Biography: Prof. Xiao Wu received his B. S. degree and Ph. D. degree in Energy information and Automation in Southeast University, Nanjing, China, in 2008 and 2014. He joined Southeast University in 2014 as assistant professor in School of Energy and Environment, was promoted to associate professor in 2019. Since 2019, he has been a visiting professor in Department of Chemical and Biological Engineering in the University of Sheffield, UK.
Prof. Wu has been investigators in over 20 research grants with funding from European Union, Royal Society, NSFC, MOST and Industry. He has published over 100 peer-reviewed papers including Applied Energy, Energy, Fuel, IEEE Trans. Energy Conversion. He has been awarded Jiangsu Outstanding Youth Foundation, Excellent young scholar of Jiangsu Engineering Thermophysics Society, Royal Society-Sino-British Trust International Fellow, Young Science and Technology Talents of Jiangsu Province in the last three years. His main research area is in Process Systems Engineering for Energy and Environment, including Process Modelling, Simulation, Control and Optimization, Big Data and Artificial Intelligence (AI), Power Plants, Carbon Capture, Utilization and Storage (CCUS) and Integrated Energy System (IES).
Speech Title: Dynamic Modelling, Identification and Advanced Control of Solvent Based Carbon Capture for Power Plants
Abstract: Solvent-based post-combustion CO2 capture (PCC) appears to be the most effective choice to overcome the CO2 emission issue of fossil fuel fired power plants. To make the PCC better suited for power plants, growing interest has been directed to the flexible operation of PCC in the past ten years. The flexible operation requires the PCC system to adapt to the strong flue gas flow rate change and to adjust the carbon capture level rapidly in wide operating range. In-depth study of the dynamic characteristics of the PCC process and developing a suitable control approach are the keys to meet this challenge. This talk introduces the state-of-the-art studies of modelling, data-driven system/process identification and control design to improve the flexibility of the carbon capture system in future low carbon energy mix.