| Autor: |
Narani, A, Coffman, P, Gardner, J, Li, C, Ray, AE, Hartley, DS, Stettler, A, Konda, NVSNM, Simmons, B, Pray, TR, Tanjore, D |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Zdroj: |
Narani, A; Coffman, P; Gardner, J; Li, C; Ray, AE; Hartley, DS; et al.(2017). Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply. Bioresource Technology, 243, 676-685. doi: 10.1016/j.biortech.2017.06.156. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/21g9m6hg |
| Popis: |
© 2017 Elsevier Ltd 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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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