An Analysis of Logistic Costs to Determine Optimal Size of a Biofuel Refinery

Larasati, Aisyah; Liu, Tieming; Epplin, Francis M.

doi:10.1080/10429247.2012.11431956

Cited by 11 publications

(18 citation statements)

References 14 publications

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“…Limited work has been done to optimize the size of biomass processing itself (Larasati, Liu, & Epplin, 2012) and few studies have analyzed bioenergy production in depth (Dwivedi & Alavalapati, 2009). Additional effort is needed to understand what the optimal size of a biomass processing facility should be (Larasati et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Biofuel Production: Utilizing Stakeholders’ Perspectives

Fawzy

Componation

2015

Engineering Management Journal

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Section: Literature Reviewmentioning

confidence: 99%

Biofuel Production: Utilizing Stakeholders’ Perspectives

Fawzy

Componation

2015

Engineering Management Journal

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“…Transportation costs decreased 25% as bale density increased from 128 to 208 kg·m −3 [14]. However, an analysis by Sokhansanj et al [15] suggested there is an optimum bale density which balances lower transport costs against greater harvest costs.…”

Section: Introductionmentioning

confidence: 99%

Energy Requirements for Biomass Harvest and Densification

Shinners

Friede

2018

Energies

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This research quantified the unit and bulk density of several biomass crops across a variety of harvest and processing methods, as well as the energy and fuel requirements for these operations. A load density of approximately 240 kg·m −3 is needed to reach the legal weight limit of most transporters. Of the three types of balers studied, only the high density (HD) large square baler achieved this target density. However, the specific energy and fuel requirements increased exponentially with bale density, and at the maximum densities for corn stover and switchgrass, the dry basis energy and fuel requirements ranged from 4.0 to 5.0 kW·h·Mg −1 and 1.2 to 1.4 L·Mg −1 , respectively. Throughputs of tub grinders when grinding bales was less than any other harvesting or processing methods investigated, so specific energy and fuel requirements were high and ranged from 13 to 32 kW·h·Mg −1 and 5.0 to 11.3 L·Mg −1 , respectively. Gross size-reduction by pre-cutting at baling increased bale density by less than 6% and increased baling energy requirements by 11% to 22%, but pre-cut bales increased the tub grinder throughput by 25% to 45% and reduced specific fuel consumption for grinding by 20% to 53%. Given the improvement in tub grinder operation, pre-cutting bales should be considered as a means to increase grinder throughput. Additional research is needed to determine the energy required to grind high density pre-cut bales at high throughputs so that better estimates of total energy required for a high density bale system can be made. An alternative bulk feedstock system was investigated that involved chopping moist biomass crops with a precision-cut forage harvester, compacting the bulk material in a silo bag, and then segmenting the densified material into modules optimized for efficient transport. The specific fuel use for chopping and then compacting biomass crops in the silo bag ranged from 1.6 to 3.0 L·Mg −1 and 0.5 to 1.3 L·Mg −1 , respectively. At the proposed moistures, the compacted density in the silo bags was sufficient to achieve weight-limited transport although there would be less dry matter (DM) shipped than with the high density dry bale system. Additional development work is needed to create transportable modules from the compacted silo bag. The overall results of this research will allow more accurate estimates of biomass logistics costs based on product density and energy expenditures.

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“…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. Castillo-Villar et al [24] use a QP model that merges the total cost of transportation, total cost of the harvest processes, cost for opening a collection of facilities, cost for opening a collection of biorefineries, mechanical drying cost, ash disposal cost, screening cost, grinding cost, and a penalty cost for reduced oil yield due to high ash content into a single cost function to be minimized.…”

Section: Discrete Event Simulation √mentioning

confidence: 99%

“…Discrete event simulation √ An analysis of logistic costs to determine optimal size of a biofuel refinery Larasati et al [17] Analyze the impact of logistic costs on the size of cellulosic ethanol biorefineries. Feedstock: Switchgrass.…”

Section: Discrete Event Simulation √mentioning

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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An Analysis of Logistic Costs to Determine Optimal Size of a Biofuel Refinery

Cited by 11 publications

References 14 publications

Biofuel Production: Utilizing Stakeholders’ Perspectives

Biofuel Production: Utilizing Stakeholders’ Perspectives

Energy Requirements for Biomass Harvest and Densification

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

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