Summary: My research will focus on process synthesis, intensification, machine learning and molecular modeling to address current open questions in Energy and Sustainability. Potential collaborations will be sought from experts in machine learning, catalysis, reaction engineering, molecular modeling, separation and bioengineering.
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Data-driven Energy Systems Innovation
Advanced machine learning techniques exhibit potential to systematically explore innovative energy systems. An energy system representation method that is data-driven and machine-readable shows promise. My future research will address the following two questions: (1) How can we develop an energy-system representation method that allows data-driven innovation? (2) How can we facilitate the exploration of energy-system innovations while meeting multiple design objectives?
Valorization of Renewable Feedstock and Plastic
Integration of circular economy technologies in the biomass utilization, carbon capture, and plastic management sectors can further reduce carbon emissions and processing costs compared with their isolated counterparts. In this regard, the following research questions exist: Which intermediate products can bridge the biorefineries and plastic depolymerization? Which techniques should be used for optimal biomass utilization, carbon capture, and plastic management? How can biomass and plastic waste be further valorized?
Integrated Product and Process Intensification
Molecular and product design are intertwined with process intensification. To this end, we will develop a framework that integrates superstructure optimization and computer-aided molecular design. Particularly, the superstructure will be constructed in a stage-by-stage manner, and the pairwise phenomena interactions of each stage will be enumerated. For the reaction phenomena at each stage, we will consider the molecular design subproblems for designing reaction products, whereby discrete decisions are made regarding the selection of functional groups (or union of functional groups) and their connections. Finally, through molecular dynamics simulations, we will validate these optimally designed products to elucidate the molecular interactions that govern the products' properties. To account for chemical systems that existing force-field parameterization does not predict well (e.g., charged systems involving polarization and reactive systems), we intend to explore other forms of potential function and develop a new parameterization by integrating Bayesian optimization, experimental data, and molecular dynamics simulations.