Xiangzhou Yuan
Prof. Xiangzhou Yuan is a Full Professor at the School of Energy and Environment in Southeast University, Nanjing, China. His academic background covers thermochemical valorization of biomass and organic waste, carbon capture and utilization, syntheses and applications of advanced engineered biochar, life-cycle sustainability assessment (LCSA). He has published over 80 refereed journal articles including Nat. Rev. Earth Environ., Matter, Environ. Sci. Technol. Dr. Yuan registered 8 patents and achieved 2 technology transfer (KR10-2197821 & KR10-1650191). He is active in serving as a Program Leader of the Association of Pacific Rim Universities Sustainable Waste Management (APRU SWM) Program from 2024, the R&D Director of Sun Brand Industrial Inc. from 2020, and an Academic Committee Member of the International Cooperation Research Centre of Carbon Capture in Ultra-low Energy-consumption, Tianjin, China from 2018. Moreover, he has been invited to deliver keynote and invited speeches for over 20 international conferences in energy, engineering, and environmental fields. He also serves as Guest Editors (Chemical Engineering Journal, Applied Energy, Advances in Applied Energy, etc.), and Youth Editorial Board Members (Biochar, Carbon Research, Resources Chemicals and Materials, etc.).
Presentation title: Machine Learning-based Guided Synthesis of Engineered Biochar for High-performance CO2 Capture
Abstract: Biomass waste-derived engineered biochar for CO2 capture presents a viable route for climate change mitigation and sustainable waste management. However, traditional synthesis approaches for developing engineered biochar materials with high-performance CO2 capture performance are both time- and labor-intensive, and the underlying mechanism for CO2 adsorption is still challenging to design textural properties and functional groups of engineered biochar. Therefore, we first applied machine learning to systematically map CO2 adsorption as a function of the textural and compositional properties of engineered biochar materials and adsorption parameters, and then devised an active learning strategy to guide and expedite their synthesis with improved CO2 adsorption capacities. Finally, we demonstrated a data-driven workflow to accelerate the development of high-performance engineered biochar with enhanced CO2 uptake and broader applications as functional materials.