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

An integrated multi-feedstock (i.e. switchgrass and crop residue) lignocellulosic-based bioethanol supply chain is studied under jointly occurring uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand and sales price. A two-stage stochastic mathematical model is proposed...

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Veröffentlicht in:	Energy (Oxford) Jg. 59; S. 157 - 172
Hauptverfasser:	Osmani, Atif, Zhang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 15.09.2013 Elsevier
Schlagworte:	Applied sciences bioethanol Biomass biorefining case studies cost effectiveness crop residues Crops Demand Energy ethanol ethanol production Exact sciences and technology Facility location feedstocks finance gasoline Lignocellulosic-based bioethanol supply chain Mathematical analysis Mathematical models Natural energy North Dakota Optimization Panicum virgatum prices Residues sales Stochastic mixed integer linear programming stochastic processes Stochasticity supply chain Sustainability Switchgrass Uncertainty North Dakota Facility location Lignocellulosic-based bioethanol supply chain Sustainability Switchgrass Stochastic mixed integer linear programming Stochastic model Monocotyledones Gramineae Angiospermae Supply Spermatophyta Biofuel Biomass Panicum virgatum
ISSN:	0360-5442
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Abstract	An integrated multi-feedstock (i.e. switchgrass and crop residue) lignocellulosic-based bioethanol supply chain is studied under jointly occurring uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand and sales price. A two-stage stochastic mathematical model is proposed to maximize expected profit by optimizing the strategic and tactical decisions. A case study based on ND (North Dakota) state in the U.S. demonstrates that in a stochastic environment it is cost effective to meet 100% of ND's annual gasoline demand from bioethanol by using switchgrass as a primary and crop residue as a secondary biomass feedstock. Although results show that the financial performance is degraded as variability of the uncertain parameters increases, the proposed stochastic model increasingly outperforms the deterministic model under uncertainties. The locations of biorefineries (i.e. first-stage integer variables) are insensitive to the uncertainties. Sensitivity analysis shows that “mean” value of stochastic parameters has a significant impact on the expected profit and optimal values of first-stage continuous variables. Increase in level of mean ethanol demand and mean sale price results in higher bioethanol production. When mean switchgrass yield is at low level and mean crop residue price is at high level, all the available marginal land is used for switchgrass cultivation. •Two-stage stochastic MILP model for maximizing profit of a multi-feedstock lignocellulosic-based bioethanol supply chain.•Multiple uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand, and bioethanol sale price.•Proposed stochastic model outperforms the traditional deterministic model under uncertainties.•Stochastic parameters significantly affect marginal land allocation for switchgrass cultivation and bioethanol production.•Location of biorefineries is found to be insensitive to the stochastic environment.
AbstractList	An integrated multi-feedstock (i.e. switchgrass and crop residue) lignocellulosic-based bioethanol supply chain is studied under jointly occurring uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand and sales price. A two-stage stochastic mathematical model is proposed to maximize expected profit by optimizing the strategic and tactical decisions. A case study based on ND (North Dakota) state in the U.S. demonstrates that in a stochastic environment it is cost effective to meet 100% of ND's annual gasoline demand from bioethanol by using switchgrass as a primary and crop residue as a secondary biomass feedstock. Although results show that the financial performance is degraded as variability of the uncertain parameters increases, the proposed stochastic model increasingly outperforms the deterministic model under uncertainties. The locations of biorefineries (i.e. first-stage integer variables) are insensitive to the uncertainties. Sensitivity analysis shows that “mean” value of stochastic parameters has a significant impact on the expected profit and optimal values of first-stage continuous variables. Increase in level of mean ethanol demand and mean sale price results in higher bioethanol production. When mean switchgrass yield is at low level and mean crop residue price is at high level, all the available marginal land is used for switchgrass cultivation. •Two-stage stochastic MILP model for maximizing profit of a multi-feedstock lignocellulosic-based bioethanol supply chain.•Multiple uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand, and bioethanol sale price.•Proposed stochastic model outperforms the traditional deterministic model under uncertainties.•Stochastic parameters significantly affect marginal land allocation for switchgrass cultivation and bioethanol production.