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Predicting energy consumption and CO2 emissions of excavators in earthwork operations: An artificial neural network model
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Industriellt och hållbart byggande.ORCID-id: 0000-0003-0465-8304
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Industriellt och hållbart byggande.ORCID-id: 0000-0002-4695-5369
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Industriellt och hållbart byggande.ORCID-id: 0000-0002-5661-5237
2017 (engelsk)Inngår i: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 9, nr 7, artikkel-id 1257Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Excavators are one of the most energy-intensive elements of earthwork operations. Predicting the energy consumption and CO2 emissions of excavators is therefore critical in order to mitigate the environmental impact of earthwork operations. However, there is a lack of method for estimating such energy consumption and CO2 emissions, especially during the early planning stages of these activities. This research proposes a model using an artificial neural network (ANN) to predict an excavator's hourly energy consumption and CO2 emissions under different site conditions. The proposed ANN model includes five input parameters: digging depth, cycle time, bucket payload, engine horsepower, and load factor. The Caterpillar handbook's data, that included operational characteristics of twenty-five models of excavators, were used to develop the training and testing sets for the ANN model. The proposed ANN models were also designed to identify which factors from all the input parameters have the greatest impact on energy and emissions, based on partitioning weight analysis. The results showed that the proposed ANN models can provide an accurate estimating tool for the early planning stage to predict the energy consumption and CO2 emissions of excavators. Analyses have revealed that, within all the input parameters, cycle time has the greatest impact on energy consumption and CO2 emissions. The findings from the research enable the control of crucial factors which significantly impact on energy consumption and CO2 emissions.

sted, utgiver, år, opplag, sider
Basel: MDPI, 2017. Vol. 9, nr 7, artikkel-id 1257
HSV kategori
Forskningsprogram
Byggproduktion
Identifikatorer
URN: urn:nbn:se:ltu:diva-65072DOI: 10.3390/su9071257ISI: 000406709500184Scopus ID: 2-s2.0-85025160264OAI: oai:DiVA.org:ltu-65072DiVA, id: diva2:1131544
Merknad

Validerad; 2017; Nivå 2; 2017-08-15 (andbra)

Tilgjengelig fra: 2017-08-15 Laget: 2017-08-15 Sist oppdatert: 2019-06-14bibliografisk kontrollert
Inngår i avhandling
1. Assessing Energy Use and Carbon Emissions to Support Planning of Environmentally Sustainable Earthmoving Operations
Åpne denne publikasjonen i ny fane eller vindu >>Assessing Energy Use and Carbon Emissions to Support Planning of Environmentally Sustainable Earthmoving Operations
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Alternativ tittel[sv]
Utvärdering av energianvändning och koldioxidutsläpp för planering av miljömässigt hållbara massförflyttningsoperationer
Abstract [en]

Road and infrastructure projects have significant environmental impacts due to their high energy consumption and CO2 emissions. Among it, earthmoving operations contribute disproportionately to these impacts because of their intensive use of heavy machinery. However, little is known about how different equipment configurations and/or operational management strategies affect the environmental impact of earthmoving operations. Specifically, there is

• a lack of tools that enables stakeholders to understand and assess environment impacts of per unit volume of earth handled,

• a lack of integrated method taking into account both environmental and economic impacts in the planning of earthmoving operations.

This work aims to facilitate the adoption of sustainable earthmoving practices in construction by providing methods for selecting environmentally costeffective equipment configurations for earthmoving operations. Based on these considerations, three research questions were formulated:

• How can planners and construction managers of earthmoving projects estimate the energy use and carbon emissions of earthmoving machines per functional unit of material handled? • Which factors relating to earthworks operations have the greatest impact on energy use and carbon emissions?

• How can stakeholders optimize equipment configurations with respect to the trade-off between the carbon emissions, time, and cost of earthwork operations?

To answer these questions, an exploratory research approach involving multiple case studies was adopted. This resulted in the generation of a large body of experimental data and made it possible to test new methods for predicting and minimizing emissions due to earthmoving operations during the planning phases of construction projects. Throughout, the work was guided by the results of comprehensive literature reviews. Key findings of the work presented here include:

• The combination of Discrete Event Simulations (DES) and mass haul optimization (MHO) can be used to assess environmental impacts during project planning stage. Artificial Neural Networks (ANNs) provides an effective approach to model the relationships between input variables relating to the earthmoving equipment and project conditions and output variables relating to energy use and CO2 emissions per unit volume of hauled materials,

• The environmental performance of an item of equipment during earthmoving operations can be expressed as a function of the equipment’s operational characteristics and the job-site conditions such as digging depth, density of hauled materials and/or the topography of haulage surface. These factors all have important effects on the environmental impacts of earthmoving operations and the efficiency of the work,

• As expected, improving equipment utilization rates and/or cycle times significantly reduces energy use and CO2 emissions per unit volume of material handled. This also increases the equipment’s usage efficiency in terms of fuel consumption per unit volume of material hauled. A high usage efficiency (evaluated in terms of utilization rates and/or cycle times) thus minimizes both the emissions and the costs of earthmoving operations.

• Planning tools that account for costs and durations when assessing the carbon emissions of earthmoving operations make it possible to select optimal earthmoving equipment configurations that minimize emissions and costs (or at least do not increases costs).

In summary, this thesis identifies key factors that facilitate the assessment and reduction of energy consumption and carbon emissions in earthmoving projects. The developed approaches allow construction managers to benchmark the emissions of different equipment configurations during project planning.

The most important outcome of this work is the development of new methods for assessing energy use and CO2 emissions per unit volume of materials handled based on equipment characteristics and project conditions. These methods can be used to compare equipment configurations during the early stages of projects, and also for benchmarking/monitoring purposes during the construction stage. In particular, their use in the planning stages could help planners and construction managers to identify optimal equipment configurations that will minimize the environmental and economic impacts simultaneously.

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2019
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Emneord
energy use, CO2 emissions, earthmoving operation, operational characteristics, project conditions, selection equipment configuration.
HSV kategori
Forskningsprogram
Byggproduktion och teknik
Identifikatorer
urn:nbn:se:ltu:diva-74574 (URN)978-91-7790-409-0 (ISBN)978-91-7790-410-6 (ISBN)
Disputas
2019-10-03, F231, F-Hus, SBN, Luleå tekniska universitet, 97187 Luleå, Luleå, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-06-19 Laget: 2019-06-14 Sist oppdatert: 2019-09-24bibliografisk kontrollert

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