Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply
This study develops a predictive model to optimize biomass blends for commercial-scale biorefinery processing
This study explores the viability of streptomyces venezualae as a platform organism for large-scale cellulosic biofuel production
Mixed feedstocks can help reduce the risk associated with feedstock availability for bio-based production of fuels and chemicals.
Two case studies exploring conversion technologies for municipal solid waste. One is a partnership INL, and the other is a collaboration with FATER.
Switchgrass with 10% and 20% dry matter loss and corn stover with 30% dry matter loss achieved higher sugar yields during biomass conversion
Predictive modeling was used to evaluate and optimize traditional pretreatment methods for biomass mixture compositions to maximize sugar yield and minimize furfural production.
Municipal solid waste and corn stover blends present great potential to meet quality and cost requirements for sugar conversion.
The collaboration with INL and SNL demonstrated 600-fold (10mL to 6L) scale up of MSW/CS blends.
In collaboration with Muufri, a fed-batch process to express milk proteins through Pichia species was optimized and scaled to 3L