Surface mining operations: coincident unit cost and emissions

Carmichael, David G.; Bartlett, Beau J.; Kaboli, Alireza S.

doi:10.1080/17480930.2013.772699

Cited by 31 publications

(30 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extensive research has been conducted on accurately predicting cycle times and production for fleets of scrapers, and determining optimum (minimum unit cost; equivalently minimum cost per production) configurations and loading practices. Recent attention has turned to also looking at minimizing unit emissions (equivalently minimizing emissions per production) for the related excavatortruck operations (for example, Carmichael et al 2012Carmichael et al , 2014 and extended in Kaboli & Carmichael 2012, 2014). However, the relationship between unit cost and unit emissions is unknown for scrapers, and it is unclear the extent to which managing scraper operations in terms of minimum unit emissions changes the unit cost of operations.…”

mentioning

confidence: 99%

Optimum scraper load time and fleet size for minimum emissions

Kaboli

Carmichael

2014

International Journal of Construction Management

Self Cite

View full text Add to dashboard Cite

Scrapers are commonly used in road construction, site formation, other earthmoving and surface mining operations. It is known that loading a scraper to capacity may not be best in terms of unit operational cost, because the load-growth curve for a scraper is nonlinear À the loading rate decreases with load time. However, there is no published research considering the effect of load time and fleet size on unit emissions, and what the corresponding optimal load time and fleet size might be. This paper shows that the optimum scraper load time for unit emissions may be the same or slightly less than that for unit cost depending on the scraper type and operation, while the optimum fleet size in terms of minimum unit emissions, is slightly higher than or the same as that in terms of minimum unit cost. Cost-load-time, emissions-load-time and hauling characteristics, particular for different equipment and operations, influence the conclusion. For practice purposes, it could be said that the optimal unit cost and unit emissions solutions essentially coincide. Case study site data, in conjunction with a scraper operational analysis, are used to demonstrate the optima. Both push-pull scrapers and push-loaded scrapers are considered. IntroductionScrapers are useful for road construction, other earthmoving and surface mining operations. Extensive research has been conducted on accurately predicting cycle times and production for fleets of scrapers, and determining optimum (minimum unit cost; equivalently minimum cost per production) configurations and loading practices. Recent attention has turned to also looking at minimizing unit emissions (equivalently minimizing emissions per production) for the related excavatortruck operations (for example, Carmichael et al. 2012Carmichael et al. , 2014 and extended in Kaboli & Carmichael 2012, 2014). However, the relationship between unit cost and unit emissions is unknown for scrapers, and it is unclear the extent to which managing scraper operations in terms of minimum unit emissions changes the unit cost of operations. There is an identified need, and addressed in this paper, to study scraper operations in this context.Diesel-powered equipment used in earthmoving operations is a major emissions contributor, and produces considerably more emissions compared to on-road vehicles such as cars. For example, the amount of particulate matter from a bulldozer with a 175 hp engine is nearly 500 times more than that of a new car (EPA 2005). Accordingly, appropriate site practices need adopting in order to reduce emissions. For efficient operations, cycle times (loading, hauling, dumping, empty returning and, in some cases, queuing) of equipment such as scrapers need close scrutiny.A long scraper load time translates to a larger payload, but slower travel times and diesel engines working harder; this increases fuel use (and emissions) and cost per scraper cycle. However, since fewer scraper cycles are needed to transport a given volume of material, the overall fuel use and cost of a hauling op...

show abstract

mentioning

confidence: 99%

Optimum scraper load time and fleet size for minimum emissions

Kaboli

Carmichael

2014

International Journal of Construction Management

Self Cite

View full text Add to dashboard Cite

show abstract

“…The rate of energy consumption is a function of a number of parameters. The research presented by Carmichael et al [ 115 ] is concerned with the effects of the geology of the site, the density of the load, road surfaces and gradients on the energy consumption of haul trucks. Cetin [127] examined the relationship between haul truck energy efficiency and loading rates, vehicle efficiency, and driving practices [127].…”

Section: Reducing Fuel Consumption Of Haul Trucks In Surface Mines Usmentioning

confidence: 99%

“…They are used in combination with other equipment, such as excavators, diggers and loaders, according to the production capacity and the site layout [95]. The trucks used in the haulage operations of surface mines use a great amount of energy [3,188] and this has encouraged truck manufacturers and major mining corporations to carry out a number of research projects on the energy efficiency of haul trucks [39,98,104,115,188].…”

Section: Conclusion Abstractmentioning

confidence: 99%

“…The research presented by Carmichael, Bartlett and Kaboli [115] is concerned with the effects of haul truck fuel consumption on costs and gas emissions in surface mining operations; however, the simulation used in their research does not include the pertinent parameters affecting the fuel consumption. Chingooshi, Daws and Madden [104] study the smart energy mining strategy and identify the effective key parameters involved in energy efficiency opportunities in the mining industry as a whole; however, their research excludes the technical aspects of the parameters that affect fuel consumption for haul trucks.…”

Section: Introductionmentioning

confidence: 99%

“…Many research studies have already been conducted on the measurement of the haul truck gas emissions in the mining industry [48,[113][114][115][116]. In addition, several numbers of economic models have been presented to predict the cost of diesel and gas emissions [117].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines

Soofastaei¹

View full text Add to dashboard Cite

Truck haulage is responsible for a majority of cost in a surface mining operation. Diesel fuel, which is costly and has a significant environmental footprint, is used as a source of energy for haul trucks in surface mines. Reducing diesel fuel consumption would lead to a reduction in haulage cost and greenhouse gas emissions. The determination of fuel consumption is complex and requires multiple parameters including the mine, fleet, truck, fuel, climate and road conditions as input. Data analytics is used to simulate the complex relationships between the input parameters affecting the truck fuel consumption. This technique is also used to optimise the input parameters to minimise the fuel consumption without losing productivity or further capital expenditure for a specific surface mining operation.The aim of this research thesis is to develop an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines. The most important controllable parameters affecting fuel consumption are first identified, namely payload, truck speed and total resistance. These parameters are selected based on the results of an online survey. A comprehensive analytical framework is developed to determine the opportunities for minimising the truck fuel consumption. The first stage of the analytical framework includes the development of an artificial neural network model to determine the relationship between truck fuel consumption and payload, truck speed and total resistance. This model is trained and tested using real data collected from some large surface mines in USA and Australia. A fitness function for the haul truck fuel consumption is successfully generated. This fitness function is then used in the second stage of the analytical framework to develop a computerised learning algorithm based on a novel multi-objective genetic algorithm. The aim of this algorithm is to estimate the optimum values of the three effective parameters to reduce the diesel fuel consumption.The following studies are also conducted to enhance the analysis of haul truck fuel consumption.First, a comprehensive investigation of loading variance influence on fuel consumption and gas emissions in mine haulage operation is carried out. Then, a discrete-event model to simulate the effect of payload variance on truck bunching, cycle time and hauled mine materials is developed. The influence of rolling resistance on haul truck fuel consumption in surface mines is investigated.

show abstract

Minimizing Emissions and Cost Through Reducing Equipment Idle Time in Concreting Operations

Mustaffa

Aziz

2020

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Surface mining operations: coincident unit cost and emissions

Cited by 31 publications

References 2 publications

Optimum scraper load time and fleet size for minimum emissions

Optimum scraper load time and fleet size for minimum emissions

Development of an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines

Minimizing Emissions and Cost Through Reducing Equipment Idle Time in Concreting Operations

Contact Info

Product

Resources

About