Impact of distributed storage and pre‐processing on Miscanthus production and provision systems

Shastri, Yogendra; Rodríguez, L. F.; Hansen, Alan C.; Ting, K. C.

doi:10.1002/bbb.326

Cited by 25 publications

(14 citation statements)

References 14 publications

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“…The study concluded that the commodity system's GHG emissions are about 4% lower, while consuming approximately the same total energy as the conventional system. The study also emphasized that the processing technology was critical to cost-reduction for the commodity system, a point also addressed by Shastri et al [16] and Uria-Martinez et al [17]. Recently, Argo et al [18] evaluated several environmental sustainability impacts (100-year global warming potential (GWP), rainfall (green water) and groundwater through irrigation (blue water) footprints), and costs for advanced logistics designs that employ densification steps for preprocessing agricultural residues and grasses in depots for long-haul transport to centralized biorefineries designed on the biochemical platform.…”

Section: Introductionmentioning

confidence: 77%

“…If the benefits of handling and storing pellets are quantified, the transportation cost savings can be increased and thus can balance or exceed the cost of densification. However, the total cost of the commodity supply chain system could be higher than the conventional system due to the addition of preprocessing operations and equipment [16,27]. Our objective is to evaluate the spatial variability of life cycle environmental impacts owing to characteristics along the biomass feedstock supply chain (i.e., from field to depot to biorefinery with intermediate transportation and preprocessing steps) that incur variability as a result of the quantity of biomass harvested, collected, stored, moved, and preprocessed prior to long-distance transport to a centralized biorefinery.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in Kansas

et al. 2014

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To meet Energy Independence and Security Act (EISA) cellulosic biofuel mandates, the United States will require an annual domestic supply of about 242 million Mg of biomass by 2022. To improve the feedstock logistics of lignocellulosic biofuels in order to access available biomass resources from areas with varying yields, commodity systems have been proposed and designed to deliver quality-controlled biomass feedstocks at preprocessing "depots". Preprocessing depots densify and stabilize the biomass prior to long-distance transport and delivery to centralized biorefineries. The logistics of biomass commodity supply chains could introduce spatially variable environmental impacts into the biofuel life cycle due to needing to harvest, move, and preprocess biomass from multiple distances that have variable spatial density. This study examines the uncertainty in greenhouse gas (GHG) emissions of corn stover logistics within a bio-ethanol supply chain in the state of Kansas, where sustainable biomass supply varies spatially. Two scenarios were evaluated each having a different number of depots of varying capacity and location within Kansas relative to a central commodity-receiving biorefinery to test GHG emissions uncertainty. The first scenario sited four preprocessing depots evenly across the state of Kansas but within the vicinity of counties having high biomass supply density. OPEN ACCESSEnergies 2014, 7 7126The second scenario located five depots based on the shortest depot-to-biorefinery rail distance and biomass availability. The logistics supply chain consists of corn stover harvest, collection and storage, feedstock transport from field to biomass preprocessing depot, preprocessing depot operations, and commodity transport from the biomass preprocessing depot to the biorefinery. Monte Carlo simulation was used to estimate the spatial uncertainty in the feedstock logistics gate-to-gate sequence. Within the logistics supply chain GHG emissions are most sensitive to the transport of the densified biomass, which introduces the highest variability (0.2-13 g CO2e/MJ) to life cycle GHG emissions. Moreover, depending upon the biomass availability and its spatial density and surrounding transportation infrastructure (road and rail), logistics can increase the variability in life cycle environmental impacts for lignocellulosic biofuels. Within Kansas, life cycle GHG emissions could range from 24 g CO2e/MJ to 41 g CO2e/MJ depending upon the location, size and number of preprocessing depots constructed. However, this range can be minimized through optimizing the siting of preprocessing depots where ample rail infrastructure exists to supply biomass commodity to a regional biorefinery supply system.

