A stochastic simulation framework for truck and shovel selection and sizing in open pit mines

Dindarloo, Saeid R.; Osanloo, M.; Frimpong, Samuel

doi:10.17159/2411-9717/2015/v115n3a6

Cited by 41 publications

(32 citation statements)

References 20 publications

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“…Meanwhile, the use of explosives was emitted in small quantities. Similar results have also been observed by the studies of Ercelebi [64], Dindarloo [65], Kittipongvises and Dubsok [66], [67] found that direct emission from transport-related activities in the quarrying process (i.e. transportation of rock from the mine the plant) was considered to be the largest share of emissions compared to other sources.…”

Section: Greenhouse Gas Protocolsupporting

confidence: 86%

Assessment of Environmental Impacts of Limestone Quarrying Operations in Thailand

Kittipongvises

2017

Environmental and Climate Technologies

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-Environmental impacts of the mineral extraction have been a public concern. Presently, there is widespread global interest in the area of mining and its sustainability that focused on the need to shift mining industry to a more sustainable framework. The aim of this study was to systematically assess all possible environmental and climate change related impacts of the limestone quarrying operation in Thailand. By considering the life cycle assessment method, the production processes were divided into three phases: raw material extraction, transportation, and comminution. Both IMPACT 2002+ and the Greenhouse Gas Protocol methods were used. Results of IMPACT 2002+ analysis showed that per 1 ton crushed limestone rock production, the total depletion of resource and GHGs emissions were 79.6 MJ and 2.76 kg CO2 eq., respectively. Regarding to the four damage categories, 'resources' and 'climate change' categories were the two greatest environmental impacts of the limestone rock production. Diesel fuel and electricity consumption in the mining processes were the main causes of those impacts. For climate change, the unit of CO2 eq. was expressed to quantify the total GHGs emissions. Estimated result was about 3.13 kg CO2 eq. per ton limestone rock product. The results obtained by the Greenhouse Gas Protocol were also similar to IMPACT 2002+ method. Electrical energy consumption was considered as the main driver of GHGs, accounting for approximately 46.8 % of total fossil fuel CO2 emissions. A final point should be noted that data uncertainties in environmental assessment over the complete life cycle of limestone quarrying operation have to be carefully considered.

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Section: Greenhouse Gas Protocolsupporting

confidence: 86%

Assessment of Environmental Impacts of Limestone Quarrying Operations in Thailand

Kittipongvises

2017

Environmental and Climate Technologies

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“…Cuatro año después Dindarloo et al (2015) reporta el caso de la mina Golegohar, una mina que se dedica a la explotación de hierro y oro ubicada en el sur de Irán. Para probar el marco de la simulación, recopilaron los datos necesarios de la logística de cada operación minera seguidos del análisis estadístico para obtener la distribución probabilística para cada conjunto de datos.…”

Section: A Trabajos Relacionadosunclassified

“…La simulación por eventos discretos se ha implantado como una herramienta decisiva para la evaluación de sistemas productivos (Sturgul, 2017). Las operaciones mineras al ser cíclicas, la aplicación de modelos de simulación por eventos discretos validados, permiten valorar alternativas de desarrollo con un elevado grado de confiabilidad (Dindarloo et al, 2015).…”

Section: Introductionunclassified

Evaluación del Sistema de Carguío y Acarreo usando Simulación por Eventos Discretos

