Equipment Selection for Surface Mining: A Review

Burt, Christina N.; Caccetta, Louis

doi:10.1287/inte.2013.0732

Cited by 59 publications

(25 citation statements)

References 80 publications

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“…Tirta Bara Unity (TBU) as the mining contractor. Moreover, technical aspect will elaborate available working scheme and production target [1]. At the end, as the core of this paper, financial aspect will be explained in details.…”

Section: Methodsmentioning

confidence: 99%

Investment Feasibility Analysis of a Coal Project Related to Improvement Production Capacity

Natan¹,

Sumirat²

2019

Proceedings of the 1st International Conference on Economics, Business, Entrepreneurship, and Finance (ICEBEF 2018)

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Indonesia is currently becoming one of the strongest emerging market in terms of economic growth. This achievement requires supporting facility such as transportation infrastructure, manufacture, etc. Such rapid growth will not last long if not sustained by an adequate supply of electricity. In order to meet the target, the government established some power plants which one of them is Naga coal-fired station in Aceh. In the end of 2017, PT. Bagan Ekuitas Losari (BEL) has successfully signed a Memorandum of Understanding (MoU) with PLTU Naga to provide an amount of 600.000 tons per year. Therefore, PT. Tirta Bara Unity (TBU) as a mining contractor of PT. Bagan Ekuitas Losari was assigned to enhance the yearly production capacity of coal from 120.000 tons to 600.000 tons with overburden removal of 192.000 bcm to2.600.000 bcm with stripping ratio of 4.1. For such increasing, the question is how TBU can finance the project with a limited cash flow due to the high number of long-term receivables. Should they provide fully owned capital, fully rent, or lease from financial institution? How's the financial analysis that can be indicator of the chosen alternative? By identifying and analyzing all aspect, particularly in terms of financial aspect, the best decision can be determined.

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Section: Methodsmentioning

confidence: 99%

Investment Feasibility Analysis of a Coal Project Related to Improvement Production Capacity

Natan¹,

Sumirat²

2019

Proceedings of the 1st International Conference on Economics, Business, Entrepreneurship, and Finance (ICEBEF 2018)

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“…The optimization of support systems (such as haulage routes, equipment selection, and allocation) and production cycle times are well established in the surface mining literature. The application of advanced methods such as operations research in optimizing these systems has been more widely studied in surface mining, specifically in truck-shovel operations, [11][12][13][14] than in underground operations. 6,15,16 However, very little progress has been made in the optimization of support systems that directly affect coal production rates, such as the effect of panel design parameters and production sequencing on the rate of production and equipment utilization.…”

Section: Related Workmentioning

confidence: 99%

Optimizing cut-out distance for maximum coal productivity

et al. 2018

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The need to increase productivity has led to innovations in highly mechanized equipment such as the continuous miner (CM). However, often times the CM is underutilized, resulting in loss of productivity and increased operating cost. The objective of this study is to apply a simulation tool to evaluate if optimizing support systems, such as cut-out distance, fleet size, and panel dimensions, can maximize CM utilization and productivity. Discrete event simulation is used to analyze different cut-out distances for a room and pillar mining system. The results show that an 11-entry panel width with a fleet size of three shuttle cars is optimum for the variable cut-out distances examined. The utilization and production rate also increases by 6% at the maximum cut-out distance.

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“…In particular, efforts are being made to efficiently operate a truck haulage system, which constitutes greater than half of the operating cost [2]. The efficiency of a truck haulage system varies depending on the combination of the equipment used and operating patterns [3]. Therefore, it is necessary to operate an optimal truck haulage system capable of maximizing ore production and minimizing the equipment delay time [4].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Predictions of Ore Production in an Underground Limestone Mine Using Different Probability Density Functions: A Comparative Study Using Big Data from ICT System

2021

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This study stochastically predicted ore production through discrete event simulation using four different probability density functions for truck travel times. An underground limestone mine was selected as the study area. The truck travel time was measured by analyzing the big data acquired from information and communications technology (ICT) systems in October 2018, and probability density functions (uniform, triangular, normal, and observed probability distribution of real data) were determined using statistical values. A discrete event simulation model for a truck haulage system was designed, and truck travel times were randomly generated using a Monte Carlo simulation. The ore production that stochastically predicted fifty times for each probability density function was analyzed and represented as a value of lower 10% (P10), 50% (P50), and 90% (P90). Ore production was underestimated when a uniform and triangular distribution was used, as the actual ore production was similar to that of P90. Conversely, the predicted ore production of P50 was relatively consistent with the actual ore production when using the normal and observed probability distribution of real data. The root mean squared error (RMSE) for predicting ore production for ten days in October 2018 was the lowest (24.9 ton/day) when using the observed probability distribution.

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Equipment Selection for Surface Mining: A Review

Cited by 59 publications

References 80 publications

Investment Feasibility Analysis of a Coal Project Related to Improvement Production Capacity

Investment Feasibility Analysis of a Coal Project Related to Improvement Production Capacity

Optimizing cut-out distance for maximum coal productivity

Stochastic Predictions of Ore Production in an Underground Limestone Mine Using Different Probability Density Functions: A Comparative Study Using Big Data from ICT System

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