Analyzing the design and management of biomass-to-biorefinery supply chain

Ekşioğlu, Sandra D.; Acharya, Ambarish; Leightley, L.E.; Arora, Sumesh

doi:10.1016/j.cie.2009.07.003

Cited by 384 publications

(167 citation statements)

References 14 publications

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“…Several previous studies report the costs of producing biobased 22 hydrocarbons via fast pyrolysis and upgrading (Islam and Ani, 2000;Wright et al, 2010). Bio-oil 23 is a viscous and corrosive liquid that must be upgraded prior to refining, which can occur either 1 at a decentralized fast pyrolysis facility or at a centralized dedicated refinery. Upgrading can be 2 accomplished either catalytically via fluid catalytic cracking or by reaction with hydrogen via 3 hydroprocessing.…”

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confidence: 99%

Optimization Model for a Thermochemical Biofuels Supply Network Design

Brown

2014

J. Energy Eng.

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This research focuses on the supply chain network design of a fast pyrolysis and hydroprocessing production pathway by utilizing corn stover as feedstock to produce gasoline and diesel fuel. A mixed integer linear programming (MILP) model was formulated to optimize fast pyrolysis and hydroprocessing facility locations and capacities to minimize total system cost, including the feedstock collecting costs, capital costs of facilities, and transportation costs. The economic feasibility of building a new biorefinery in Iowa was analyzed based on the optimal supply chain configuration and savings in bio-oil logistic costs to the centralized upgrading facility. Keywords

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confidence: 99%

Optimization Model for a Thermochemical Biofuels Supply Network Design

Brown

2014

J. Energy Eng.

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“…We propose the integrated optimization mathematical model by modifying the model (Eksioglu et al, 2009). In our proposed model, we assume that CFs can be located at any HS and a biorefinery (BR) can only be built at candidate BR location, since BR locations must satisfy some realistic requirements.…”

Section: Development Of Integrated Optimization Modelmentioning

confidence: 99%

A simulation-based robust biofuel facility location model for an integrated bio-energy logistics network

Hong

Feng

Xie

2014

JIEM

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Abstract:Purpose: The purpose of this paper is to propose a simulation-based robust biofuel facility location model for solving an integrated bio-energy logistics network (IBLN) problem, where biomass yield is often uncertain or difficult to determine.Design/methodology/approach: The IBLN considered in this paper consists of four different facilities: farm or harvest site (HS), collection facility (CF), biorefinery (BR), and blending station (BS). Authors propose a mixed integer quadratic modeling approach to simultaneously determine the optimal CF and BR locations and corresponding biomass and bio-energy transportation plans. The authors randomly generate biomass yield of each HS and find the optimal locations of CFs and BRs for each generated biomass yield, and select the robust locations of CFs and BRs to show the effects of biomass yield uncertainty on the optimality of CF and BR locations. Case studies using data from the State of South Carolina in the United State are conducted to demonstrate the developed model's capability to better handle the impact of uncertainty of biomass yield. Findings:The results illustrate that the robust location model for BRs and CFs works very well in terms of the total logistics costs. The proposed model would help decision-makers find the most robust locations for biorefineries and collection facilities, which usually require huge -1415-Journal of Industrial Engineering and Management -http://dx.doi.org/10.3926/jiem.1196 investments, and would assist potential investors in identifying the least cost or important facilities to invest in the biomass and bio-energy industry.Originality/value: An optimal biofuel facility location model is formulated for the case of deterministic biomass yield. To improve the robustness of the model for cases with probabilistic biomass yield, the model is evaluated by a simulation approach using case studies.The proposed model and robustness concept would be a very useful tool that helps potential biofuel investors minimize their investment risk.

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“…Issues that are problematic for biomass transport include its low bulk density, poor flowability, and susceptibility to physical degradation during storage (especially if moisture is present) (Rentizelas, et al, 2009;Richard, 2010). Therefore, highly efficient harvest, densification, and storage systems for biomass are critical for an efficient biomass-to-biorefinery supply chain that will minimize transportation costs and energy consumption, while maintaining a high quality feedstock for processing (Eksioglu, et al 2009). Each of the different types of proposed feedstocks has a unique set of challenges or advantages regarding harvest method and timing, densification, transportation, and storage (Sims, 2003).…”

Section: Logisticsmentioning

confidence: 99%

“…Instead of the 100-120 million gallon/yr (MMGY) corn ethanol plants, 2 nd -4 th generation biofuel facilities will be in the 10-40 MMGY range. The upper size limit will likely be determined by the amount of biomass that can be economically delivered to the plant gate (Carolan, et al, 2007;Eksioglu, et al, 2009). This will be affected by biomass yields per acre, competing uses/markets, price, and feedstock logistic issues.…”

Section: Conversion and Recoverymentioning

confidence: 99%