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Quantification of Energy Consumption and Carbon Dioxide Emissions During Excavator Operations
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction. Babylon University.ORCID iD: 0000-0003-0465-8304
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.ORCID iD: 0000-0002-4695-5369
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.ORCID iD: 0000-0002-5661-5237
2018 (English)In: Advanced Computing Strategies for Engineering: 25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018, Proceedings, Part I, Cham, 2018, p. 431-453Conference paper, Published paper (Refereed)
Abstract [en]

A number of studies have assessed the energy consumed and carbon dioxide emitted by construction machinery during earthwork operations. However, little attention has been paid to predicting these variables during planning phases of such operations, which could help efforts to identify the best options for minimizing environmental impacts. Excavators are widely used in earthwork operations and consume considerable amounts of fuel, thereby generating large quantities of carbon dioxide. Therefore, rigorous evaluation of the energy consumption and emissions of different excavators during planning stages of project, based on characteristics of the excavators and projects, would facilitate selection of optimal excavators for specific projects, thereby reducing associated environmental impacts. Here we describe use of artificial neural networks (ANNs), developed using data from Caterpillar’s handbook, to model the energy consumption and CO2 emissions of different excavators per unit volume of earth handled. We also report a sensitivity analysis conducted to determine effects of key parameters (utilization rate, digging depth, cycle time, bucket payload, horsepower, load factor, and hauler capacity) on excavators’ energy consumption and CO2 emissions. Our analysis shows that environmental impacts of excavators can be most significantly reduced by improving their utilization rates and/or cycle times, and reducing their engine load factor. We believe our ANN models can potentially improve estimates of energy consumption and CO2 emissions by excavators. Their use in planning stages of earthworks projects could help planners make informed decisions about optimal excavator(s) to use, and contractors to evaluate environmental impacts of their activities. Finally, we describe a case study, based on a road construction project in Sweden, in which we use empirical data on the quantities and nature of the materials to be excavated, to estimate the environmental impact of using different excavators for the project

Place, publisher, year, edition, pages
Cham, 2018. p. 431-453
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10863
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
URN: urn:nbn:se:ltu:diva-69572DOI: 10.1007/978-3-319-91635-4_22Scopus ID: 2-s2.0-85049074506ISBN: 978-3-319-91634-7 (print)ISBN: 978-3-319-91635-4 (electronic)OAI: oai:DiVA.org:ltu-69572DiVA, id: diva2:1219043
Conference
25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018
Available from: 2018-06-15 Created: 2018-06-15 Last updated: 2019-06-14Bibliographically approved
In thesis
1. Assessing Energy Use and Carbon Emissions to Support Planning of Environmentally Sustainable Earthmoving Operations
Open this publication in new window or tab >>Assessing Energy Use and Carbon Emissions to Support Planning of Environmentally Sustainable Earthmoving Operations
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[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.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2019
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
energy use, CO2 emissions, earthmoving operation, operational characteristics, project conditions, selection equipment configuration.
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
urn:nbn:se:ltu:diva-74574 (URN)978-91-7790-409-0 (ISBN)978-91-7790-410-6 (ISBN)
Public defence
2019-10-03, F231, F-Hus, SBN, Luleå tekniska universitet, 97187 Luleå, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2019-06-19 Created: 2019-06-14 Last updated: 2019-09-24Bibliographically approved

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Jassim, HassaneanLu, WeizhuoOlofsson, Thomas

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