This document contains all publication outcomes of the Sun Grant Regional Feedstock Partnership. For more information, visit www.sungrant.org
Abstract: To ensure effective biomass feedstock provision for large-scale ethanol production, a three-stage supply chain was proposed to include biomass supply sites, centralized storage and preprocessing (CSP) sites, and biorefi nery sites. A GIS-enabled biomass supply chain optimization model (BioScope) was developed to minimize annual biomass-ethanol production costs by selecting the optimal numbers, locations, and capacities of farms, CSPs, and biorefi neries as well as identifying the optimal biomass fl ow pattern from farms to biorefi neries. The model was implemented to study the Miscanthus-ethanol supply chain in Illinois. The results of the baseline case, assuming 2% of cropland is allocated for Miscanthus production, showed that unit Miscanthus-ethanol production costs were $220.6 Mg–1, or $0.74 L–1. Biorefi nery-related costs are the largest cost component, accounting for 48% of the total costs, followed by biomass procurement, transportation, and CSP related costs. The unit Miscanthus-ethanol production costs could be reduced to $198 Mg–1 using 20% of cropland, primarily due to savings in transportation costs. Sensitivity analyses showed that the optimal supply chain confi gurations, including the numbers and locations of supply sites, CSP facilities, and biorefi neries, changed signifi cantly for different cropland usage rates, biomass demands, transportation means, and pre-processing technologies. A supply chain composed of large biorefi neries with the support of distributed CSP facilities was recommended to reduce biofuels production costs. Rail outperformed truck transportation to ship pre-processed biomass. Ground biomass with tapping is the suggested biomass format for the case study in Illinois, while high-density biomass formats are suggested for long distance transportation.
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
candidate power plants are identied using an iterative procedure that locates bioenergy units and establishes the needed cultivated area for biomass collection. Electricity production cost is used as a criterion in the identication of the sites of economically exploited biomass potential. The island of Crete is used as an example of the decision-making analysis. A signicant biomass potential exists that could be economically and competitively harvested. The main parameters that affect the location and number of bioenergy conversion facilities are plant capacity and spatial distribution of the available biomass potential.
United States is experiencing increasing interests in fermentation and anaerobic digestion processes for the production of biofuels. A simple methodology of spatial biomass assessment is presented in this paper to evaluate biofuel production and support the first decisions about the conversion technology applications. The methodology was applied to evaluate the potential biogas and ethanol production from biomass in California and Washington states. Solid waste databases were filtered to a short list of digestible and fermentable wastes in both states. Maximum methane and ethanol production rates were estimated from biochemical and ultimate analysis of each waste and projected on a GIS database. Accordingly, the optimal locations for methane and ethanol production plants were approximately determined.
The available net power for transportation and electricity generation was evaluated considering three process efficiency factors in the waste to power life cycle. The net power from methane and ethanol would ultimately cover ~ 6 - 8% of the transportation needs for motor gasoline or cover ~ 3 - 4% of the electrical power consumption in each state.
The use of Geographic Information Systems (GIS) for understanding the geographic context of bioenergy supplies is discussed and a regional-scale, GIS-based modeling system for estimating potential biomass supplies from energy crops is described. While GIS models can capture geographic variation that may in?uence biomass costs and supplies, GIS models are not likely to handle uncertainty well and are often limited by the lack of spatially explicit data. The presented modeling system estimates the costs and environmental implications of supplying speci?ed amounts of energy crop feedstock across a state. The system considers where energy crops could be grown, the spatial variability in their yield, and transportation costs associated with acquiring feedstock for an energy facility. The modeling system was used to estimate potential switchgrass costs and supplies in eleven US states. Transportation costs increased with increased facility demand and were lowest in Iowa, North Dakota and South Dakota and highest in South Carolina, Missouri, Georgia, and Alabama. Farmgate feedstock costs were lowest in Alabama, North Dakota and South Dakota and highest in Iowa and Nebraska. Across the eleven states, delivered feedstock costs ranged from $33 to $55/dry tonne to supply a facility requiring 100,000 tonne/yr. Delivered feedstock costs for a 630,000 tonne/yr facility ranged from $36 to $58/dry tonne.
When fuelwood is harvested at a rate exceeding natural growth and inefficient conversion technologies are used, negative environmental and socio-economic impacts, such as fuelwood shortages, natural forests degradation and net GHG emissions arise. In this study, we argue that analyzing fuelwood supply/demand spatial patterns require multiscale approaches to effectively bridge the gap between national results with local situations. The proposed methodology is expected to help 1) focusing resources and actions on local critical situations, starting from national wide analyses and 2) estimating, within statistically robust confidence bounds, the proportion of non-renewable harvested fuelwood. Starting from a previous work, we selected a county-based fuelwood hot spot in the Central Highlands of Mexico, identified from a national wide assessment, and developed a grid-based model in order to identify single localities that face concomitant conditions of high fuelwood consumption and insufficient fuelwood resources. By means of a multicriteria analysis (MCA), twenty localities, out of a total of 90, were identified as critical in terms of six indicators related to fuelwood use and availability of fuelwood resources. Fuelwood supply/demand balances varied among localities from 16.2 2.5 Gg y 1 to 4.4 2.6 Gg y 1, while fractions of non-renewable fuelwood varied from 0 to 96%. These results support the idea that balances and non-renewable fuelwood fractions (mandatory inputs for Clean Development Mechanism (CDM) cookstoves projects) must be calculated on a locality by locality basis if gross under or over-estimations want to be avoided in the final carbon accounting.