The biomass supply chain is one of the most critical elements of large-scale bioenergy production and in many cases a key barrier for procuring initial funding for new developments on specific energy crops. Most productions rely on complex transforming chains linked to feed and food markets. The term 'supply chain' covers various aspects from cultivation and harvesting of the biomass, to treatment, transportation, and storage. After energy conversion, the product must be delivered to final consumption, whether it is in the form of electricity, heat, or more tangible products, such as pellets and biofuels. Effective supply chains are of utmost importance for bioenergy production, as biomass tends to possess challenging seasonal production cycles and low mass, energy and bulk densities. Additionally, the demand for final products is often also dispersed, further complicating the supply chain. The goal of this paper is to introduce key components of biomass supply chains, examples of related modeling applications, and if/how they address aspects related to environmental metrics and management. The paper will introduce a concept of integrated supply systems for sustainable biomass trade and the factors influencing the bioenergy supply chain landscape, including models that can be used to investigate the factors. The paper will also cover various aspects of transportation logistics, ranging from alternative modal and multi-modal alternatives to introduction of support tools for transportation analysis. Finally gaps and challenges in supply chain research are identified and used to outline research recommendations for the future direction in this area of study.
Bioenergy with carbon capture and storage (BECCS) is one strategy to remove CO2 from the atmosphere. To assess the potential scale and cost of CO2 sequestration from BECCS in the US, this analysis models carbon sequestration net of supply chain emissions and costs of biomass production, delivery, power generation, and CO2 capture and sequestration in saline formations. The analysis includes two biomass supply scenarios (near-term and long-term), two biomass logistics scenarios (conventional and pelletized), and two generation technologies (pulverized combustion and integrated gasification combined cycle). Results show marginal cost per tonne CO2 (accounting for costs of electricity and CO2 emissions of reference power generation scenarios) as a function of CO2 sequestered (simulating capture of up to 90% of total CO2 sequestration potential) and associated spatial distribution of resources and generation locations for the array of scenario options. Under a near-term scenario using up to 206 million tonnes per year of biomass, up to 181 million tonnes CO2 can be sequestered annually at scenario-average costs ranging from $62 to $137 per tonne CO2; under a long-term scenario using up to 740 million tonnes per year of biomass, up to 737 million tonnes CO2 can be sequestered annually at scenario-average costs ranging from $42 to $92 per tonne CO2. These estimates of CO2 sequestration potential may be reduced if future competing demand reduces resource availability or may be increased if displaced emissions from conventional power sources are included. Results suggest there are large-scale opportunities to implement BECCS at moderate cost in the US, particularly in the Midwest, Plains States, and Texas.
A typical airlift mission carrying troops and cargo to the Persian Gulf required a three-day round-trip, visited seven or more different airfields, burned almost one million pounds of fuel, and cost $280,000. During Operation Desert Storm, the Military Airlift Command (MAC) averaged more than 100 such missions daily as it managed the largest airlift in history. By August 7, 1991, more than 25,000 missions had moved more than 966,000 passengers and 774,000 tons of cargo to and from the Persian Gulf region. Each mission required scheduling aircraft, crew, and mission support resources to maximize the on-time delivery of cargo and passengers. To meet this challenge, MAC worked with the Oak Ridge National Laboratory to develop and deploy the Airlift Deployment Analysis System (ADANS). Within three months, ADANS provided a set of decision support tools to manage information on cargo and passengers to be moved and the available resources, as well as tools to schedule missions, to analyze the schedule, and to distribute the schedule to MAC's worldwide command and control system.
The Ohio River Navigation Investment Model (ORNIM) estimates the benefits of navigation improvements and balances those estimated benefits against the estimated costs of improvements. The economic assumptions within ORNIM are identified; the rationale for these assumptions is provided; and how these assumptions alter the estimates of inlandwater navigation benefits, as compared with those of the theoretical model, are addressed. ORNIM is a spatially detailed partial equilibrium model that incorporates the following assumptions: ( a) demand for individual movements, provided exogenously, is perfectly inelastic; ( b) willingness to pay (WTP) for individual river movements is equal to the exogenously given least-cost alternative rail rate; and ( c) the supply of rail for individual movements is perfectly elastic at the exogenously given rail rate. The first assumption biases upward estimates of with-project benefits. However, empirical evidence on demand elasticity and WTP suggests that these assumptions are reasonable in the short run. In the long run, decisions to move cargo by water depend only in part on river rates, with environmental and energy policies also being critical. The demand for waterway movements is determined exogenously to ORNIM, and the Corps' recent scenario-based approach to demand projection is laudable. The third assumption unequivocally biases downward ORNIM's estimate of with-project benefits. Future ORNIM enhancements include improvements in analyzing congestion fees, environmental externalities, traffic management, and system reliability as well as improvements in data quantity and quality. ORNIM, like other navigation models, is data constrained. Without significant data improvements, attempts to relax economic assumptions within ORNIM are of questionable value.
The Transportation Storage Logistics (TSL) Model Data Management Manual describes the SQL Server database that is used by the TSL model to house reference data as well as the scenario data. The manual is a reference document, subject to change as the model evolves.
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