Supply chain design and operational planning models for biomass to drop-in fuel production

Zhang, Leilei; Hu, Guiping

doi:10.1016/j.biombioe.2013.08.016

Cited by 65 publications

(33 citation statements)

References 27 publications

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“…In both the deterministic and the stochastic cases, 17 distributed fast pyrolysis plants are proposed but they are not at the same locations. The plants are all proposed to be built at the highest capacity level which was found to be more cost effective due to the economies of scale, and this conclusion is consistent with previous literature [32,46]. The centralized gasification plant will be constructed at the same site (Hamilton County) in both cases, which is located at the center of high corn yield counties.…”

Section: Analysis For the Stochastic Casesupporting

confidence: 81%

“…It is assumed that all the facilities have a 20-year operation life and an interest rate of 10% [20,46]; the online time of all the facilities is 328 days per year (equivalent capacity factor of 90%). In the following two sections, the computational results of the biofuel supply chain design for both deterministic case and stochastic case are presented.…”

Section: Data Sourcesmentioning

confidence: 99%

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Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification

2014

Energy

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An advanced biofuels supply chain is proposed to reduce biomass transportation costs and take advantage of the economics of scale for a gasification facility. In this supply chain, biomass is converted to bio-oil at widely distributed small-scale fast pyrolysis plants, and after bio-oil gasification, the syngas is upgraded to transportation fuels at a centralized biorefinery. A two-stage stochastic programming is formulated to maximize biofuel producers' annual profit considering uncertainties in the supply chain for this pathway. The first stage makes the capital investment decisions including the locations and capacities of the decentralized fast pyrolysis plants as well as the centralized biorefinery, while the second stage determines the biomass and biofuels flows. A case study based on Iowa in the U.S. illustrates that it is economically feasible to meet desired demand using corn stover as the biomass feedstock. The results show that the locations of fast pyrolysis plants are sensitive to uncertainties while the capacity levels are insensitive. The stochastic model outperforms the deterministic model in the stochastic environment, especially when there is insufficient biomass. Also, farmers' participation can have a significant impact on the profitability and robustness of this supply chain. Keywords stochastic programming, bio-oil gasification, supply chain design Disciplines Industrial Engineering | Systems EngineeringComments NOTICE: This is the author's version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy, 74, September (2014) NOTICE: This is the author's version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy, 74, September (2014) AbstractAn advanced biofuels supply chain is proposed to reduce biomass transportation costs and take advantage of the economics of scales for gasification facility. In this supply chain, biomass is converted to bio-oil at widely distributed small-scale fast pyrolysis plants, and after bio-oil gasification, the syngas is upgraded to transportation fuels at centralized biorefi . A twostage stochastic programming is formulated to maximize biofuel producers' annual profi considering uncertainties in supply chain for this pathway. The fi makes the capital investment decisions including the locations and capacities of the decentralized fast pyrolysis plants as well as the centralized biorefi while the second-stag...

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Section: Analysis For the Stochastic Casesupporting

confidence: 81%

Section: Data Sourcesmentioning

confidence: 99%

Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification

2014

Energy

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“…First the supply chain network alternatives were identified and evaluated in terms of usefulness (profitability, flexibility, and robustness) and then a multi-echelon multi product MILP model was proposed. Zhang and Hu (2013) proposed a MILP model in the biofuel supply chain. Their multi-period models determine biofuel supply chain facility location, facility capacity at strategic levels, and biofuel production decisions at operational levels.…”

Section: Lp Modelsmentioning

confidence: 99%

Optimization methods applied to renewable and sustainable energy: A review

Asadi¹,

Sadjadi²

2017

10.5267/j.uscm

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“…Zhang and Hu [20], Shabani and Sowlati [21] and Santibañez-Aguilar et al [22] also developed multi-echelon models with 12 one-month intervals. Zhang and Hu presented a 3-echelon model to produce bio-gasoline from corn stover via fast pyrolysis with upgrading to drop-in biofuels; Shabani and Sowlati considered forestry residues to produce electricity; and Santibañez-Aguilar et al developed a multi-objective model, considering cost, emissions and social impacts, focussing on biofuels from a variety of biomass feedstocks.…”

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confidence: 99%

“…Most are restricted to a particular purpose: many are concerned with biofuels [18,19,20,22,23,24,27,29,30,31,32,33,36,37,41] …”

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confidence: 99%

BVCM: A comprehensive and flexible toolkit for whole system biomass value chain analysis and optimisation – Mathematical formulation

Samsatli

Shah

2015

Applied Energy

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Supply chain design and operational planning models for biomass to drop-in fuel production

Cited by 65 publications

References 27 publications

Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification

Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification

Optimization methods applied to renewable and sustainable energy: A review

BVCM: A comprehensive and flexible toolkit for whole system biomass value chain analysis and optimisation – Mathematical formulation

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