Current Research Projects
Feedstock to Function tool
Funded by DOE's Bioenergy Technologies Office, this project aims to develop the foundation for an adaptive computational tool that predicts bioproduct and biofuel properties for validation and certification, and determines the cost, benefits, and risk of promising new and uncertified pathways and their blending effects. This ‘Feedstock to Function’ tool will incorporate supervised machine learning to predict desired properties of high-potential biobased molecules early in technology validation and certification process. Coupled with a lightweight technoeconomic and life-cycle assessment model, this tool will enable bioproduct and biofuel developers and researchers to streamline bioproduct and biofuel scale-up, overcome experimentally and kinetically derived property bottlenecks, identify cost and emissions bottlenecks, and potentially de-risk investments needed to scale up fuel production for the technology certification process.
Past Research Projects
DOE's Vehicle Technologies Office Co-Optima project will identify improved fuels that in combination with new engine designs will reduce petroleum reduction through a) greater overall efficiency and b) substitution of lower-GHG-lifecycle fuels for current market fuels. The Co-Optima project is a collaborative project between nine national laboratories, multiple universities, and industries. LBNL's role was to provide rapid feedback of measured fundamental thermodynamic and combustion properties to enable metabolic engineering and process optimization for quality assurance, thus streamlining the process for creating and producing a viable, sustainable biofuel for advanced property testing and engine testing conducted by other national laboratories. We initiated this process by measuring laminar flame speed of biofuels identified by the Optima Low Greenhouse Gas Fuels Team. Laminar flame speed is a low-volume, fundamental combustion property that has translational value across multiple fuels and is applicable to multiple engine platforms and scales. It is used to predict turbulent flame speed as well as validate and expand combustion models for multiple engine platforms, thus supporting understanding and interpretation of engine combustion performance.