Development and implementation of integrated biomass supply analysis and logistics model (IBSAL)

Sokhansanj, Shahab; Kumar, Amit; Turhollow, Anthony

doi:10.1016/j.biombioe.2006.04.004

Cited by 319 publications

(171 citation statements)

References 20 publications

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“…This paper focuses on second-generation biofuels using a type of energy crop (i.e., switchgrass). The production of second-generation biofuels involves activities like harvesting, extracting, transporting and feedstock handling that can be enhanced to improve the biofuel supply chain performance [3,4]. Feedstock logistics play a relevant role in the optimization of the whole supply chain.…”

Section: Introductionmentioning

confidence: 99%

A Principal Component Analysis in Switchgrass Chemical Composition

Aboytes–Ojeda

Castillo-Villar

et al. 2016

Energies

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Abstract:In recent years, bioenergy has become a promising renewable energy source that can potentially reduce the greenhouse emissions and generate economic growth in rural areas. Gaining understanding and controlling biomass chemical composition contributes to an efficient biofuel generation. This paper presents a principal component analysis (PCA) that shows the influence and relevance of selected controllable factors over the chemical composition of switchgrass and, therefore, in the generation of biofuels. The study introduces the following factors: (1) storage days; (2) particle size; (3) wrap type; and (4) weight of the bale. Results show that all the aforementioned factors have an influence in the chemical composition. The number of days that bales have been stored was the most significant factor regarding changes in chemical components due to its effect over principal components 1 and 2 (PC1 and PC2, approximately 80% of the total variance). The storage days are followed by the particle size, the weight of the bale and the type of wrap utilized to enclose the bale. An increment in the number of days (from 75-150 days to 225 days) in storage decreases the percentage of carbohydrates by −1.03% while content of ash increases by 6.56%.Keywords: lignocellulosic biomass; principal component analysis; statistical hypothesis; bioenergy; switchgrass BackgroundBiofuel production is a promising alternative source of energy which provides advantages such as displacement of fossil-based fuels, reduction of greenhouse gas emissions, among others [1]. Biofuels can be classified according to the type of biomass utilized as follows: (1) first-generation biofuels are produced from organic sources commonly used as food for human consumption (e.g., sorghum, corn, among others); (2) second-generation biofuels are obtained from a wide range of non-food sources such as agricultural and forest residues, energy crops, and municipal solid waste; and (3) third-generation biofuels commonly refer to biofuels produced from algae [2]. This paper focuses on second-generation biofuels using a type of energy crop (i.e., switchgrass). The production of second-generation biofuels involves activities like harvesting, extracting, transporting and feedstock handling that can be enhanced to improve the biofuel supply chain performance [3,4]. Feedstock logistics play a relevant role in the optimization of the whole supply chain. Identifying factors in logistics operations that affect feedstock properties and its quality helps to improve the production of reliable biofuels.

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Section: Introductionmentioning

confidence: 99%

A Principal Component Analysis in Switchgrass Chemical Composition

Aboytes–Ojeda

Castillo-Villar

et al. 2016

Energies

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“…For instances Sacchelli et al [37], Schelhaas et al [39], Sokhansanj et al [40], Masera et al [41] and Alemán-Nava et al [42], among others, suggest simulation methods which incorporate variables according to a given analysis context. For the research design, the presented study takes into consideration these and other investigations, having a focus on biomass extraction limits which are based on forest operations criteria at a local context.…”

Section: Discussionmentioning

confidence: 99%

Bioenergy Potential and Utilization Costs for the Supply of Forest Woody Biomass for Energetic Use at a Regional Scale in Mexico

2017

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While considering constraints in regard to sustainability, this paper reviews the development of a methodology to assess the introduction of bioenergy supply chains in Mexico based on forest woody biomass. Three research modules include analyses of forest biomass utilization residues that originated from: (1) harvesting activities, (2) non-extracted stands and (3) sawmills. A regional case study focusing on tree species of commercial importance (pine, oak and fir) in the 10 provinces with the highest timber production located in the north and central-south part of the country, is analyzed. After applying the methodology, the theoretical potential of available woody biomass for energetic use amounts to 6,357,482 m 3 . When applying the sustainability constraints, the technically feasible supply of forest woody biomass for energetic use sums up to 5,798,722 m 3 , which relate to a technical energy potential of 45.96 PJ for 2013. Moreover, a biomass energy flow chart showing energy values for each analyzed source and species is presented. Monte Carlo simulations were carried out for each cost involved in utilizing the resulting available woody biomass for energetic use. In the absence of national studies which include forest operations and bioenergy transformation to calculate the sustainable energy potential, the developed methodology adds innovation for assessing woody biomass availability.

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“…Most of these existing studies focus either on the optimization of biorefinery locations (Celli, Ghiani, Loddo, Pilo & Pani, 2008;Graham, English & Noon, 2000;Panichelli & Gnansounou, 2008; -1418-Journal of Industrial Engineering and Management -http://dx.doi.org/10.3926/jiem.1196 Perpiñá, Alfonso, Pérez-Navarro, Peñalvo, Vargas & Cárdenas, 2009;Steen, Kang, Bokinky, Hu, Schimer, McClure et al, 2010) or on the optimization and simulation of the biomass collection, storage, and transport operations (Frombo, Minciardi, Robba, Rosso & Sacile, 2009;Kumar & Sokhansanj, 2007;Rentizelas, Tolis & Tatsiopoulos, 2009;Sokhansanj, Kumar & Turhollow, 2006). Eksioglu, Acharya, Leightley and Arora (2009) investigate the integrated biomass and biofuel logistics network design, simultaneously taking into account the optimization of facility locations (e.g., collection facilities, biorefineries), transportation, and inventory control.…”

Section: Literature Reviewmentioning

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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Development and implementation of integrated biomass supply analysis and logistics model (IBSAL)

Cited by 319 publications

References 20 publications

A Principal Component Analysis in Switchgrass Chemical Composition

A Principal Component Analysis in Switchgrass Chemical Composition

Bioenergy Potential and Utilization Costs for the Supply of Forest Woody Biomass for Energetic Use at a Regional Scale in Mexico

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

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