Stochastic optimization of a multi-feedstock lignocellulosic-based bioethanol supply chain under multiple uncertainties

Osmani, Atif; Zhang, Jun

doi:10.1016/j.energy.2013.07.043

Cited by 112 publications

(46 citation statements)

References 40 publications

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“…Compare Figure 5 to Figure 3, it is observed that the locations of fast pyrolysis plants are more centralized when availability factor δ is equal to 0.7 and we only need 40 counties (rather than 71 when δ is equal to 0.4) to supply the biomass. These results not only illustrate the phenomena that the locations of fast pyrolysis plants are sensitive to uncertainties, but also suggest that the optimal supply chain decisions will be improved by increasing biomass availability due to the additional fl in choosing the biomass harvesting sites and consequently reduction of total system cost [32,17]. Table 5 shows the value of the stochastic solution (VSS) will decrease as the availability factor increase.…”

Section: Discussion On the Impact Of Farmers' Participationmentioning

confidence: 90%

“…Alex et al formulated a mixed integer linear programming model to determine optimal locations and capacities of biorefi [16]. Osmani et al used stochastic optimization to deal with the uncertainties in biomass yield and price as well as biofuel demand and price [17]. As a recent advancement in the cellulosic biofuel technology, decentralized supply chain design for thermochemical pathways have not been studied extensively, especially scenario under uncertainties.…”

Section: Introductionmentioning

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: Discussion On the Impact Of Farmers' Participationmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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

2014

Energy

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“…The results are compared with those of a deterministic model using simulation technique. Osmani and Zhang (2013) developed a two-stage stochastic MILP for decision making in a multi-feedstock bioethanol supply chain. In the first stage, strategic decisions such as location and production capacity of biorefineries and allocation of farmlands for biomass cultivation are made.…”

Section: Studies With Single Objectivementioning

confidence: 99%

Biomass to Energy Supply Chain Network Design: An Overview of Models, Solution Approaches and Applications

Balaman

Selim

2015

Handbook of Bioenergy

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Energy production from biomass is an alternative and additive way to fossil fuel based energy production to reduce the dependency on limited fossil fuel sources and mitigate the harmful environmental impacts of these systems. One of the major challenges in establishing efficient renewable energy systems is the complex supply chain structure in an uncertain decision environment, various decisions to be made and different conflicting criteria/objectives. This study describes the key issues in decision making for biomass to energy supply chains such as decision levels, uncertainty and sustainability concepts. It also provides a comprehensive review and systematic classification of the current literature on decision making approaches for design, management and operation of biomass to energy supply chains. This study allows readers to identify the decision making methods that satisfy the problem specific requirements and offer a clear vision of the advances in the field.

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“…They solved their model by benders decomposition and Monte Carlo simulation technique and in order to compare its performance, they compared the model with simulation methods. Osmani and Zhang (2013) proposed a two-stage stochastic MILP model to minimize profit. Their multi-feedstock model addresses the strategic decisions such as location, production capacity, and allocation in the first phase and tactical decisions are made in the second phase.…”

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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Stochastic optimization of a multi-feedstock lignocellulosic-based bioethanol supply chain under multiple uncertainties

Cited by 112 publications

References 40 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

Biomass to Energy Supply Chain Network Design: An Overview of Models, Solution Approaches and Applications

Optimization methods applied to renewable and sustainable energy: A review

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