biomass

This document contains all publication outcomes of the Sun Grant Regional Feedstock Partnership. For more information, visit www.sungrant.org

In support of the national goals for biofuel use in the United States, numerous technologies have been developed that convert biomass to biofuels. Some of these biomass to biofuel conversion technology pathways are operating at commercial scales, while others are in earlier stages of development. The advancement of a new pathway toward commercialization involves various types of progress, including yield improvements, process engineering, and financial performance. Actions of private investors and public programs can accelerate the demonstration and deployment of new conversion technology pathways. These investors (both private and public) will pursue a range of pilot, demonstration, and pioneer scale biorefinery investments; the most cost-effective set of investments for advancing the maturity of any given biomass to biofuel conversion technology pathway is unknown. In some cases, whether or not the pathway itself will ultimately be technically and financially successful is also unknown. This report presents results from the Biomass Scenario Model—a system dynamics model of the biomass to biofuels system—that estimate effects of investments in biorefineries at different maturity levels and operational scales. The report discusses challenges in estimating effects of such investments and explores the interaction between this deployment investment and a volumetric production incentive. Model results show that investments in demonstration and deployment have a substantial growth impact on the development of the biofuels industry. Results also show that other conditions, such as accompanying incentives, have major impacts on the effectiveness of such investments. This report does not advocate for or against investments, incentives, or policies, but analyzes simulations of their effects.

Vimmerstedt, L. and Bush, B. "Effects of Deployment Investment on the Growth of the Biofuels Industry." Golden, CO: National Renewable Energy Laboratory (December). NREL/TP-6A20-60802

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Biomass Scenario Model Zotero References
National Renewable Energy Laboratory

Biofuels are promoted in the United States through aggressive legislation as one part of an overall strategy to lessen dependence on imported energy as well as to reduce the emissions of greenhouse gases. Meeting mandated volumetric targets has prompted substantial funding for biofuels research, much of it focused on producing ethanol and other fuel types from biomass feedstocks. A variety of incentive programs (including subsidies, fixed capital investment grants, loan guarantees, vehicle choice credits, and aggressive corporate average fuel economy standards)have been developed, but their short-and long-term ramifications are not well known. This paper describes the Biomass Scenario Model, a system dynamics model developed under the support of the U.S. Department of Energy as the result of a multi-year project at the National Renewable Energy Laboratory. The model represents multiple pathways leading to the production of fuel ethanol as well as advanced biofuels such as biomass-based gasoline, diesel, jet fuel, and butanol). This paper details the BSM system dynamics architecture, the design of the supporting database infrastructure, the associated scenario libraries used in model runs, as well as key insights resulting from BSM simulations and analyses.

The production of biobased feedstocks (i.e., plant– or algal-based material use for transportation fuels, heat, power and bioproducts) for energy consumption has been expanding rapidly in recent years. Biomass now accounts for 4.1% of total U.S. primary energy production. Unfortunately, there are considerable knowledge gaps relative to implications of this industry expansion for wildlife.

The Wildlife Society convened an expert committee to analyze the latest scientific literature on the effects of growing, managing, and harvesting feedstocks for bioenergy on wildlife and wildlife habitat, and provide answers to questions and variables affecting bioenergy development and wildlife so that site managers might better predict consequences of managing bioenergy feedstocks.

This Technical Review is organized with respect to an ecosystems approach and tries to identify key biomass management practices within those systems, including agricultural lands and croplands; grassland ecosystems and Conservation Reserve Program (CRP) grasslands; forest ecosystems; and algae and aquatic feedstocks. A PDF of this review can be downloaded for free at the link below.

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.

