The Experts below are selected from a list of 669 Experts worldwide ranked by ideXlab platform

Donghai Wang - One of the best experts on this subject based on the ideXlab platform.

  • sorghum protein extraction by sonication and its relationship to ethanol fermentation
    Cereal Chemistry, 2008
    Co-Authors: Renyong Zhao, Scott R. Bean, Donghai Wang
    Abstract:

    Cereal Chem. 85(6):837–842 The objectives of this research were to develop a rapid method for extracting proteins from mashed and nonmashed sorghum meal using sonication (ultrasound), and to determine the relationships between the levels of extractable proteins and ethanol fermentation properties. Nine grain sorghum hybrids with a broad range of ethanol fermentation efficiencies were used. Proteins were extracted in an alkaline borate buffer using sonication and characterized and quantified by size-exclusion HPLC. A 30-sec sonication treatment extracted a lower level of proteins from nonmashed sorghum meal than extracting the proteins for 24 hr with buffer only (no sonication). However, more protein was extracted by sonication from the mashed samples than from the buffer-only 24-hr extraction. In addition, sonication extracted more polymeric proteins from both the mashed and nonmashed samples compared with the buffer-only extraction method. Confocal laser-scanning microscopy images showed that the web-like protein microstructures were disrupted during sonication. The results showed that there were strong relationships between extractable proteins and fermentation parameters. Ethanol yield increased and conversion efficiency improved significantly as the amount of extractable proteins from sonication of mashed samples increased. The absolute amount of polymeric proteins extracted through sonication were also highly related to ethanol fermentation. Thus, the SE-HPLC area of proteins extracted from mashed sorghum using sonication could be used as an indicator for predicting fermentation quality of sorghum. Sorghum (Sorghum bicolor L. Moench) is a drought-resistant and low-input cereal grain grown throughout the world, and interest in using it for bioindustrial applications is now growing in the United States (Farrell et al 2006). Although currently only ≈2.5% of fuel ethanol is produced from grain sorghum, annual consumption of sorghum by the ethanol industry is steadily increasing from 11.25% in 2004 to 15% in 2005 and 26% in 2006 (Renewable Fuels Association 2005, 2006, 2007). Researchers and ethanol producers have shown that grain sorghum is a viable feedstock (technically acceptable, fits the infrastructure, and can be economically viable) for ethanol, and could make a larger contribution to the nation’s fuel ethanol requirements. Starch and protein are the two major components in sorghum grain. Recent research has shown that starch content is a good indicator of ethanol yield in the dry-grind process but starch content itself could not explain conversion efficiency well (Wu et al 2007). Sorghum varies in protein content from 6 to 18%, with 70–90% of the total protein belonging to the storage proteins (kafirins) (Lookhart et al 2000). According to previous research with 68 sorghum hybrids, a strong negative correlation was observed between ethanol yield and protein content (R 2 = 0.60, P < 0.01) (unpublished data), which is similar to data reported for soft wheat cultivars (Swanston et al 2007). However, multiple linear regression, including both starch and protein content as predictors, verified that protein content did not significantly contribute to ethanol yield (P = 0.395). The effect of protein content on conversion efficiency was statistically significant (P = 0.015) but represented only 8.6% of variation in efficiency (unpublished data).

J. Kaiser - One of the best experts on this subject based on the ideXlab platform.

  • Airborne measurements of the atmospheric emissions from a fuel ethanol refinery
    Journal of Geophysical Research: Atmospheres, 2015
    Co-Authors: J. A. De Gouw, Stuart A. Mckeen, Kenneth C. Aikin, Charles A. Brock, Steven S. Brown, Jessica B. Gilman, Martin Graus, Thomas F. Hanisco, John S. Holloway, J. Kaiser
    Abstract:

    Ethanol made from corn now constitutes approximately 10% of the fuel used in gasoline vehicles in the U.S. The ethanol is produced in over 200 fuel ethanol refineries across the nation. We report airborne measurements downwind from Decatur, Illinois, where the third largest fuel ethanol refinery in the U.S. is located. Estimated emissions are compared with the total point source emissions in Decatur according to the 2011 National Emissions Inventory (NEI-2011), in which the fuel ethanol refinery represents 68.0% of sulfur dioxide (SO2), 50.5% of nitrogen oxides (NOx = NO + NO2), 67.2% of volatile organic compounds (VOCs), and 95.9% of ethanol emissions. Emissions of SO2 and NOx from Decatur agreed with NEI-2011, but emissions of several VOCs were underestimated by factors of 5 (total VOCs) to 30 (ethanol). By combining the NEI-2011 with fuel ethanol production numbers from the Renewable Fuels Association, we calculate emission intensities, defined as the emissions per ethanol mass produced. Emission intensities of SO2 and NOx are higher for plants that use coal as an energy source, including the refinery in Decatur. By comparing with fuel-based emission factors, we find that fuel ethanol refineries have lower NOx, similar VOC, and higher SO2 emissions than from the use of this fuel in vehicles. The VOC emissions from refining could be higher than from vehicles, if the underestimated emissions in NEI-2011 downwind from Decatur extend to other fuel ethanol refineries. Finally, chemical transformations of the emissions from Decatur were observed, including formation of new particles, nitric acid, peroxyacyl nitrates, aldehydes, ozone, and sulfate aerosol.

