Impact of Harvest Equipment on Ash Variability of Baled Corn Stover Biomass for Bioenergy

Bonner, Ian J.; Smith, William A.; Einerson, Jeffery J.; Kenney, Kevin L.

doi:10.1007/s12155-014-9432-x

Cited by 41 publications

(50 citation statements)

References 20 publications

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“…Bioref. 12:624-648 (2018); DOI: 10.1002/bbb yield, capacity, and efficiency of harvest and collection equipment, moisture content, and ash content have been estimated based on published data, 20,[32][33][34][35] data from field trials, [36][37][38] and data taken from the INL Bioenergy Feedstock Library. 27…”

Section: Harvest and Collectionmentioning

confidence: 99%

Optimal blending management of biomass resources used for biochemical conversion

Roni

Thompson

Hartley

et al. 2018

Biofuels Bioprod Bioref

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This research develops an optimization model to describe the tradeoff among blend components in the least‐cost biomass blend, based on resource availability, quality requirements, and logistics cost for a biochemical conversion. A mixed‐integer linear programming model is developed to determine the least‐cost blend from a set of candidate feedstocks. A case study – based on a biorefinery located in western Kansas that uses three‐pass corn stover, two‐pass corn stover, switchgrass, miscanthus, and municipal solid waste fractions to meet biochemical conversion specifications and feedstock demand – shows that the delivered cost of an optimal blend that meets carbohydrate and ash specifications is 12.12% higher than the delivered cost of optimal blend that meets a carbohydrate specification only. The results indicate that a least‐cost blend that meets both carbohydrate and ash specifications consists of miscanthus (48.2%) and switchgrass (29.4%) whereas the least‐cost blend meeting carbohydrate specification only comprises three‐pass corn stover (55.4%) and two‐pass corn stover (20.4%). An optimal blend uses a low‐cost municipal solid waste fraction in all cases, implying that blending could be a potential strategy to reduce delivered feedstock cost. Published 2018. This article is a U.S. Government work and is in the public domain in the USA. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

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Section: Harvest and Collectionmentioning

confidence: 99%

Optimal blending management of biomass resources used for biochemical conversion

Roni

Thompson

Hartley

et al. 2018

Biofuels Bioprod Bioref

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“…Combined hand and commercial harvesting should be performed to assess the quantity of entrained soil collected by commercial harvest systems [69]. Differences in total ash content for corn stover harvested in Kansas due to differences in harvesting equipment were between 11.5 and 28.2 % [80].…”

Section: Variability Due To Harvest Conditionsmentioning

confidence: 99%

Sources of Biomass Feedstock Variability and the Potential Impact on Biofuels Production

et al. 2015

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Terrestrial lignocellulosic biomass has the potential to be a carbon neutral and domestic source of fuels and chemicals. However, the innate variability of biomass resources, such as herbaceous and woody materials, and the inconsistency within a single resource due to disparate growth and harvesting conditions, presents challenges for downstream processes which often require materials that are physically and chemically consistent. Intrinsic biomass characteristics, including moisture content, carbohydrate and ash compositions, bulk density, and particle size/shape distributions are highly variable and can impact the economics of transforming biomass into value-added products. For instance, ash content increases by an order of magnitude between woody and herbaceous feedstocks (from ∼0.5 to 5 %, respectively) while lignin content drops by a factor of two (from ∼30 to 15 %, respectively). This increase in ash and reduction in lignin leads to biofuel conversion consequences, such as reduced pyrolysis oil yields for herbaceous products as compared to woody material. In this review, the sources of variability for key biomass characteristics are presented for multiple types of biomass. Additionally, this review investigates the major impacts of the variability in biomass composition on four conversion processes: fermentation, hydrothermal liquefaction, pyrolysis, and direct combustion. Finally, future research processes aimed at reducing the detrimental impacts of biomass variability on conversion to fuels and chemicals are proposed.

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“…High content of ash reduces the pretreatment efficiency, increases the deterioration of handling systems, increases operational costs such as the usage of water, treatment of the waste stream, and ultimately disposing of the excess ash [33]. Bonner et al [34] found that two thirds of the increase on the cost of the supply system for corn stover with ash content ranging from 10% to 25% was due to the feedstock replacement needed to maintain the supply of convertible biomass. The other third comes mainly from the ash disposal operation.…”

Section: The Impact Of Feedstock Qualitymentioning

confidence: 99%

“…The other third comes mainly from the ash disposal operation. Bonner et al [34] assume the specification for ash content at 5%.…”

Section: The Impact Of Feedstock Qualitymentioning

confidence: 99%

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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Impact of Harvest Equipment on Ash Variability of Baled Corn Stover Biomass for Bioenergy

Cited by 41 publications

References 20 publications

Optimal blending management of biomass resources used for biochemical conversion

Optimal blending management of biomass resources used for biochemical conversion

Sources of Biomass Feedstock Variability and the Potential Impact on Biofuels Production

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

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