KDF Search Results

Reducing dependence on fossil‐based energy has raised interest in biofuels as a potential energy source, but concerns have been raised about potential implications for water quality. These effects may vary regionally depending on the biomass feedstocks and changes in land management. Here, we focused on the Tennessee River Basin (TRB), USA.

Discussions of alternative fuel and propulsion technologies for transportation often overlook the infrastructure required to make these options practical and cost-effective. We estimate ethanol production facility locations and use a linear optimization model to consider the economic costs of distributing various ethanol fuel blends to all metropolitan areas in the United States. Fuel options include corn-based E5 (5% ethanol, 95% gasoline) to E16 from corn and switchgrass, as short-term substitutes for petroleum-based fuel.

When the lignocellulosic biofuels industry reaches maturity and many types of biomass sources become economically viable, management of multiple feedstock supplies – that vary in their yields, density (tons per unit area), harvest window, storage and seasonal costs, storage losses, transport distance to the production plant – will become increasingly important for the success of individual enterprises. The manager’s feedstock procurement problem is modeled as a multi-period sequence problem to account for dynamic management over time.

This study focuses on the simulation of a complete process for producing butanol via
acetone, butanol, and ethanol corn fermentation.

We assessed the life-cycle energy and greenhouse gas (GHG) emission impacts of the following three soybean-derived fuels by expanding, updating, and using Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model: (1) biodiesel produced from soy oil transesterification, (2) renewable diesel produced from hydrogenation of soy oil by using two processes (renewable diesel I and II), and (3) renewable gasoline produced from catalytic cracking of soy oil.

We assessed current water consumption during liquid fuel production, evaluating major steps of fuel lifecycle for five fuel pathways: bioethanol from corn, bioethanol from cellulosic feedstocks, gasoline from U.S. conventional crude obtained from onshore wells, gasoline from Saudi Arabian crude, and gasoline from Canadian oil sands.

The purpose of this study is to analyse the economical and environmental performance of switchgrass and miscanthus production and supply chains in the European Union (EU25), for the years 2004 and 2030. The environmental performance refers to the greenhouse gas (GHG) emissions, the primary fossil energy use and to the impact on fresh water reserves, soil erosion and biodiversity. Analyses are carried out for regions in five countries.

A dry-grind ethanol from corn process analysis is performed. After defining a complete model of the process, a pinch technology analysis is carried out to optimise energy and water demands. The so-defined base case is then discussed in terms of production costs and process profitability. A detailed sensitivity analysis on the most important process and financial variables is carried out. The possibility to adopt different alternatives for heat and power generation combined to the process is evaluated.

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 model is a vehicle fuel-cycle model for transportation systems. The model provides a set of outcomes that would involve feedstock production, biorefinery production, storage and consumer demand as the complete fuel-cycle. The data is internal to the model, but might be adaptive to different biofuels specifications. This model was developed by the Energy Systems Division at Argonne National Laboratory.