S. Alamdari; M.H. Basiri; A. Mousavi; A. Soofastaei
Abstract
The haul trucks consume a significant energy source in open-pit mines, where diesel fuel is widely used as the main energy source. Improving the haul truck fuel consumption can considerably decrease the operating cost of mining, and more importantly, reduce the pollutants and greenhouse gas emissions. ...
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The haul trucks consume a significant energy source in open-pit mines, where diesel fuel is widely used as the main energy source. Improving the haul truck fuel consumption can considerably decrease the operating cost of mining, and more importantly, reduce the pollutants and greenhouse gas emissions. This work aims to model and evaluate the diesel fuel consumption of the mining haul trucks. The machine learning techniques including multiple linear regression, random forest, artificial neural network, support vector machine, and kernel nearest neighbor are implemented and investigated in order to predict the haul truck fuel consumption based on the independent variables such as the payload, total resistance, and actual speed. The prediction models are built on the actual dataset collected from an Iron ore open-pit mine located in the Yazd province, Iran. In order to evaluate the goodness of the predicted models, the coefficient of determination, mean square error, and mean absolute error are investigated. The results obtained demonstrate that the artificial neural network has the highest accuracy compared to the other models (coefficient of determination = 0.903, mean square error = 489.173, and mean absolute error = 13.440). In contrast, the multiple linear regression exhibits the worst result in all statistical metrics. Finally, a sensitivity analysis is used to evaluate the significance of the independent variables.
K. Tolouei; E. Moosavi; A.H. Bangian Tabrizi; P. Afzal; A. Aghajani Bazzazi
Abstract
It is significant to discover a global optimization in the problems dealing with large dimensional scales to increase the quality of decision-making in the mining operation. It has been broadly confirmed that the long-term production scheduling (LTPS) problem performs a main role in mining projects to ...
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It is significant to discover a global optimization in the problems dealing with large dimensional scales to increase the quality of decision-making in the mining operation. It has been broadly confirmed that the long-term production scheduling (LTPS) problem performs a main role in mining projects to develop the performance regarding the obtainability of constraints, while maximizing the whole profits of the project in a specific period. There is a requirement for improving the scheduling methodologies to get a good solution since the production scheduling problems are non-deterministic polynomial-time hard. The current paper introduces the hybrid models so as to solve the LTPS problem under the condition of grade uncertainty with the contribution of Lagrangian relaxation (LR), particle swarm optimization (PSO), firefly algorithm (FA), and bat algorithm (BA). In fact, the LTPS problem is solved under the condition of grade uncertainty. It is proposed to use the LR technique on the LTPS problem and develop its performance, speeding up the convergence. Furthermore, PSO, FA, and BA are projected to bring up-to-date the Lagrangian multipliers. The consequences of the case study specifies that the LR method is more influential than the traditional linearization method to clarify the large-scale problem and make an acceptable solution. The results obtained point out that a better presentation is gained by LR–FA in comparison with LR-PSO, LR-BA, LR-Genetic Algorithm (GA), and traditional methods in terms of the summation net present value. Moreover, the CPU time by the LR-FA method is approximately 16.2% upper than the other methods.
Exploitation
A. Mozafari; A. H. Bangian Tabrizi; M. Taji; A. Parhizkar
Abstract
In this paper, we present an integrated model to find the optimum size of blast block that uses (i) a multi-criteria decision-making method to specify the applicable size of the mineable block; (ii) a linear programming method for the selection of the blasted areas to be excavated and in deciding the ...
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In this paper, we present an integrated model to find the optimum size of blast block that uses (i) a multi-criteria decision-making method to specify the applicable size of the mineable block; (ii) a linear programming method for the selection of the blasted areas to be excavated and in deciding the quantity of ores and wastes to be mined from each one of the selected blocks. These two methods use improved estimates of the orebody characteristics utilizing the blast hole data in addition to the usual borehole statistics to improve the prediction accuracy of the block level ore body characteristics. This work aims to make a mathematical model to figure out the ideal width and length of the blast block in order to curtail drilling and blasting expenses in open-pit mines. As a consequence, the effective blast block size is heeded so as to decrease the expenses of drilling and blasting. Furthermore, a complete set of actual principles is presented to specify the applicable size of the mineable block by means of the multi-criteria decision-making method of fuzzy logic. The aforementioned model is practiced to forecast the block size necessary for the purpose of production planning. Next, a mixed integer programming model is developed to blast planning in order to select the optimal size of the blast block by considering the mineable block. The proposed model is applied in the Chadormalu iron ore mine and the rationality of the model is demonstrated by the outcomes of dissimilar circumstances.
Rock Mechanics
H. Zebarjadi Dana; R. Khaloo Kakaie; R. Rafiee; A.R. Yarahmadi Bafghi
Abstract
Slope stability analysis is one of the most important problems in mining and geotechnical engineering. Ignoring the importance of these problems can lead to significant losses. Selecting an appropriate method to analyze the slope stability requires a proper understanding of how different factors influence ...
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Slope stability analysis is one of the most important problems in mining and geotechnical engineering. Ignoring the importance of these problems can lead to significant losses. Selecting an appropriate method to analyze the slope stability requires a proper understanding of how different factors influence the outputs of the analyses. This paper evaluates the effects of considering the real geometry, changes in the mesh size, and steepness of the slope, as the dimensional effects, and changes in the geomechanical parameters, as the media effects on the global slope stability of an open-pit mine using finite difference methods with a strength reduction technique. The case study is the Tectonic Block I in the old pit (steep slope) and the redesigned new pit (gentle slope) of the Choghart iron mine. In the first step, a series of 2D and 3D slope stability analyses are performed and compared in terms of safety and potential failure surface. The results obtained show that by considering the real geometry of the slope, the FOS3D/FOS2D ratio (3D-effect) is more than 1 in the all cases. The 3D-effect in the new pit is smaller than that in the old one. In the next step, sensitivity analysis of the cohesion and the friction angle is performed for the 2D and 3D analyses. The results obtained show that the sensitivity of the analyses in terms of the 3D-effect to the change in the friction angle, especially in a low-friction angle, is more significant than that to the change in the cohesion.
Ali Asghar khodaiari; A Jafarnejad
Abstract
Maximizing economic earnings is the most common goal in cut-off grade optimization of open-pit mining operations. When this is the case, the price of the product has a critical effect on optimum value of cut-off grade. This paper investigates the relationship between optimum cut-off grade and price to ...
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Maximizing economic earnings is the most common goal in cut-off grade optimization of open-pit mining operations. When this is the case, the price of the product has a critical effect on optimum value of cut-off grade. This paper investigates the relationship between optimum cut-off grade and price to maximize total cash flow and net percent value (NPV) of operation. In order to visualize this relationship, two hypothetical mines were employed. To determine the optimum value of cut-off grade in different cases, two nonlinear programming models were formulated, and then, all models were solved using Solver in Excel. The results show that the optimum cut-off grade would always be a descending function of price when we intend to maximize total cash flow. On the other hand, this function may be descending or ascending when we intend to maximize NPV. This result also reveals that both maximum cash flow and maximum NPV always increase and decrease, respectively when the price of product increases or decreases.
