Mine operating costs and the potential impacts of energy and grinding

Curry, James A.; Ismay, Mansel J.L.; Jameson, Graeme J.

doi:10.1016/j.mineng.2013.10.020

Cited by 93 publications

(34 citation statements)

References 10 publications

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“…For fossil resources, future technology development is accounted for in the estimates made by the International Energy Agency (2013). For mineral resources, technological innovation, economies of scale and new discoveries may partly offset higher costs of extracting resources (Crowson 2012;Curry et al 2014). Reserves beyond 20-30 years of consumption are rarely identified (Yaksic and Tilton 2009), meaning that new mining projects with lower costs than other running mines in the future may occur.…”

Section: Limitationsmentioning

confidence: 99%

Comparing mineral and fossil surplus costs of renewable and non-renewable electricity production

Vieira

Huijbregts

2017

Int J Life Cycle Assess

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Purpose Life cycle assessment aims to assess trade-offs between different impacts, including mineral and fossil resource use. The goals of this study were (1) to derive surplus cost potentials (SCPs) for a large number of fossil and mineral resources and (2) to derive surplus costs per megawatt hour of electricity produced for a range of both renewable and nonrenewable technologies. Methods The SCP of a resource refers to the total cost increase over the full amount of resource expected to be extracted in the future, expressed as US dollar (USD) per unit of resource extracted. For the fossil resources oil, natural gas and hard coal, cost-cumulative production relationships were derived that were subsequently used as input to calculate SCPs for these three fossil resources. For mineral resources, SCPs were readily available for 12 resources and platinumgroup metals as a separate group. SCPs for an additional number of 57 mineral resources and 4 mineral resource groups were derived on the basis of a statistical relationship between SCP and average price in year 2013. The SCPs of fossil and mineral resources were subsequently used to derive the surplus costs per megawatt hour of 10 electricity production technologies.Results and discussion The surplus costs of electricity production ranged from 0.3 to 148 USD 2013 /MWh. The three fossil-based energy production technologies, based on coal, gas and oil, resulted in the highest overall surplus costs (23 to 148 USD 2013 /MWh), while nuclear, geothermal, photovoltaic, wind and hydropower technologies have the lowest surplus costs (0.3-6 USD 2013 /MWh). We found that the contribution of fossil resource use to the surplus costs was higher compared to mineral resource use, including the renewable energy technologies. Conclusions Surplus costs of fossil and mineral resources can be used to compare renewable and non-renewable electricity production technologies. This case study shows that fossil fuel use drives the surplus costs of all energy technologies.

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Section: Limitationsmentioning

confidence: 99%

Comparing mineral and fossil surplus costs of renewable and non-renewable electricity production

Vieira

Huijbregts

2017

Int J Life Cycle Assess

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“…As higher grade ore bodies are exhausted, the average grades of the remaining deposits will diminish requiring even greater volumes of ore to be milled to meet the growing demand (Crowson, 2012;Mudd et al, 2013;Northey et al, 2014;Prior et al, 2012). It is also well established that comminution is the most energy intensive stage of mineral processing and that the process is inherently inefficient (Batterham, 2011;BCS, 2007;Curry et al, 2014;Nadolski et al, 2014;Tromans, 2008). Furthermore, new ore bodies are often more complex, occurring in harder rock with finer grain sizes, which further increases the energy required to liberate the valuable minerals for downstream separation processes (Cooke et al, 2006;Norgate and Jahanshahi, 2011;Sillitoe, 2010).…”

Section: Introductionmentioning

confidence: 99%

Deriving the ideal ore texture for microwave treatment of metalliferous ores

Jones

Plint

Kingman

2015

Minerals Engineering

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Highlights The influence of mineralogy on microwave-induced fracture in ores is investigated  Microwave-heating phase and matrix thermo-mechanical properties are both important  Hard microwave-heating phases constrained by a hard matrix gave the best results  ~2-20%wt heating phase abundance with grain size d50 >500µm gave the best results  Ore textural consistency is required for high average strength reductions AbstractHigh power density microwave treatments on metalliferous ores have historically been shown to reduce ore competency prior to beneficiation at economically feasible energy inputs. However, the relationship between mineralogical textural features and the extent of the microwave-induced fracturing had previously been limited to qualitative descriptions or simplistic two-phase numerical models, which could not account for the complex mineral assemblages in real ores. In this paper, mineralogy, grain size, dissemination, textural consistency and mineral associations were determined for 13 commercially exploited nickel, copper and lead-zinc ores using a Mineral Liberation Analyser (MLA). The ores were subjected to high power density microwave treatments at up to 25kW in a single mode cavity with microwave energy inputs of approximately 0.5-10kWh/t, and the subsequent reductions in ore competency were measured by the Point Load Test. The ores that demonstrated the greatest reductions in strength typically contained between approximately 2%wt to 20%wt of highly microwave-absorbing minerals, with a native grain size d50 greater than approximately 500µm, constrained by hard matrix minerals such as quartz and feldspar. Texturally consistent ores with a high proportion of amenable textures also demonstrated the highest average reductions in strength. These findings support the qualitative descriptions and numerical modelling results available in the literature and provide a baseline for selecting likely candidate ores for microwave treatments in the future. Minerals Engineering 84 (2015) 116-1292 http://dx

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“…According to the literature, haulage costs can be some of the largest in a mining system [133,134]. In various case studies it was found that material transportation represents 50 per cent of the operating costs of a surface mine [135].…”

Section: A Discrete-event Model To Simulate the Effect Of Payload Varmentioning

confidence: 99%

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

Soofastaei¹

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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.

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Mine operating costs and the potential impacts of energy and grinding

Cited by 93 publications

References 10 publications

Comparing mineral and fossil surplus costs of renewable and non-renewable electricity production

Comparing mineral and fossil surplus costs of renewable and non-renewable electricity production

Deriving the ideal ore texture for microwave treatment of metalliferous ores

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

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