Facilities Location for Residual Biomass Production System Using Geographic Information System under Uncertainty

Santibáñez-Aguilar, José Ezequiel; Flores‐Tlacuahuac, Antonio; Betancourt-Galvan, Francisco; Lozano-García, Diego F.; Lozano, Francisco J.

doi:10.1021/acssuschemeng.7b03303

Cited by 22 publications

(19 citation statements)

References 47 publications

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“…(2018) [54] did in their study. One example of including emission estimation was supplied by Furubayashi and Nakata (2018) [53], who used mathematical expressions for energy consumption.…”

Section: Combinations Of Methods In Generalmentioning

confidence: 88%

Modeling of biomass supply system by combining computational methods – A review article

Aalto

Korpinen

et al. 2019

Applied Energy

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As computing power increases, more complex computational models are utilized for biomass supply system studies. The paper describes three commonly used modeling methods in this context, geographic information systems, life-cycle assessment, and discrete-time simulation and presents bibliometric analysis of work using these three study methods. Of the 498 publications identified in searches of the Scopus and Web of Science databases, 17 reported on combinations of methods: 10 on life-cycle assessment and geographic information systems, six on joint use of life-cycle assessment and discrete-time simulation, and one on use of geographic information systems jointly with discrete-time simulation. While no articles dealt directly with simultaneous use of all three methods, several acknowledged the potential of this. The authors discuss numerous challenges identified in the review that arise in combining methods, among them computational load, the increasing number of assumptions, guaranteeing coherence between the models used, and the large quantities of data required.Discussion of issues such as the complexity of reporting and the need for standard procedures and terms becomes more critical as repositories bring together research materials, including entire models, from various sources. Efforts to mitigate many of modeling's challenges have involved phase-specific modeling and use of such methods as expressions or uncertainty analysis in place of a complex secondary model. The authors conclude that combining modeling methods offer considerable potential for taking more variables into account; improving the results; and benefiting researchers, decision-makers, and operation managers by producing more reliable information.

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“…(2018) [54] did in their study. One example of including emission estimation was supplied by Furubayashi and Nakata (2018) [53], who used mathematical expressions for energy consumption.…”

Section: Combinations Of Methods In Generalmentioning

confidence: 88%

Modeling of biomass supply system by combining computational methods – A review article

Aalto

Korpinen

et al. 2019

Applied Energy

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“…A GIS-based model taking into account various features connected to a geographic region in order to determine the location of biomass-processing facilities considering also uncertainty factors [35]. These factors are connected to the availability of crop residues, other environmental, financial, weather, or even social constraints.…”

Section: Strategic Planningmentioning

confidence: 99%

Green, Yellow, and Woody Biomass Supply-Chain Management: A Review

et al. 2019

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Various sources of biomass contribute significantly in energy production globally given a series of constraints in its primary production. Green biomass sources (such as perennial grasses), yellow biomass sources (such as crop residues), and woody biomass sources (such as willow) represent the three pillars in biomass production by crops. In this paper, we conducted a comprehensive review on research studies targeted to advancements at biomass supply-chain management in connection to these three types of biomass sources. A framework that classifies the works in problem-based and methodology-based approaches was followed. Results show the use of modern technological means and tools in current management-related problems. From the review, it is evident that the presented up-to-date trends on biomass supply-chain management and the potential for future advanced approach applications play a crucial role on business and sustainability efficiency of biomass supply chain.

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“…Davis et al [6] and Baustert and Benetto [14] suggest that ABM could be used to generate foreground data, also known as inventory, which could then be coupled with LCA. In addition to ABM, geographical information systems (GIS) are often used to analyse spatial information, which is important for collecting inventories in local contexts [15]. A review by Aalto et al [13] suggests that there are multiple benefits of coupling GIS and ABM with LCA; however, their study found no research that combines all three modelling approaches.…”

Section: Introductionmentioning

confidence: 99%

Lifecycle Assessment of Biomass Supply Chain with the Assistance of Agent-Based Modelling

Aalto

Korpinen

et al. 2020

Sustainability

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Even though biomass is characterised as renewable energy, it produces anthropogenic greenhouse gas (GHG) emissions, especially from biomass logistics. Lifecycle assessment (LCA) is used as a tool to quantify the GHG emissions from logistics but in the past the majority of LCAs have been steady-state and linear, when in reality, non-linear and temporal aspects (such as weather conditions, seasonal biomass demand, storage capacity, etc.) also have an important role to play. Thus, the objective of this paper was to optimise the environmental sustainability of forest biomass logistics (in terms of GHG emissions) by introducing the dynamic aspects of the supply chain and using the geographical information system (GIS) and agent-based modelling (ABM). The use of the GIS and ABM adds local conditions to the assessment in order to make the study more relevant. In this study, GIS was used to investigate biomass availability, biomass supply points and the road network around a large-scale combined heat and power plant in Naantali, Finland. Furthermore, the temporal aspects of the supply chain (e.g., seasonal biomass demand and storage capacity) were added using ABM to make the assessment dynamic. Based on the outcomes of the GIS and ABM, a gate-to-gate LCA of the forest biomass supply chain was conducted in order to calculate GHG emissions. In addition to the domestic biomass, we added imported biomass from Riga, Latvia to the fuel mixture in order to investigate the effect of sea transportation on overall GHG emissions. Finally, as a sensitivity check, we studied the real-time measurement of biomass quality and its potential impact on overall logistical GHG emissions. According to the results, biomass logistics incurred GHG emissions ranging from 2.72 to 3.46 kg CO2-eq per MWh, depending on the type of biomass and its origin. On the other hand, having 7% imported biomass in the fuel mixture resulted in a 13% increase in GHG emissions. Finally, the real-time monitoring of biomass quality helped save 2% of the GHG emissions from the overall supply chain. The incorporation of the GIS and ABM helped in assessing the environmental impacts of the forest biomass supply chain in local conditions, and the combined approach looks promising for developing LCAs that are inclusive of the temporal aspects of the supply chain for any specific location.

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Facilities Location for Residual Biomass Production System Using Geographic Information System under Uncertainty

Cited by 22 publications

References 47 publications

Modeling of biomass supply system by combining computational methods – A review article

Modeling of biomass supply system by combining computational methods – A review article

Green, Yellow, and Woody Biomass Supply-Chain Management: A Review

Lifecycle Assessment of Biomass Supply Chain with the Assistance of Agent-Based Modelling

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