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Open Access Publications from the University of California

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

  • Author(s): Narani, Akash;
  • Coffman, Phil;
  • Gardner, James;
  • Li, Chenlin;
  • Ray, Allison E;
  • Hartley, Damon S;
  • Stettler, Allison;
  • Konda, NVSN Murthy;
  • Simmons, Blake;
  • Pray, Todd R;
  • Tanjore, Deepti
  • et al.

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.

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