy systems, and has delivered affordable, inclusive low-carbon solutions to tackle global climate change challenges. His work on advanced modelling has influenced the fundamental understanding of complex flow and chemical systems, by having discovered a number of important reaction/fluid phenomena at microscale, and has impacted on the development of a series of energy devices, such as fuel cells, electrolysers and solar fuel reactors.
His current research is focusing on the digital transformation of complex chemical and energy processes via integrated smart sensing, advanced modelling and data-centric deep learning, with a vision to deliver a paradigm-shift in how future materials, processes and systems can be designed, optimised and self-evolved throughout their entire lifecycle, enabling connected products and services, and making them super-efficient, zero-loss, whilst maximising their value creation.
Circular Chemical Economy
Prof Xuan is the Director of the £4.5 million UKRI Interdisciplinary Centre for Circular Chemical Economy. It brings together stakeholders from academia, industry, government, NGOs and general public to transform the UK’s chemical industry into a fossil-independent, climate-positive and environmentally-friendly circular economy. The Centre is playing a key role in helping the UK to reduce waste and the environmental impacts of production and consumption and creating opportunities for new UK industries.
EPSRC Open Fellowship
Prof Xuan holds a prestigious EPSRC Open Fellowship to develop the next generation of clean energy devices using advanced artificial intelligence (AI). The five-year £2 million fellowship will enable Prof Xuan to develop novel explainable AI (XAI) tools and models which lead to an automated loop of materials design, manufacturing and testing of electrochemical materials and devices. Prof Xuan will work with industrial partners from Siemens PSE, Intelligent Energy and Johnson Matthey.
Flue2Chem:
Building a UK value chain in converting industrial waste gases into sustainable materials for consumer products
Prof Xuan is leading the Surrey team as part of the Flue2Chem project. The £5.4m Flue2Chem Project is a two-year programme spearheaded by Unilever funded by Innovate UK. In the project, industry giants in the UK are joining forces in the first-ever cross-sector collaboration aimed at reducing greenhouse gas emissions, with the aims to convert industrial waste gases into chemicals that can be used to manufacture superior and more sustainable consumer products.
Research interests
Professor Xuan’s research focuses on net zero energy, circular economy and sustainable development via digital and engineering innovations. He is leading the development of bespoke AI and digital solutions to enable next generation energy and chemical materials, devices, processes and systems. His research has significantly influenced the early development of Energy and AI as a young, interdisciplinary field internationally.
The future sustainable development of novel energy generation and materials manufacturing relies on radical innovations in chemical processes with highly embedded functionality, integration and multi-physics interactions. Prof Xuan has led the development of novel multiscale predictive models beyond the state of art for complex chemical and energy systems, and has delivered affordable, inclusive low-carbon solutions to tackle global climate change challenges. His work on advanced modelling has influenced the fundamental understanding of complex flow and chemical systems, by having discovered a number of important reaction/fluid phenomena at microscale, and has impacted on the development of a series of energy devices, such as fuel cells, electrolysers and solar fuel reactors.
His current research is focusing on the digital transformation of complex chemical and energy processes via integrated smart sensing, advanced modelling and data-centric deep learning, with a vision to deliver a paradigm-shift in how future materials, processes and systems can be designed, optimised and self-evolved throughout their entire lifecycle, enabling connected products and services, and making them super-efficient, zero-loss, whilst maximising their value creation.
Circular Chemical Economy
Prof Xuan is the Director of the £4.5 million UKRI Interdisciplinary Centre for Circular Chemical Economy. It brings together stakeholders from academia, industry, government, NGOs and general public to transform the UK’s chemical industry into a fossil-independent, climate-positive and environmentally-friendly circular economy. The Centre is playing a key role in helping the UK to reduce waste and the environmental impacts of production and consumption and creating opportunities for new UK industries.
EPSRC Open Fellowship
Prof Xuan holds a prestigious EPSRC Open Fellowship to develop the next generation of clean energy devices using advanced artificial intelligence (AI). The five-year £2 million fellowship will enable Prof Xuan to develop novel explainable AI (XAI) tools and models which lead to an automated loop of materials design, manufacturing and testing of electrochemical materials and devices. Prof Xuan will work with industrial partners from Siemens PSE, Intelligent Energy and Johnson Matthey.
Flue2Chem:
Building a UK value chain in converting industrial waste gases into sustainable materials for consumer products
Prof Xuan is leading the Surrey team as part of the Flue2Chem project. The £5.4m Flue2Chem Project is a two-year programme spearheaded by Unilever funded by Innovate UK. In the project, industry giants in the UK are joining forces in the first-ever cross-sector collaboration aimed at reducing greenhouse gas emissions, with the aims to convert industrial waste gases into chemicals that can be used to manufacture superior and more sustainable consumer products.
