Computational Modeling of Renewable Energy

Computational Modeling of Renewable Energy
Seonah Kim
Colorado State University, USA. E-mail : Seonah.Kim(ELIMINAR)@colostate.edu

The overarching goal of our group is to develop new methods to extract sustainable fuels and chemicals from plants. Our approach has been to develop and apply computational tools to both biological and chemical conversion processes as part of an iterative 'model-validate-predict' design process for de novo catalysts.
With its high carbon and hydrogen content, lignocellulosic biomass presents an alternative to petroleum as a nearly carbon-neutral precursor to upgraded liquid fuels. I will present some representative results in designing new catalysts for biological and chemocatalytic processes of biomass.
Our group has introduced a "Fuel property first" design approach to reduce emissions and increase performance. Traditional approaches for developing these mechanistic models require many years for each new molecule, a pace that is poorly suited to the large-scale search for new bioderived blendstocks. We have developed a quantitative structure-property relationship (QSPR) model for sooting tendency based on the experimental yield sooting index (YSI), developed by collaborators at Yale (Prof. L. Pfefferle and Dr. C. McEnally). This is the first fuel property predictive tool using ML (Machine Learning) approaches in combustion research. We have started to build kinetic mechanisms of soot precursor formation during combustion using DFT and flow reactor experiments (collaboration with Dr. R. McCormick, NREL) to show how the fundamental chemistry affects this practical engineering problem..