Linear mixed-integer models for biomass supply chains with transport, storage and processing

Dyken, Silke Van; Bakken, B.H.; Skjelbred, Hans Ivar

doi:10.1016/j.energy.2009.11.017

Cited by 154 publications

(74 citation statements)

References 14 publications

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“…The current costs of primary biomass fuels are also often higher than the cost of competing fossil fuels (Junginger et al 2005). Compared to more traditional energy transport technologies like electricity and gas, there are fewer studies dealing with techno-economic modelling and optimisation of biomass supply chains (van Dyken et al 2010).…”

mentioning

confidence: 99%

Managing the moisture content of wood biomass for the optimisation of Ireland's transport supply strategy to bioenergy markets and competing industries

Sosa

Acuña

McDonnell

et al. 2015

Energy

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Publication information Energy, 86 : 354-368Publisher Elsevier Item record/more information http://hdl.handle.net/10197/7007 Publisher's statementThis 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 (VOL 86, ISSUE 2015, (2015  Low wood moisture content increases supply cost due to longer transport distances and/or inclusion of more costly forest products. Optimal truck loads can be achieved by controlling wood moisture content.

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mentioning

confidence: 99%

Managing the moisture content of wood biomass for the optimisation of Ireland's transport supply strategy to bioenergy markets and competing industries

Sosa

Acuña

McDonnell

et al. 2015

Energy

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“…Many existing studies have focused on bio-processing technologies to improve the biofuel yield and quality (Antonpoulou, Gavala, Skiadas, Angelopoulos & Lyberatos, 2008;Lee, Chou, Ham, Lee & Keasling, 2008;Ranganathan, Narasimhanm & Muthukumar, 2008;van Dyken, Bakken & Skjelbred, 2010;Weyens, Lelie, Taghavi, Newman & Vangronsveld, 2009). Although the cost of transporting bulky and unrefined biomass feedstock is also significant as compared to the total cost for producing biofuel, much less attention has been given to the understanding of biomass and bio-energy logistics systems and the reduction of biomass and bio-energy logistics costs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is because a BR usually requires several million dollars as the annualized construction and operation cost. Also, the use of CFs would affect the quality of biofuel that primarily depends upon the moisture content in the biomass (Dyken, Bakken & Skjelbred, 2010), letting alone the total transportation cost between HSs and BSs. What complicates the decision of CF and BR locations is the uncertainty in biomass yield.…”

Section: Introductionmentioning

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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“…Models that have been employed to schedule and plan the storage capacity, and location have been analysed and enumerated in previous studies see (Nilsson et al, 2001 p247;Rentizelas et al, 2009 p887;Sokhansanj et al, 2010 p75;Sokhansanj et al, 2002 p347;Vadas et al, 2013 p133;Van Dyken et al, 2010van Vliet et al, 2009 p855;Wang et al, 2014 p32).…”

Section: Literature Reviewmentioning

confidence: 99%

Harvesting and transport operations to optimise biomass supply chain and industrial biorefinery processes

Matindi¹,

Masoud²,

Hobson³

et al. 2018

10.5267/j.ijiec

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In Australia, Bioenergy plays an important role in modern power systems, where many biomass resources provide greenhouse gas neutral and electricity at a variety of scales. By 2050, the Biomass energy is projected to have a 40-50 % share as an alternative source of energy. In addition to conversion of biomass, barriers and uncertainties in the production, supply may hinder biomass energy development. The sugarcane is an essential ingredient in the production of Bioenergy, across the whole spectrum ranging from the first generation to second generation, e.g., production of energy from the lignocellulosic component of the sugarcane initially regarded as waste (bagasse and cane residue). Sustainable recovery of the Lignocellulosic component of sugarcane from the field through a structured process is largely unknown and associated with high capital outlay that have stifled the growth of bioenergy sector. In this context, this paper develops a new scheduler to optimise the recovery of lignocellulosic component of sugarcane and cane, transport and harvest systems with reducing the associated costs and operational time. An Optimisation Algorithm called Limited Discrepancy Search has been adapted and integrated with the developed scheduling transport algorithms. The developed algorithms are formulated and coded by Optimization Programming Language (OPL) to obtain the optimised cane and cane residues transport schedules. Computational experiments demonstrate that high-quality solutions are obtainable for industry-scale instances. To provide insightful decisions, sensitivity analysis is conducted in terms of different scenarios and criteria.

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Linear mixed-integer models for biomass supply chains with transport, storage and processing

Cited by 154 publications

References 14 publications

Managing the moisture content of wood biomass for the optimisation of Ireland's transport supply strategy to bioenergy markets and competing industries

Managing the moisture content of wood biomass for the optimisation of Ireland's transport supply strategy to bioenergy markets and competing industries

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

Harvesting and transport operations to optimise biomass supply chain and industrial biorefinery processes

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