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Simultaneous application of predictive model and least cost formulation can substantially benefit biorefineries outside Corn Belt in United States: A case study in Florida.

  • Author(s): Narani, Akash
  • Konda, NVSN Murthy
  • Chen, Chyi-Shin
  • Tachea, Firehiwot
  • Coffman, Phil
  • Gardner, James
  • Li, Chenlin
  • Ray, Allison E
  • Hartley, Damon S
  • Simmons, Blake
  • Pray, Todd R
  • Tanjore, Deepti
  • et al.
Abstract

Previously, a predictive model was developed to identify optimal blends of expensive high-quality and cheaper low-quality feedstocks for a given geographical location that can deliver high sugar yields. In this study, the optimal process conditions were tested for application at commercially-relevant higher biomass loadings. We observed lower sugar yields but 100% conversion to ethanol from a blend that contained only 20% high-quality feedstock. The impact of applying this predictive model simultaneously with least cost formulation model for a biorefinery location outside of the US Corn Belt in Lee County, Florida was investigated. A blend ratio of 0.30 EC, 0.45 SG, and 0.25 CS in Lee County was necessary to produce sugars at high yields and ethanol at a capacity of 50 MMGY. This work demonstrates utility in applying predictive model and LCF to reduce feedstock costs and supply chain risks while optimizing for product yields.

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