Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply

• Published in: Bioresource technology 2017-11, Vol.243 (C), p.676-685
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: 09 BIOMASS FUELS; Biomass; Carbohydrates; Feedstock blends; Hydrolysis; Least cost formulation; Lignin; Lignocellulosic biomass; Predictive model; Pretreatment and enzymatic hydrolysis; Zea mays
• ISSN: 0960-8524; 1873-2976
• DOI: 10.1016/j.biortech.2017.06.156

[Display omitted] •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. Commercial-scale bio-refineries are designed to process 2000tons/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 14.8h. By using ionic liquid to pretreat an equal part blend of the biomass feedstocks at 160°C for 2.2h, we achieved 87.6% glucose yield. Such a predictive model can potentially overcome dependence on a single feedstock.