Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply. (November 2017) - Narani, Akash; Coffman, Phil; Gardner, James; Li, Chenlin; Ray, Allison E.; Hartley, Damon S.; Stettler, Allison; Konda, N.V.S.N. Murthy; Simmons, Blake; Pray, Todd R.; Tanjore, Deepti
Graphical abstract: Highlights: Feedstock blending can enable nationwide production of biofuels. Predictive model can identify ideal blend ratios to achieve high sugar yields. A low ratio of high-quality feedstock can substantially improve sugar yields. Abstract: Commercial-scale bio-refineries are designed to process 2000 tons/day of single lignocellulosic biomass. Several geographical areas in the United States generate diverse feedstocks that, when combined, can be substantial for bio-based manufacturing. Blending multiple feedstocks is a strategy being investigated to expand bio-based manufacturing outside Corn Belt. In this study, we developed a model to predict continuous envelopes of biomass blends that are optimal for a given pretreatment condition to achieve a predetermined sugar yield or vice versa. For example, our model predicted more than 60% glucose yield can be achieved by treating an equal part blend of energy cane, corn stover, and switchgrass with alkali pretreatment at 120 °C for 14.8 h. By using ionic liquid to pretreat an equal part blend of the biomass feedstocks at 160 °C for 2.2 h, we achieved 87.6% glucose yield. Such a predictive model can potentially overcome dependence on a single feedstock.