Small Is Better: Fostering Growth in the Biofuels Industry with Energy Manufacturing

The 2013 Intergovernmental Panel on Climate Change (IPCC) report stresses the need to reduce atmospheric carbon levels to avoid disastrous changes in global climate. Lignocellulosic biofuels are part of emerging strategies to reduce carbon emissions by replacing fossil-derived transportation fuels. We need an industry paradigm shift to have a meaningful impact on the transportation fuel market. Energy manufacturing could foster growth in the biofuels industry by enabling the economic production of small, modular biorefineries.

The topic of this presentation is the role of energy manufacturing in technological innovation for sustainable biofuel production. Energy manufacturing enables faster learning rates. Increasing learning rates has led to drastic cost reductions in feedstock production and conversion to ethanol both in the U.S. and Brazil. Sugarcane production costs have decreased by more than 60% since 1971, and U.S. corn costs have seen a similar reduction. Sugarcane ethanol production costs decreased by about 70% over a similar time period. These cost reductions occurred during a period of rapid expansion in biofuel production. We project that a similar effect will boost the nascent lignocellulosic biofuel industry. Furthermore, investing in energy manufacturing strategies could amplify the impact of learning rates on industry growth.

Most of the biofuel industry growth has been driven by first generation biofuel technologies. In the span of 10 years, global biofuel production increased from 238 thousand barrels per day of oil equivalent (BPDOE) to 1206 thousand BPDOE in 2012. Ethanol production accounted for 78.7% of 2011 global biofuel production, and biodiesel accounted for almost all of the remaining fuel. First generation biorefineries from either corn grain or sugarcane produce virtually all the commercially available ethanol.

The continued growth of the biofuel industry will substantially depend on the commercialization of advanced biofuel technologies, which face significant techno-economic challenges. Advanced biofuel technologies are those capable of converting a wide range of lignocellulosic feedstock into a variety of transportation fuels such as ethanol, gasoline, diesel, and dimethyl-ether. There has been significant investment in research and development of these technologies. However, biofuel production from advanced biorefineries has yet to meet the goals established by government policies. This lack of advanced biofuel production has prompted the U.S. EPA to reduce the annual advanced biofuel targets by more than 90% from the original mandates due to a lack of eligible supply. Recent industry developments suggest that companies may be able to increase production of advanced biofuels albeit at quantities far below the mandated targets. Two of the main constraints limiting advanced biorefinery adoption are high capital and feedstock costs.

Innovative and energy efficient technologies could overcome the high costs of pioneer facilities with sufficient commercial experience. These technologies require significant initial investments that are difficult to justify without knowledge of their earning potential. Increasing their earning potential will depend on cost reductions enabled by the deployment of facilities at their optimal scale and cost reductions from technological learning. Despite the significant literature contributions on biorefinery optimal facility sizes and learning rates, there is scarce information on their interaction particularly for the biofuel industry. This study could lead to coordinated strategies that would result in significant economic savings and rapid technological growth.

We evaluated the impact of learning rates on the optimal scale and production costs of lignocellulosic biorefineries. 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.

During this presentation, we will 1) describe the role of energy manufacturing in technological innovation, 2) discuss the impact of learning rates on the lignocellulosic biofuel industry, and 3) identify advanced energy manufacturing strategies with the greatest impact on biofuel production growth.  The outcome of this work could lead to effective recommendations for engineering investments that accelerate the growth of the lignocellulosic biofuel industry.