Vernon R. Eidman - One of the best experts on this subject based on the ideXlab platform.

  • 1 The Evolving Ethanol Industry in the United States
    2015
    Co-Authors: Vernon R. Eidman
    Abstract:

    This paper discusses the likely growth in the production of ethanol from grain in the United States over the next 4 to 5 years. It describes how the costs of production have changed and some of the major factors that are likely to impact profitability and rate of growth in the industry over this period. It discusses co-product production and utilization, and the impact of expanding ethanol production on land use. The final section briefly discusses recent progress in the production of liquid Fuels from cellulose. Growth of BioFuels Ethanol production has grown rapidly in the United States in recent years, increasing from 3.400 billion gallons in 2004 to 3.904 billion gallons in 2005 and 4.855 billion gallons in 2006 (Renewable Fuels Association). It is expected to grow even more rapidly over the 2007 through 2009 period, increasing from about 6.3 billion gallons in 2007 to 9.8 billion gallons in 2008, and to more than 12 billion gallons in 2009 (Krissek). The Renewable Fuels Association reported that the United States has120 biorefineries with 6.187 billion gallons of annual capacity on line on May 22, 2007. They also list an additional 77 plants and 8 expansions with a total capacity of 6.430 billion gallons as “under construction”. Industry contacts confirm the plants under construction will bring ethanol capacity to over 12 billion gallons by September 2008 (Krissek). However, the enthusiasm to invest in a new ethanol plant has waned and major ethanol builders have “open slots ” to begin building plants in 2008. The amount of productio

  • Chapter 3 Ethanol Economics of Dry Mill Plants
    2015
    Co-Authors: Vernon R. Eidman
    Abstract:

    This chapter provides estimates of the cost of building new ethanol plants and the cost of producing ethanol in those plants. Fuel ethanol can be produced from various feedstocks including starch, sugar and cellulose. Ninety-seven percent of ethanol production in the United States is produced from corn and 2.5 percent is produced from grain sorghum, because they are the lowest cost sources of starch that are available in abundant supplies. The remaining 0.5 percent is produced from whey from cheese plants (Renewable Fuels Association). Ethanol produced from other sources of starch (including wheat, barley, rye and potatoes) and sugar (sugar cane and sugar beets) is more costly given the usual market price relationships that exist among these commodities. The methods of producing ethanol from cellulose are not as well developed and result in more expensive ethanol than using corn and grain sorghum as the feedstock. Improvements in conversion technology are expected to reduce these costs over time; making ethanol produced from cellulosic feedstocks (including crop residues, woody species, and energy crops) a major source of fuel at a competitive price. This chapter focuses on the costs of producing fuel ethanol from corn because that is the most commonly used feedstock in Illinois at the current time, and it is likely to be the most economical feedstock for commercial ethano

Benjamin Senauer - One of the best experts on this subject based on the ideXlab platform.

  • how bioFuels could starve the poor
    Foreign Affairs, 2007
    Co-Authors: Ford C Runge, Benjamin Senauer
    Abstract:

    IN 1974, as the United States was reeling from the oil embargo imposed by the Organization of Petroleum Exporting Countries, Congress took the first of many legislative steps to promote ethanol made from corn as an alternative ftuel. On April 18, 1977, amid mounting calls for energy independence, President Jimmy Carter donned his cardigan sweater and appeared on television to tell Americans that balancing energy demands with available domestic resources would be an effort the "moral equivalent of war." The gradual phaseout of lead in the 1970S and 1980s provided an additional boost to the fledgling ethanol industry. (Lead, a toxic substance, is a performance enhancer when added to gasoline, and it was partly replaced by ethanol.) A series of tax breaks and subsidies also helped. In spite of these measures, with each passing year the United States became more dependent on imported petroleum, and ethanol remained marginal at best. Now, thanks to a combination of high oil prices and even more generous government subsidies, corn-based ethanol has become the rage. There were no ethanol refineries in operation in the United States at the end of 2006, according to the Renewable Fuels Association. Many were being expanded, and another 73 were under construction. When

Korte Megan - One of the best experts on this subject based on the ideXlab platform.

  • In-plant validation of an ethanol yield prediction equation
    Iowa State University Digital Repository, 2015
    Co-Authors: Korte Megan
    Abstract:

    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, corporately-produced 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. The slopes for the plants’ predicted and reported ethanol yields did not differ significantly from one another. Overall, the combined model for the linear regression produced a low R2 value (0.23) which shows that a significant amount of variability in the data is not explained by the model. On average, the data validated the prediction model. Day to day or batch by batch variability in processing was not accounted for in the equation, but the variability of the corn composition was. From the linear regression analyses performed on each plant, the slopes are the same, but there is a plant-specific bias. This equation identified key corn quality parameters. Because the equation validated for all plants, the equation is validated to function independently of corn hybrid, corn supplier, growing location, and NIRS instrument make/model used. The model validated with a root mean square error of 0.13 gal/bu, and no difference (0.0008 gal/bu) between overall reported and predicted yield means