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Bioresource Technology
Article . 2017 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply

Authors: Todd Pray; Damon Hartley; James Gardner; Chenlin Li; Chenlin Li; Phil Coffman; Deepti Tanjore; +5 Authors

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

Abstract

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.

Countries
United States, Australia
Keywords

330, Agricultural biotechnology, Carbohydrates, Industrial biotechnology, Microbiology, Lignin, Zea mays, Industrial Biotechnology, 2305 Environmental Engineering, Affordable and Clean Energy, Biomass, Least cost formulation, 660, 1502 Bioengineering, Sustainability and the Environment, Feedstock blends, Hydrolysis, Biological Sciences, Lignocellulosic biomass, 620, 2105 Renewable Energy, 2311 Waste Management and Disposal, Predictive model, Pretreatment and enzymatic hydrolysis, Biotechnology

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