•Location of biorefineries is found to be insensitive to the stochastic environment. An integrated multi-feedstock (i.e. switchgrass and crop residue) lignocellulosic-based bioethanol supply chain is studied under jointly occurring uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand and sales price. A two-stage stochastic mathematical model is proposed to maximize expected profit by optimizing the strategic and tactical decisions. A case study based on ND (North Dakota) state in the U.S. demonstrates that in a stochastic environment it is cost effective to meet 100% of ND's annual gasoline demand from bioethanol by using switchgrass as a primary and crop residue as a secondary biomass feedstock. Although results show that the financial performance is degraded as variability of the uncertain parameters increases, the proposed stochastic model increasingly outperforms the deterministic model under uncertainties. The locations of biorefineries (i.e. first-stage integer variables) are insensitive to the uncertainties. Sensitivity analysis shows that “mean” value of stochastic parameters has a significant impact on the expected profit and optimal values of first-stage continuous variables. Increase in level of mean ethanol demand and mean sale price results in higher bioethanol production. When mean switchgrass yield is at low level and mean crop residue price is at high level, all the available marginal land is used for switchgrass cultivation. An integrated multi-feedstock (i.e. switchgrass and crop residue) lignocellulosic-based bioethanol supply chain is studied under jointly occurring uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand and sales price. A two-stage stochastic mathematical model is proposed to maximize expected profit by optimizing the strategic and tactical decisions. A case study based on ND (North Dakota) state in the U.S. demonstrates that in a stochastic environment it is cost effective to meet 100% of ND's annual gasoline demand from bioethanol by using switchgrass as a primary and crop residue as a secondary biomass feedstock. Although results show that the financial performance is degraded as variability of the uncertain parameters increases, the proposed stochastic model increasingly out-performs the deterministic model under uncertainties. The locations of biorefineries (i.e. first-stage integer variables) are insensitive to the uncertainties. Sensitivity analysis shows that "mean" value of stochastic parameters has a significant impact on the expected profit and optimal values of first-stage continuous variables. Increase in level of mean ethanol demand and mean sale price results in higher bioethanol production. When mean switchgrass yield is at low level and mean crop residue price is at high level, all the available marginal land is used for switchgrass cultivation. An integrated multi-feedstock (i.e. switchgrass and crop residue) lignocellulosic-based bioethanol supply chain is studied under jointly occurring uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand and sales price. A two-stage stochastic mathematical model is proposed to maximize expected profit by optimizing the strategic and tactical decisions. A case study based on ND (North Dakota) state in the U.S. demonstrates that in a stochastic environment it is cost effective to meet 100% of ND's annual gasoline demand from bioethanol by using switchgrass as a primary and crop residue as a secondary biomass feedstock. Although results show that the financial performance is degraded as variability of the uncertain parameters increases, the proposed stochastic model increasingly outperforms the deterministic model under uncertainties. The locations of biorefineries (i.e. first-stage integer variables) are insensitive to the uncertainties. Sensitivity analysis shows that ameana value of stochastic parameters has a significant impact on the expected profit and optimal values of first-stage continuous variables. Increase in level of mean ethanol demand and mean sale price results in higher bioethanol production. When mean switchgrass yield is at low level and mean crop residue price is at high level, all the available marginal land is used for switchgrass cultivation.
Author	Zhang, Jun Osmani, Atif
Author_xml	– sequence: 1 givenname: Atif surname: Osmani fullname: Osmani, Atif email: atif.osmani@ndsu.edu, at.osmani@gmail.com – sequence: 2 givenname: Jun surname: Zhang fullname: Zhang, Jun email: jun.zhang@ndsu.edu
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Keywords	Facility location Lignocellulosic-based bioethanol supply chain Sustainability Switchgrass Stochastic mixed integer linear programming Stochastic model Monocotyledones Gramineae Angiospermae Supply Spermatophyta Biofuel Biomass Panicum virgatum
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SubjectTerms	Applied sciences bioethanol Biomass biorefining case studies cost effectiveness crop residues Crops Demand Energy ethanol ethanol production Exact sciences and technology Facility location feedstocks finance gasoline Lignocellulosic-based bioethanol supply chain Mathematical analysis Mathematical models Natural energy North Dakota Optimization Panicum virgatum prices Residues sales Stochastic mixed integer linear programming stochastic processes Stochasticity supply chain Sustainability Switchgrass Uncertainty
Title	Stochastic optimization of a multi-feedstock lignocellulosic-based bioethanol supply chain under multiple uncertainties
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