Publications
Highlights
Recent representative publications
- Jiao K, Xuan J, Du Q, Bao Z, Xie B, Wang B, Zhao Y, Fan L, Wang H, Hou Z, Huo S, Brandon NP, Yin Y, Guiver MD, Designing the next-generation of proton exchange membrane fuel cells, Nature, 2021, 595, 361-369
- Niu Z, Pinfield VJ, Wu B, Wang H, Jiao K, Leung DYC, Xuan J*, Towards the digitalisation of porous energy materials: Evolution of digital approaches for microstructural design, Energy & Environmental Science, 2021, 14, 2549-2576. (Highlighted as journal front cover)
- Niu Z, Zhao W, Wu B, Wang H, Lin W, Pinfield VJ, Xuan J*, π Learning: A Performance-informed framework for microstructural electrode design, Advanced Energy Materials, 2023, 13, 2300244. (Highlighted as journal front cover)
- Leong KW, Pan W, Yi X, Luo S, Zhao X, Zhang Y, Wang Y, Mao J, Chen Y, Xuan J, Wang H, Leung DYC, Next-generation magnesium-ion batteries: The quasi-solid-state approach to multivalent metal ion storage, Science Advances, 2023, 9, eadh1181.
- Pan W, Zhao Y, Mao J, Wang Y, Zhao X, Leung KW, Luo S, Liu X, Wang H, Xuan J, Yang S, Chen Y, Leung DYC, High-energy single-walled carbon nanotube cathode for aqueous Al-ion battery boosted by multi-ion intercalation chemistry, Advanced Energy Materials, 2021, 11, 2101514. (Highlighted as journal back cover)
- Wang B, Zhang G, Wang H, Xuan J*, Jiao K, Multi-physics-resolved digital twining of proton exchange membrane fuel cells with a data-driven surrogate model, Energy and AI, 2020, 1, 100004.
- Lu X, Zhu C, Wu Z, Xuan J, Francisco JS, Wang H, In-situ observation of the pH gradient near the gas diffusion electrode of CO2 reduction in alkaline electrolyte, Journal of the American Chemical Society, 2020, 142, 15438–15444. (Highlighted as journal front cover)
- Xu H, Ma J, Tan P, Chen B, Wu Z, Zhang Y, Xuan J*, Ni M, Towards online optimisation of solid oxide fuel cell performance: combining deep learning with multi-physics simulation, Energy and AI, 2020, 1, 100003.
- Zhakeyev A, Wang P, Zhang L, Shu W, Wang H, Xuan J*, Additive manufacturing: Unlocking the evolution of energy materials, Advanced Science, 2017, 4, 1700187. (Most Accessed Article in November 2017)
- Wang B, Prinsen P, Wang H, Bai Z, Wang H, Luque R, Xuan J*, Macroporous materials: microfluidic production, functionalization and application, Chemical Society Reviews, 2017, 46, 855-914.
This work evaluated the practicability and economy of the enhanced weathering (EW)-based CO2 capture in series packed bubble column (S-PBC) contactors operated with different process configurations and conditions. The S-PBC contactors are designed to fully use the advantages of abundant seawater and highly efficient freshwater through a holistic M4 model, including multi-physics, machine learning, multi-variable and multi-objective optimisation. An economic analysis is then performed to investigate the cost of different S-PBC configurations. A data-driven surrogate model based on a novel machine learning algorithm, extended adaptive hybrid functions (E-AHF), is implemented and trained by the data generated by the physics-based models. GA and NSGA-II are applied to perform single- and multi-objective optimisation to achieve maximum CO2 capture rate (CR) and minimum energy consumption (EC) with the optimal values of eight design variables. The R2 for the prediction of CR and EC is higher than 0.96 and the relative errors are lower than 5%. The M4 model has proven to be an efficient way to perform multi-variable and multi-objective optimisation, that significantly reduces computational time and resources while maintaining high prediction accuracy. The trade-off of the maximum CR and minimum EC is presented by the Pareto front, with the optimal values of 0.1014 kg h−1 for CR and 6.1855 MJ kg−1CO2 for EC. The calculated net cost of the most promising S-PBC configuration is around 400 $ t−1CO2, which is about 100 $ t−1CO2 lower than the net cost of current direct air capture (DAC), but compromised by slower CO2 capture rate.