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

Aalto, Mika; Kc, Raghu; Korpinen, Olli-Jussi; Karttunen, Kalle; Ranta, T.

doi:10.1016/j.apenergy.2019.03.201

Cited by 22 publications

(14 citation statements)

References 58 publications

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“…On the contrary, LCA was first developed to account for environmental impacts before being developed to include economic (with LCC) and social (with S-LCA) impacts (Fauzi et al, 2019). MFA can evaluate the (Turner et al, 2016) EEIOA with information on material flows (Hawkins et al, 2007) None 22 13 2 156 SD Dynamic LCA of large systems; models market potential (Peng et al, 2018) Dynamic EEIOA and inclusion of feedback loops; models market potential (Cordier et al, 2017) Dynamic MFA revealing causal mechanisms within the system; models market potential (Gao et al, 2020) None 394 140 181 DES Dynamic LCA of small systems and their processes (Aalto et al, 2019) Technological details on EEIOA industrial sectors?…”

Section: Critical Analysismentioning

confidence: 99%

Do We Need a New Sustainability Assessment Method for the Circular Economy? A Critical Literature Review

Walzberg¹,

Lonca²,

Hanes³

et al. 2021

Front. Sustain.

104

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The goal of the circular economy (CE) is to transition from today's take-make-waste linear pattern of production and consumption to a circular system in which the societal value of products, materials, and resources is maximized over time. Yet circularity in and of itself does not ensure social, economic, and environmental performance (i.e., sustainability). Sustainability of CE strategies needs to be measured against their linear counterparts to identify and avoid strategies that increase circularity yet lead to unintended externalities. The state of the practice in quantitatively comparing sustainability impacts of circular to linear systems is one of experimentation with various extant methods developed in other fields and now applied here. While the proliferation of circularity metrics has received considerable attention, to-date, there is no critical review of the methods and combinations of methods that underlie those metrics and that specifically quantify sustainability impacts of circular strategies. Our critical review herein analyzes identified methods according to six criteria: temporal resolution, scope, data requirements, data granularity, capacity for measuring material efficiency potentials, and sustainability completeness. Results suggest that the industrial ecology and complex systems science fields could prove complementary when assessing the sustainability of the transition to a CE. Both fields include quantitative methods differing primarily with regard to their inclusion of temporal aspects and material efficiency potentials. Moreover, operations research methods such as multiple-criteria decision-making (MCDM) may alleviate the common contradictions which often exist between circularity metrics. This review concludes by suggesting guidelines for selecting quantitative methods most appropriate to a particular research question and making the argument that while there are a variety of existing methods, additional research is needed to combine existing methods and develop a more holistic approach for assessing sustainability impacts of CE strategies.

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Section: Critical Analysismentioning

confidence: 99%

Do We Need a New Sustainability Assessment Method for the Circular Economy? A Critical Literature Review

Walzberg¹,

Lonca²,

Hanes³

et al. 2021

Front. Sustain.

104

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“…In the area of mathematical modelling, several methods exist to analyze the processes undergoing at a log-yard [16][17][18]. In literature, one of the most adopted methods to model forestry processes is via discrete-event modelling (DEM), because many of these processes operate as a sequence of events.…”

Section: Introductionmentioning

confidence: 99%

Modelling Dynamics of a Log-Yard through Discrete-Event Mathematics

Kons

Hera

Bergström

2020

Forests

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This article deals with the topic of modelling the log-yard of one of our industry partners. To this end, our framework is based on discrete-events modelling (DEM), as consequence that many stages of the process run as a sequence of events. The sequence starts when trucks, trains or ships arrive loaded with logs to the log-yard. A machine unloads these logs and accumulates them in different storage areas. Consequently, a machine transports logs from these areas to the pulp mill, thus finishing the process. As using probability density functions is the core concept of DEM, the necessary process data to build these PDFs have been partly provided by the company. Other necessary data have been acquired through time studies, and by defining operational requirements. The company data tell when trucks, trains, or ships arrive to the log-yard, and the amount of volume they carry. The objective is to develop the necessary formulations, model calibration techniques, and software, such that computer simulations reproduce the quantities observed in these data. To this end, this work suggests two alternatives to analyse the data itself. These two alternatives lead to two different models: (1) The first being a hybrid model, in the sense that it involves the events in the process, and the logic decisions taken by machine operators for handling the incoming load, and (2) the second containing only the main mathematical essence of the process. After running 100 simulations, both mathematical models show that the simulated values for input and output, in terms of transport units and their volume, differ only by less than 3% compared to company data. The first model has also shown the ability to replicate the decision making that a machine operator undergoes for driving the logs to the storage areas, and from there to the mill. Therefore, the framework adopted provides the necessary mathematical tools and data analysis to model the log-yard and obtain highly reliable results via simulations.

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“…Kishita et al [12] conducted a scenario analysis for a sustainable bioenergy business through a narrative storyline, which provides a consistent story as to what will happen to the business in the future. Aalto et al [13] refers to this study by Kishita et al as a way of using DES to assist with environmental impact assessment (CO 2 emission calculation). However, the authors note that there are disadvantages to their approach since the study omits the ripple effects of the actions.…”

Section: Introductionmentioning

confidence: 99%

“…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. There are however, a number of studies that have coupled ABM with LCA in the bioenergy sector [6,[16][17][18][19], and GIS with LCA [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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

Aalto

Korpinen

et al. 2020

Sustainability

Self Cite

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

Cited by 22 publications

References 58 publications

Do We Need a New Sustainability Assessment Method for the Circular Economy? A Critical Literature Review

Do We Need a New Sustainability Assessment Method for the Circular Economy? A Critical Literature Review

Modelling Dynamics of a Log-Yard through Discrete-Event Mathematics

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

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