Simultaneous application of predictive model and least cost formulation can substantially benefit biorefineries outside Corn Belt in United States: A case study in Florida. (January 2019) - Narani, Akash; Konda, N.V.S.N. 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
Graphical abstract: Highlights: Biomass blend predictions from 10% (w/w) solid loading were applicable at 30%. Impact analysis demonstrated the simultaneous application of LCF and predictive model. Lignocellulosic sugars from biomass blends converted to ethanol at 100% (of theoretical) yield. Techno-economic analysis validated industrial application of predictive model. 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.