Optimizing a pellet supply system: Market-specific pellet production with biomass quality considerations

Aghalari, Amin; Aladwan, Badr Saleh; Marufuzzaman, Mohammad; Tanger, Shaun; Silva, Bruno Kanieski da; Gude, Veera Gnaneswar

doi:10.1016/j.compchemeng.2021.107417

Cited by 11 publications

(4 citation statements)

References 51 publications

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“…It should be added that more and more often in computer-aided systems of choosing the optimal distribution routes for goods, multi-criteria mathematical models are used, which take into account many qualitative and quantitative parameters; this requires their additional standardization and determination of their priority level [52]. An example is the work of Aghalari et al [53], in which they presented the problem of optimizing the entire biomass-to-pellet supply system. They have developed a two-stage stochastic model that takes into account various elements such as: harvesting, storage, transportation, or quality inspection.…”

Section: Discussionmentioning

confidence: 99%

Estimating the Optimal Location for the Storage of Pellet Surplus

Bochniak

Stoma

2021

Energies

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This paper deals with the problem of managing the surplus that arises during the seasonal production of pellets, which will be sold in the period of increased demand. Dijkstra’s algorithm is used in issues connected with finding a new storage place with a view of the optimisation of the transport costs of pellets produced by a company in 18 different towns in the Lubelskie Voivodeship in Poland. The most optimal location for the new pellet storage site has been determined, for which the total length of the traveled routes is the shortest, taking into account the actual shares of individual plants in the total production. The construction of the graph with the shortest paths was made on the basis of the existing network of available transport roads, and the nodes of the graph were their intersections. The most advantageous storage location of pellets was identified by the calculation the total transport cost using a minimum-cost tree of shortest paths. Based on the estimated transport assumptions, the lowest total cost of transport from all 18 plants was 3092.0 (km), which corresponds to an average distance to production plants of 89.7 km and 61.7 km to estimated selling distribution. The new storage point is suggested near the town of Piaski. Average cost of travel for all trees obtained for existing plant locations and subsequent distribution to points of sale was 4113.7 (km), while standard deviation 735.2 (km). Additionally, a relative increase in costs was estimated in the case of selecting other locations. Using spatial interpolation and geoprocessing tools, a map—showing the increase in pellet transport costs in relation to the most optimal solution—was developed. The constructed map allows for a better analysis of cost increases than a single point. It was stated that the increase in transport costs does not exceed 10% of lowest cost for 17.6% area of studied area. It was found that the most convenient area is shifted to the south of the voivodship and improperly adopted storage location can increase transport costs by up to 75%.

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Section: Discussionmentioning

confidence: 99%

Estimating the Optimal Location for the Storage of Pellet Surplus

Bochniak

Stoma

2021

Energies

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“…Starting from the premise that a BSC is stochastic by nature, Aghalari et al [71] used mathematical modeling (MILP and a hybrid algorithm) to assess the impact of quality (ash and moisture content) while optimizing the production of biomass pellets in the USA.…”

Section: Case Studies Of Linear Programming In the Context Of Biomass...mentioning

confidence: 99%

“…Usually, in this stage the resource availability is promising but conversion technology has not been considered yet; • A second group involves Cundiff et al [64], Bruglieri and Liberti [66], Rocco and Morabito [67], Abdelhady et al [69], Yahya et al [33] and Saghaei et al [68]; their models aid the decision maker in the process of power plant location and/or storage location definition and multiple-facility operational status definition. Some of them also allow for uncertainty in the model (Saghaei et al [68], Cundiff et al [64]); • A third group focuses on the optimization for different uses for the considered biomasses; Ferretti [70] and Aghalari et al [71] could be included in this group; • A fourth group involves the research of Paes et al [72] that, despite optimizing the BSC from an economic standpoint, also uses explicit environmental constrains. Nienow et al [65] could also be placed in this group, since their research also helps the power plant comply with environmental regulations; • A fifth group involves Wu et al [38] whose research focuses on holistic approaches giving great attention to multidisciplinarity (geographic information systems and mathematical modeling with technical economic analysis and sensitivity analysis).…”

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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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“…These components affect the density and burning process. Compressed proteins and starch act as a binder that contributes to the durability of the granules [18][19][20]. Typically, pellets and briquettes have a bulk density of 600-750 kg•m 3 and 350-450 kg•m 3 [13].…”

Section: Introductionmentioning

confidence: 99%

Co-pelletization of Hemp Residues and Agricultural Biomass: Effect on Pellet Quality and Stability

2023

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The rising interest in lowering the use of fossil fuels, which influence environmental pollution and global warming, is driving a substantial increase in renewable sources. Agricultural residues are the likely potential source for bioenergy generation. Some of them are already utilized for energy. Nonetheless, their potential is underutilized due to low biomass quality and high concentrations of sulfur and chloride, which induce the corrosion of adjoining equipment. However, their ash content and ash melting point make their utilization as renewable resources essential. Therefore, there is a need to find technologies to enhance biomass utilization for bioenergy processes. With the increase in hemp cultivation to extract phytocannabinoids, the amount of unused biomass has increased. The aim of this research was to investigate the use of hemp biomass for pellets and improve pellet quality by mixing them with lignin and oak sawdust. The results showed that the lowest amount of ash was found in pellets with 80% oak sawdust and 20% hemp residue compared with pellets made from mixtures of hemp residues, lignin, and oak sawdust. The highest calorific value was achieved by mixing hemp residues (20%) with lignin (80%).

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Optimizing a pellet supply system: Market-specific pellet production with biomass quality considerations

Cited by 11 publications

References 51 publications

Estimating the Optimal Location for the Storage of Pellet Surplus

Estimating the Optimal Location for the Storage of Pellet Surplus

Tools for Optimization of Biomass-to-Energy Conversion Processes

Co-pelletization of Hemp Residues and Agricultural Biomass: Effect on Pellet Quality and Stability

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