Dr Jie Li is a Senior Lecturer in the Centre for Process Integration, Department of Chemical Engineering at The University of Manchester since. He is also a member of EPSRC Peer Review College. He has expertise in mathematical modelling, machine learning, data-driven optimisation and their applications in process synthesis, design, integration and intensification, energy savings, CO2 capture and utilisation, pressure swing adsorption, hydrogen energy production, renewable energy storage and utilisation, advanced process planning and smart scheduling, and integrated material design and process design. He has published over 90 peer-reviewed journal papers and refereed conference proceedings, mostly in leading journals of chemical engineering and process systems engineering. He is an editorial board member of several academic journals including processes, Frontiers in Chemical Engineerign, Computers and Applied Chemistry, and Energy Storage and Saving. He has received several awards such as EPSRC New Investigator Award, Best poster award at ChemEngDayUK22 (with Xi Cheng), Top 20 most viewed presentation award, PRES'20, Best poster award of 26th CSCST-SCI (with Yingjie Ma), and "One-hundred Talents“ of the Institute of Process Engineering, Chinese Academy of Sciences 2011. 

Our research interests are in the domain of Process Systems Engineering (PSE), which lies at the interface of chemical engineering, applied mathematics/operations research, and computer science. Traditionally, the scope of PSE is restricted to developing efficient mathematical models and solution algorithms for analysis, design, synthesis, and optimization of chemical processes, operations and supply chain network with main objectives of maximizing profit margin or minimizing total cost. Nowadays, the focus of PSE has extended to addressing energy, renewability, sustainability and environment issues. Our research is to develop efficient modeling techniques, optimization methods and computational tools for large-scale complex chemical systems and applies these techniques, methods and tools to support PSE new applications in energy efficiency, water management, wastewater treatment, and carbon capture. We have the following research topics:

• Data-driven optimisation of large-scale complex systems
• Machine-learning based process synthesis, design, integration, and intensification
• Multi-scale modeling and global optimization of industrial energy systems
• Renewable energy storage
• Industry 4.0: Advanced planning and AI-based smart scheduling
• Carbon capture and utilisation
• Integrated material design and process design
• Robust optimization

• Process Development and Integration