An efficient and sustainable biomass feedstock production system is critical for the success of the biomass based energy sector. An integrated systems analysis framework to coordinate various feedstock production related activities is, therefore, highly desirable. This article presents research conducted towards the creation of such a framework. A breadth level mixed integer linear programming model, named BioFeed, is proposed that simulates different feedstock production operations such as harvesting, packing, storage, handling and transportation, with the objective of determining the optimal system level configuration on a regional basis. The decision variables include the design/planning as well as management level decisions. The model was applied to a case study of switchgrass production as an energy crop in southern Illinois. The results illustrated that the total cost varied between 45 and 49 $ Mg
1 depending on the collection area and the sustainable biorefinery capacity was about 1.4 Gg d
1. The transportation fleet consisted of 66 trucks and the average utilization of the fleet was 86%. On-farm covered storage of biomass was highly beneficial for the system. Lack of on-farm open storage and centralized storage reduced the system profit by 17% and 5%, respectively. Increase in the fraction of larger farms within the system reduced the cost and increased the biorefinery capacity, suggesting that co-operative farming is beneficial. The optimization of the harvesting schedule led to 30% increase in the total profit. Sensitivity analysis showed that the reduction in truck idling time as well as increase in baling throughput and output density significantly increased the profit.

We present a system dynamics global LUC model intended to examine LUC attributed to biofuel production. The model has major global land system stocks and flows and can be exercised under different food and biofuel demand assumptions. This model provides insights into the drivers and dynamic interactions of LUC, population, dietary choices, and biofuel policy rather than a precise number generator.

The estimation of greenhouse gas (GHG) emissions from a change in land-use and management resulting from growing biofuel feedstocks has undergone extensive – and often contentious – scientific and policy debate. Emergent renewable fuel policies require life cycle GHG emission accounting that includes biofuel-induced global land-use change (LUC) GHG emissions. However, the science of LUC generally, and biofuels-induced LUC specifically, is nascent and underpinned with great uncertainty. We critically review modeling approaches employed to estimate biofuel-induced LUC and identify major challenges, important research gaps, and limitations of LUC studies for transportation fuels. We found LUC modeling philosophies and model structures and features (e.g. dynamic vs. static model) significantly differ among studies. Variations in estimated GHG emissions from biofuel-induced LUC are also driven by differences in scenarios assessed, varying assumptions, inconsistent definitions (e.g. LUC), subjective selection of reference scenarios against which (marginal) LUC is quantified, and disparities in data availability and quality. The lack of thorough sensitivity and uncertainty analysis hinders the evaluation of plausible ranges of estimates of GHG emissions from LUC. The relatively limited fuel coverage in the literature precludes a complete set of direct comparisons across alternative and conventional fuels sought by regulatory bodies and researchers.

Improved modeling approaches, consistent definitions and classifications, availability of high-resolution data on LUC over time, development of standardized reference and future scenarios, incorporation of non-economic drivers of LUC, and more rigorous treatment of uncertainty can help improve LUC estimates in effectively achieving policy goals.

Bioenergy has been recognized as an important source of energy that will reduce nation’s dependency on petroleum, and have a positive impact on the economy, environment, and society. Production of bioenergy is expected to increase. As a result, we foresee an increase in the number of biorefineries in the near future. This paper analyzes logistical challenges with supplying biomass to a biorefinery. We also propose a mathematical model that can be used to design the supply chain and manage the logistics of a biorefinery. Supply chain-design decisions are long-term type of decisions; while logistics management involves medium to short-term decisions. The proposed model coordinates these decisions. The model determines the number, size and location of biorefineries needed to produce biofuel using the available biomass. The model also determines the amount of biomass shipped, processed and inventoried during a time period. Inputs to the model are the availability of biomass feedstock, as well as biomass transportation, inventory and processing costs. We use the State of Mississippi as the testing ground of this model.

Interest in using biomass feedstocks to produce power, liquid fuels, and chemicals in the U.S. is increasing. Central to determining the potential for these industries to develop is an understanding of the location, quantities, and prices of biomass resources. This paper describes the methodology used to estimate biomass quantities and prices for each state in the continental U.S. An Excel™ spreadsheet contains estimates of biomass quantities potentially available in five categories: mill wastes, urban wastes, forest residues, agricultural residues and energy crops. Availabilities are sorted by anticipated delivered price. A presentation that explains how this information was used to support the goal of increasing biobased products and bioenergy 3 times by 2010 expressed in Executive Order 13134 of August 12, 1999 is also available.
Originally available at https://bioenergy.ornl.gov/resourcedata/index.html (Accessed January 7, 2013).