Learning rates and their impacts on the optimal capacities and production costs of biorefineries

Industry statistics indicate that technology-learning rates can dramatically reduce both feedstock and biofuel production costs. Both the Brazilian sugarcane ethanol and the United States corn ethanol industries exhibit drastic historical cost reductions that can be attributed to learning factors. Thus, the purpose of this paper is to estimate the potential impact of industry learning rates on the emerging advanced biofuel industry in the United States. Results from this study indicate that increasing biorefinery capital and feedstock learning rates could significantly reduce the optimal size and production costs of biorefineries. This analysis compares predictions of learning-based economies of scale, S-Curve, and Stanford-B models. The Stanford-B model predicts biofuel cost reductions of 55 to 73% compared to base case estimates. For example, optimal costs for Fischer-Tropsch diesel decrease from $4.42/gallon to $2.00/gallon. The optimal capacities range from small-scale (grain ethanol and fast pyrolysis) producing 16 million gallons per year to large-scale gasification facilities with 210 million gallons per year capacity. Sensitivity analysis shows that improving capital and feedstock delivery learning rates has a stronger impact on reducing costs than increasing industry experience suggesting that there is an economic incentive to invest in strategies that increase the learning rate for advanced biofuel production. (c) 2014 Society of Chemical Industry and John Wiley & Sons, Ltd