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Much of the fuel ethanol industry’s current interest centers on maximizing ethanol yield and overall profits. This can be achieved by knowing the potential yield of input corn and working to identify what parameters are inhibiting reaching 100% fermentation efficiency. On average, ethanol plants produce 2.82 gallons of ethanol per bushel of corn, as compared to 2.51 gallons per bushel in 1994 (Renewable Fuels Association 2015). With the focus on improved starch production and access, corn quality is one of the best indicators of ethanol yield, as the amount of starch determines the theoretical amount of ethanol. Near-infrared spectroscopy (NIRS) is one such method that can be used to evaluate corn composition and, with an appropriate model, corn composition can be used to predict ethanol yield. Many current models are held back by real world applicability, in that they are restricted to lab-scale validation, direct NIRS calibrations, or proprietary models/equipment. At the commercial level, corporatelyproduced propriety models have been developed by DuPont Pioneer and Monsanto. Neither Monsanto nor DuPont Pioneer’s products are available outside of company databases, and both are only applicable to Foss Infratec units, which left a need for a more universal method. Burgers et al. developed a multiple-linear regression equation for predicting corn ethanol yield based on near-infrared spectroscopy (NIRS) measurements of protein, oil, and density on a 15% moisture basis (Burgers, Hurburgh, and Jane 2009). Unlike corporately-developed models, this equation was intended to function independently of corn hybrid, corn supplier, growing location, and NIRS instrument make/model used, as long as the calibration database was consistent. Iterations of the model were evaluated, and the most current version was chosen to use in the rest of the research. A comparison of the model predicted yield, based on inbound grain composition, and corresponding reported ethanol yield from the same grain was performed to validate the model.

In-plant validation of an ethanol yield prediction equation