KDF Search Results

Production costs of bio-ethanol from sugarcane in Brazil have declined continuously over the last three decades. The aims of this study are to determine underlying reasons behind these cost reductions, and to assess whether the experience curve concept can be used to describe the development of feedstock costs and industrial production costs. The analysis was performed using average national costs data, a number of prices (as a proxy for production costs) and data on annual Brazilian production volumes.

In the last decade biofuel production has been driven by governmental policies. This article reviews the national strategy plans of the world’s leading producers. Particular attention is dedicated to blending targets, support schemes and feedstock use. Individual country profiles are grouped by continent and include North America (Canada and the US), South America (Argentina, Brazil, and Colombia), Europe (the European Union, France, and Germany), Asia (China, India, Indonesia, Malaysia, and Thailand) and Australia.

The important key technologies required for the successful biological conversion of lignocellulosic biomass to ethanol have been extensively reviewed. The biological process of ethanol fuel production utilizing lignocellulose as substrate requires: (1) delignification to liberate cellulose and hemicellulose from their complex with lignin, (2) depolymerization of the carbohydrate polymers (cellulose and hemicellulose) to produce free sugars, and (3) fermentation of mixed hexose and pentose sugars to produce ethanol.

The rapidly expanding biofuel industry has changed the fundamentals of U.S. agricultural commodity markets. Increasing ethanol and biodiesel production has generated a fast-growing demand for corn and soybean products, which competes with the well-established domestic livestock industry and foreign buyers. Meanwhile, the co-products of biofuel production are replacing or displacing coarse grains and oilseed meal in feed rations for livestock.

Prior studies have estimated that a liter of bioethanol requires 263−784 L of water from corn farm to fuel pump, but these estimates have failed to account for the widely varied regional irrigation practices. By using regional time-series agricultural and ethanol production data in the U.S., this paper estimates the state-level field-to-pump water requirement of bioethanol across the nation. The results indicate that bioethanol’s water requirements can range from 5 to 2138 L per liter of ethanol depending on regional irrigation practices.

There is a strong societal need to evaluate and understand the sustainability of biofuels, especially because of the significant increases in production mandated by many countries, including the United States. Sustainability will be a strong factor in the regulatory environment and investments in biofuels. Biomass feedstock production is an important contributor to environmental, social, and economic impacts from biofuels.

The water consumption and agrochemical use during biofuel production could adversely impact both availability and quality of a precious resource.

In this paper, we assess what is known or anticipated about environmental and sustainability factors associated with next-generation biofuels relative to the primary conventional biofuels (i.e., corn grain-based ethanol and soybean-based diesel) in the United States during feedstock production and conversion processes. Factors considered include greenhouse (GHG) emissions, air pollutant emissions, soil health and quality, water use and water quality, wastewater and solid waste streams, and biodiversity and land-use changes.

A method is presented, which estimates the potential for power production from agriculture residues. A GIS decision support system (DSS) has been developed, which implements the method and provides the tools to identify the geographic distribution of the economically exploited biomass potential. The procedure introduces a four level analysis to determine the
theoretical, available, technological and economically exploitable potential. The DSS handles all possible restrictions and