Veintimilla¹,

Moreno-Chávez²,

Moscoso³

et al. 2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

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This research presents an optimal number of dumpers and shovels that should be designated to each front of a quarry using discrete event simulation as a tool for the evaluation of all possible scenarios and present the optimal ones. The open pit mine where the investigation took place does not have a loadinghaulage system by software. The company performs its mine planning using mathematical methods to exploit two fronts simultaneously per day using a fleet of seven dump trucks and three shovels. Probabilistic distributions were used based on data collected in the field to simulate the model in the Arena Input Analyzer. In addition, an analysis of the internal network track of the mine was carried out in order to simulate the movement of the mining trucks in the haul cycle, thus considering a variation of the process in each cycle. The results of the simulation showed that, of the 140 scenarios evaluated, 14 of them are optimal. Resumen: Esta investigación presenta un número óptimo de volquetes mineros y excavadoras que debe ser designado a cada frente de explotación de una cantera la simulación por eventos discretos como herramienta para la evaluación de todos los escenarios posibles y presentar los óptimos. La mina a cielo abierto donde tuvo lugar la investigación no posee un sistema de carga y transporte mediante software. La empresa realiza la planificación minera usando métodos matemáticos para explotar material de dos frentes simultáneamente por día con una flota de siete volquetes y tres excavadoras. Se usaron distribuciones probabilísticas en base a datos recolectados en campo para simular el modelo en el software Arena. Además, se realizó un análisis de las vías internas de la mina con la finalidad de simular el movimiento de los volquetes mineros en el ciclo de acarreo, considerando así una variación del tiempo del proceso en cada ciclo. Los resultados de la simulación mostraron que, de los 140 escenarios evaluados, 14 de ellos son óptimos.Palabras clave: Simulación eventos discretos, Arena, distribución probabilística, sistema, acarreo, minería. process in each cycle. The results of the simulation showed that, of the 140 scenarios evaluated, 14 of them are optimal. Abstract: This research presents an optimal number of dumpers and shovels that should be designated to each front of a quarry using discrete event simulation as a tool for the evaluation of all possible scenarios and present the optimal ones. The open pit mine where the investigation took place does not have a loading-haulage system by software. The company performs its mine planning using mathematical methods to exploit two fronts simultaneously per day using a fleet of seven dump trucks and three shovels. Probabilistic distributions were used based on data collected in the field to simulate the model in the Arena Input Analyzer. In addition, an analysis of the internal network track of the mine was carried out in order to simulate the movement of the mining trucks in the haul cycle, thus considering a variation of the

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“…In the case of a mine truck haulage system, simulations may be used to predict productivity indices, such as the mine production and equipment utilization, according to system operating conditions, including the task time, amount of equipment, dispatch interval, and time consumed per unit task [9,10]. In this manner, it is possible to select optimal equipment combinations [11][12][13][14], establish equipment dispatch plans [10,[15][16][17][18], and identify optimal haulage routes [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Ore Production and Crusher Utilization Prediction of Truck Haulage System in Underground Mine

Baek

Choi

2019

Applied Sciences

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A new method using a deep neural network (DNN) model is proposed to predict the ore production and crusher utilization of a truck haulage system in an underground mine. An underground limestone mine was selected as the study area, and the DNN model input/output nodes were designed to reflect the truck haulage system characteristics. Big data collected on-site for 1 month were processed to create learning datasets. To select the optimal DNN learning model, the numbers of hidden layers and hidden layer nodes were set to various values for analyzing the training and test data. The optimal DNN model structure for ore production prediction was set to five hidden layers and 40 hidden layer nodes. The test data exhibited a coefficient of determination of 0.99 and mean absolute percentage error (MAPE) of 2.80%. The optimal configuration for the crusher utilization prediction was set to four hidden layers and 40 hidden layer nodes, and the test data exhibited a coefficient of determination of 0.99 and MAPE of 2.49%. The trained DNN model was used to predict the ore production and crusher utilization, which were similar to the actual observed values.

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A stochastic simulation framework for truck and shovel selection and sizing in open pit mines

Cited by 41 publications

References 20 publications

Assessment of Environmental Impacts of Limestone Quarrying Operations in Thailand

Assessment of Environmental Impacts of Limestone Quarrying Operations in Thailand

Evaluación del Sistema de Carguío y Acarreo usando Simulación por Eventos Discretos

Deep Neural Network for Ore Production and Crusher Utilization Prediction of Truck Haulage System in Underground Mine

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