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Section: Introductionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in Kansas

et al. 2014

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“…Multi-commodity flow mathematical model Impact of distributed storage and pre-processing on Miscanthus production and provision systems Shastri et al [15] Analyze the cost reduction from implementing distributed storage and pre-processing at satellite storage locations. Feedstock: Miscanthus.…”

Section: Discrete Event Simulation √mentioning

confidence: 99%

“…The scope of the formulation covers from the feedstock suppliers to the biofuel customers. Shastri et al [15] formulate a MILP model (e.g., BioFeed) to maximize the profit of the system (i.e., Miscanthus production and provision system). Other papers such as Larasati et al [17] explain the dynamics between competing performance measures, such as the trade-off between switchgrass transportation cost and the economic scale of cellulosic ethanol production.…”

Section: Discrete Event Simulation √mentioning

confidence: 99%

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Development of the IBSAL-SimMOpt Method for the Optimization of Quality in a Corn Stover Supply Chain

2017

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Abstract:Variability on the physical characteristics of feedstock has a relevant effect on the reactor's reliability and operating cost. Most of the models developed to optimize biomass supply chains have failed to quantify the effect of biomass quality and preprocessing operations required to meet biomass specifications on overall cost and performance. The Integrated Biomass Supply Analysis and Logistics (IBSAL) model estimates the harvesting, collection, transportation, and storage cost while considering the stochastic behavior of the field-to-biorefinery supply chain. This paper proposes an IBSAL-SimMOpt (Simulation-based Multi-Objective Optimization) method for optimizing the biomass quality and costs associated with the efforts needed to meet conversion technology specifications. The method is developed in two phases. For the first phase, a SimMOpt tool that interacts with the extended IBSAL is developed. For the second phase, the baseline IBSAL model is extended so that the cost for meeting and/or penalization for failing in meeting specifications are considered. The IBSAL-SimMOpt method is designed to optimize quality characteristics of biomass, cost related to activities intended to improve the quality of feedstock, and the penalization cost. A case study based on 1916 farms in Ontario, Canada is considered for testing the proposed method. Analysis of the results demonstrates that this method is able to find a high-quality set of non-dominated solutions.

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Lignocellulosic biomass feedstock transportation alternatives, logistics, equipment configurations, and modeling

Miao

Shastri

Grift

et al. 2012

Biofuels Bioprod Bioref

123

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Lignocellulosic biomass feedstock transportation bridges biomass production, transformation, and conversion into a complete bioenergy system. Transportation and associated logistics account for a major portion of the total feedstock supply cost and energy consumption, and therefore improvements in transportation can substantially improve the cost‐competitiveness of the bioenergy sector as a whole. The biomass form, intended end use, supply and demand locations, and equipment and facility availability further affect the performance of the transportation system. The sustainability of the delivery system thus requires optimized logistic chains, cost‐effective transportation alternatives, standardized facility design and equipment configurations, efficient regulations, and environmental impact analysis. These issues have been studied rigorously in the last decade. It is therefore prudent to comprehensively review the existing literature, which can then support systematic design of a feedstock transportation system. The paper reviews the major transportation alternatives and logistics and the implementation of those for various types of energy crops such as energy grasses, short‐rotation woody coppices, and agricultural residue. It emphasizes the importance of performance‐based equipment configuration, standard regulations, and rules for calculating transport cost of delivery systems. Finally, the principles, approaches, and further direction of lignocellulosic feedstock transportation modeling are reviewed and analyzed. © 2012 Society of Chemical Industry and John Wiley & Sons, Ltd

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Impact of distributed storage and pre‐processing on Miscanthus production and provision systems

Cited by 25 publications

References 14 publications

Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in Kansas

Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in Kansas

Development of the IBSAL-SimMOpt Method for the Optimization of Quality in a Corn Stover Supply Chain

Lignocellulosic biomass feedstock transportation alternatives, logistics, equipment configurations, and modeling

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