Exploitation
Mehrnaz Mohtasham
Abstract
In open-pit mining, haulage equipment accounts for a significant portion of total operating costs. Optimizing fleet performance is therefore crucial for reducing costs and improving productivity. Within this system, loading equipment plays a key role, as truck efficiency depends heavily on loader performance. ...
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In open-pit mining, haulage equipment accounts for a significant portion of total operating costs. Optimizing fleet performance is therefore crucial for reducing costs and improving productivity. Within this system, loading equipment plays a key role, as truck efficiency depends heavily on loader performance. The match factor, a metric that evaluates compatibility between loaders and trucks, is commonly used to enhance fleet efficiency. However, many existing approaches fail to account for practical mining conditions such as equipment downtime, accurate truck cycle times, and material fragmentation resulting from blasting. These omissions can lead to inaccurate fleet performance evaluations and higher operational costs. This study proposes an improved match factor method that incorporates these critical variables. It includes equipment downtime, truck cycle time estimates based on travel routes, and material fragmentation. The model applies to both homogeneous and heterogeneous fleet configurations and integrates the operational efficiency coefficient of each machine to reflect real conditions more accurately. The model was tested using data from the Sungun copper mine. The match factor values were calculated both with and without accounting for equipment downtime, and loader capacities were adjusted according to the size distribution of blasted material. Results showed that in heterogeneous fleet operations, the match factor increased from 0.74 to 0.85 when operational efficiency was included. Subsystem analyses also revealed match factor values below 1, indicating a need for additional trucks. Overall, the enhanced model enables more efficient equipment use, reduces loader idle time, and contributes to substantial operating-cost savings.
Exploitation
Hossein Mirzaei Nasir Abad; Mehrnaz Mohtasham; Farshad Rahimzadeh-Nanekaran
Abstract
Transportation of materials is the most cost-intensive component in open-pit mining operations. The aim of the allocation models is to manage and optimize transportation activities, leading to reduced wasted time, and ultimately, increasing profitability while reducing operational costs. Given that the ...
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Transportation of materials is the most cost-intensive component in open-pit mining operations. The aim of the allocation models is to manage and optimize transportation activities, leading to reduced wasted time, and ultimately, increasing profitability while reducing operational costs. Given that the implementation of allocation models is one of the essential requirements in Iranian mining operations, this research work focuses on the transportation system in the Sungun copper mine, one of the largest mines in Iran, and highlights the challenges faced by the fixed allocation approach. The aim is to develop and implement a mathematical model to evaluate its performance, and suggest improvements. The allocation model attempts to optimize truck capacity utilization and maximize mining production. Implementing the model in the mine results in a 13.42% increase in total production compared to the conventional method, with a cost increase of 14.7%. The model shows the potential to meet operational and technical constraints to achieve optimal production. Overall, the developed model, with optimized management and improved fleet efficiency, outperforms the traditional haulage method in the mine.
Exploitation
M. Mohtasham; H. Mirzaei Nasirabad; A. Mahmoodi Markid
Abstract
Truck and shovel operations comprise approximately 60% of the total operating costs in open pit mines. In order to increase productivity and reduce the cost of mining operations, it is essential to manage the equipment used with high efficiency. In this work, the chance-constrained goal programing (CCGP) ...
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Truck and shovel operations comprise approximately 60% of the total operating costs in open pit mines. In order to increase productivity and reduce the cost of mining operations, it is essential to manage the equipment used with high efficiency. In this work, the chance-constrained goal programing (CCGP) model presented by Michalakopoulos and Panagiotou is developed to determine an optimal truck allocation plan in open pit mines and reduce the waiting times of trucks and shovels. The developed goal programming (GP) model is established considering four desired goals: “maximizing shovel production”, “minimizing deviations in head grade”, “minimizing deviations in tonnage feed to the processing plants from the desired feed” and “minimizing truck operating costs”. To employ the developed model, a software is prepared in Visual Studio with C# programming language. In this computer program, the CPLEX optimizer software is incorporated for solving the developed goal programing model. The case study of Sungun copper mine is also considered to evaluate the presented GP model and prepared software. The results obtained indicate that the developed model increases the mine production above 20.6% with respect to the traditional truck allocation plan, while meeting the desired grade and the stripping ratio constraints.
