Modeling Biomass Logistics

Annevelink, Bert; Anttila, Perttu; Väätäinen, Kari; Gabrielle, Benoît; García-Galindo, D.; Leduc, Sylvain; Staritsky, I.G.

doi:10.1016/b978-0-12-812303-4.00004-5

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Within the 16 studies that focused on Asian countries, different biomass types and end products were represented in a range of different ways. Optimisation was used as study method in the vast majority (65) of the cases that we reviewed. The second largest class was regression analysis, with 20 records, while LCA was used in eight studies and simulation was used in six studies.…”

Section: Classification Analysismentioning

confidence: 99%

“…Two studies used the analytic hierarchy process (AHP) [80] and a weighted overlay analysis (WOA) [44] as decision-making tools, which resembled optimisation; however, they were classified as 'other methods' due to the absence of a mathematical formula for problem solving in the paper. Optimisation was used as study method in the vast majority (65) of the cases that we reviewed. The second largest class was regression analysis, with 20 records, while LCA was used in eight studies and simulation was used in six studies.…”

Section: Classification Analysismentioning

confidence: 99%

See 1 more Smart Citation

Utilisation of Spatial Data in Energy Biomass Supply Chain Research—A Review

Korpinen

Aalto

et al. 2023

Energies

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The supply logistics of energy biomasses generally involves a complex system of supply chains, which aim to achieve timely and cost-efficient feedstock deliveries to biomass demand points. The performance of supply chains is often examined in case studies where spatial data about biomass sources and transportation networks are deployed in varying resolutions and to different geographical extents. In this paper, we have reviewed 94 publications, in which spatial data were used in case studies that focused on analysing and optimising energy biomass supply chains. The reviewed publications were classified into 16 categories, according to the publication year, study methods and objectives, biomass types, supply system complexity and the spatial features of each study area. This review found that the use of geographical information systems in this context has increased in popularity in recent years, and that and the multiformity of the applied methods, study objectives and data sources have increased simultaneously. Another finding was that most of the studies that we reviewed focused on countries in which spatial biomass and transport network data of high quality were unrestrictedly available. Nevertheless, case studies, including spatial data from multiple countries, were represented marginally in the papers that we reviewed. In this paper we also argue that a standard way of reporting geographical contents in biomass case studies should be developed to improve the comprehension and reproducibility of the publications in this field of research.

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Section: Classification Analysismentioning

confidence: 99%

Section: Classification Analysismentioning

confidence: 99%

Utilisation of Spatial Data in Energy Biomass Supply Chain Research—A Review

Korpinen

Aalto

et al. 2023

Energies

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“…This mathematical model of deterministic origin involves an objective function to be either maximized or minimized (the objective function generally regards the overall operating cost of a plant when applied to the optimization of biomass supply chains for energy purposes) and a set of restrictions (mathematical expressions, such as linear equations or inequations) that address the selected decision variables (outputs) and parameters (inputs) [59,60]. In the context of BSC, restrictions in linear programming problems may be related to the thermal demand required by the boiler, admissible moisture content of biomass (40 to 50%, according to Annevelink et al [61]), available pretreatments, prevention of natural degradation, prevention of boiler corrosion (generally associated with sulfur and chlorine contents in biomass), limits of pollutant emissions generated during combustion, capacities of the available stocks, load capacity of transporters of biomass to storage, supply limits (sensitive to crops and other factors), contractual conditions, such as the minimum purchased from suppliers, etc. If one or more decision variables are integers, this is known as mixed integer linear programming (MILP) [38,62].…”

Section: Linear Programmingmentioning

confidence: 99%

Tools for Optimization of Biomass-to-Energy Conversion Processes

et al. 2023

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Biomasses are renewable sources used in energy conversion processes to obtain diverse products through different technologies. The production chain, which involves delivery, logistics, pre-treatment, storage and conversion as general components, can be costly and uncertain due to inherent variability. Optimization methods are widely applied for modeling the biomass supply chain (BSC) for energy processes. In this qualitative review, the main aspects and global trends of using geographic information systems (GISs), linear programming (LP) and neural networks to optimize the BSC are presented. Modeling objectives and factors considered in studies published in the last 25 years are reviewed, enabling a broad overview of the BSC to support decisions at strategic, tactical and operational levels. Combined techniques have been used for different purposes: GISs for spatial analyses of biomass; neural networks for higher heating value (HHV) correlations; and linear programming and its variations for achieving objectives in general, such as costs and emissions reduction. This study reinforces the progress evidenced in the literature and envisions the increasing inclusion of socio-environmental criteria as a challenge in future modeling efforts.

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“…Therefore, the amount of biomass in every plot in an area must be studied in order to apply the method. Another possible model is the bioloco model (Biomass Logistics Computer Optimization) developed by Annevelink and Mol and Diekema et al . This algorithm provides a logistic model based on graphs where the source nodes (sources of biomass) and destination nodes (biomass processing plants) exist, connected by arcs that represent costs or distances.…”

Section: Introductionmentioning

confidence: 99%

Quantification based on dimensionless dendrometry and drying of residual biomass from the pruning of orange trees in Bolivar province (Ecuador)

Velázquez‐Martí

Gaibor‐Chávez

Pérez

2016

Biofuels Bioprod Bioref

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In this work, a new approach to evaluating the amount of residual biomass obtained from orange trees based on normalization of variables is proposed for Bolivar province, Ecuador. So far, several models to quantify the amount of residues obtained from pruning have been proposed from dendrometric and cultivation variables, such as height, crown diameter, stem diameter, area per plant, yield, and age. However, the high dispersion of their values, caused by uncontrolled conditions, gave models with a low-medium coeffi cient of determination. The aim of this work has been to develop several models in order to predict wet available biomass using dimensionless dendrometric parameters from height, diameter and height of the crown, and the stem height. They improved the coeffi cients of determination to 0.94 for the global mathematical model. The drying process of pruned materials has also been analyzed. Residual biomass with 50% initial moisture content was dried outdoors on cement and agricultural soil until it reached constant moisture content. Models used to describe the drying process of agricultural products were employed to fi t the observed data of the drying process of orange tree chips. Among the tested models, the Midili and Page models were those that best fi tted the observed data in the drying process. The information offered by these equations is of vital importance because they help estimate the amount of biomass that is generated in a given area, and the implementation geographic information system (GIS) maps. In addition, logistic algorithms can be applied.

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Modeling Biomass Logistics

Cited by 8 publications

References 12 publications

Utilisation of Spatial Data in Energy Biomass Supply Chain Research—A Review

Utilisation of Spatial Data in Energy Biomass Supply Chain Research—A Review

Tools for Optimization of Biomass-to-Energy Conversion Processes

Quantification based on dimensionless dendrometry and drying of residual biomass from the pruning of orange trees in Bolivar province (Ecuador)

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