Emissions Modelling of Earthmoving Equipment

Barati, Khalegh; Shen, Xuesong

doi:10.22260/isarc2016/0065

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…The Caterpillar performance handbook presents excavator specifications and cycle times based on working conditions [78]. However, cycle time is affected by other operational parameters such as bucket size, swing angle, and truck position [79]. In this study, the proposed model uses a cycle time identified from the manufacturer's handbook using specific swing angle, depth of cut, type of earth, and minimum distance to truck [41,79], for various earth densities with different excavating depths.…”

Section: Resultsmentioning

confidence: 99%

“…However, cycle time is affected by other operational parameters such as bucket size, swing angle, and truck position [79]. In this study, the proposed model uses a cycle time identified from the manufacturer's handbook using specific swing angle, depth of cut, type of earth, and minimum distance to truck [41,79], for various earth densities with different excavating depths. Therefore, the cycle time for each excavator can be estimated for different operating conditions in the project such as excavating depths and type of earth [80] based on physical quantities [41,78] such as the cutting and loading force required for digging.…”

Section: Resultsmentioning

confidence: 99%

“…Cycle time is considered a basic metric for loading performance, one that impacts the productivity rates of construction equipment [80,81]. Cycle time for each operation mode is one of the main operational parameters that effect on the level emission of excavators of earthmoving operations [79]. Efficient excavating cycle time is a best idea that leads to fuel saving [82].…”

Section: Resultsmentioning

confidence: 99%

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Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

Jassim

Olofsson

2017

Sustainability

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Excavators are one of the most energy-intensive elements of earthwork operations. Predicting the energy consumption and CO 2 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 CO 2 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 CO 2 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 CO 2 emissions of excavators. Analyses have revealed that, within all the input parameters, cycle time has the greatest impact on energy consumption and CO 2 emissions. The findings from the research enable the control of crucial factors which significantly impact on energy consumption and CO 2 emissions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

Jassim

Olofsson

2017

Sustainability

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“…The machinery is mostly deployed at a large scale in mega projects, performing heavy duty operations, which generates a substantial amount of pollutants compared to other sectors. For instance, a middle-sized loader machine produces 500 times more emissions as compared to a private car [2][3][4]. The large-scale dependency on diesel-fueled machinery, which emits pollutants such as CO 2 , SO 2 , NO X , and particulate matter (PM) [5], jeopardizes urban life.…”

Section: Introductionmentioning

confidence: 99%

Toward Zero Emission Construction: A Comparative Life Cycle Impact Assessment of Diesel, Hybrid, and Electric Excavators

Khan,

Huang

2023

Energies

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Due to an extensive usage of heavy machinery, the construction sector is criticized as one of the major CO2 emitters. To address climate concerns, mitigating these greenhouse gas (GHG) emissions is important. This study aimed to strategize for “zero emission construction” by assessing the life cycle environmental impacts of diesel, electric, and hybrid construction machinery. By applying life cycle assessment (LCA) principles with adherence to ISO 14040/44 methodologies, this study scrutinizes the environmental repercussions of a standard excavator over 9200 effective operational hours, from raw material acquisition to end-of-life disposal. The results demonstrate a significant reduction in global warming potential (GWP), ozone depletion potential (ODP), and acidification potential (AP) in transitioning from diesel to hybrid and fully electric machines. A nominal increase due to this shift also occurred and impacted categories such as human carcinogenic toxicity (HT), freshwater eutrophication (EP), and marine ecotoxicity (ME); however, a more significant upsurge was noted in terrestrial ecotoxicity (TE) due to battery production. Thus, this study highlights the need for a careful management of environmental trade-offs in the shift toward electrified machinery and the importance of centering on the environmental profile of the battery. Future work should focus on enhancing the environmental profile of battery production and disposal, with policy decisions encouraging holistic sustainability based on green energies in construction projects.

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“…The machinery is mainly involved in huge earthmoving operations which their emitted pollution is by far more than other vehicles. For example, the pollution production of a middle-sized loader is nearly 500 times more than that of a private car [7,8]. Based on the report prepared by the EPA's Clean Air Act Advisory Committee (CAAAC), construction sector accounts for 6% of the light-duty vehicles (LDVs) and 17% of the heavy-duty vehicles (HDVs) while producing 32% of nitrogen oxide (NO x ) and 37% of particulate matters (PM) of all mobile source emissions [9].…”

Section: Introductionmentioning

confidence: 99%

Modelling Traffic Conditions on Fuel Use and Emissions of On-road Construction Equipment

Barati¹,

Shen²

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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The consumption of fossil fuels by on-road vehicles is a main source of air pollution specifically greenhouse gases (GHGs) worldwide. The construction industry, due to the use of a large number of heavy-duty equipment including haulage trucks, contributes to a significant amount of fuel consumption and consequent emissions production. Due to increasing number of vehicles on the road, traffic condition is becoming one of the main variables having a considerable impact on the fuel use and emissions of such vehicles. There is a lack of comprehensive studies in construction field quantifying the effect of road traffic on fuel use and emissions of construction equipment. This research aims to model the impact of traffic conditions on fuel use and emissions of on-road construction vehicles. The research framework is first developed to present the methodology of data collection and analysis, and then introduce the fuel use and emissions models applied in predicting the effect of traffic conditions. Three variables of driving speed, idling time and equipment stop are identified as representatives of traffic conditions, and their impacts on fuel use and emissions are quantified through conducting statistical analyses on collected field data. The achieved results found that by having more effective traffic management and planning, the fuel cost and consequent emissions can be reduced up to 9% due to the decreasing idling time of equipment. It is also indicated that by lowering the number of equipment stops, up to 0.66 l/100kW fuel is saved and up to 1.7 kg/100kW CO 2 is emitted less per each stop.

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Emissions Modelling of Earthmoving Equipment

Cited by 6 publications

References 9 publications

Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

Toward Zero Emission Construction: A Comparative Life Cycle Impact Assessment of Diesel, Hybrid, and Electric Excavators

Modelling Traffic Conditions on Fuel Use and Emissions of On-road Construction Equipment

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