Exploitation
R. Shamsi; M. S. Amini; H. Dehghani; M. Bascompta; B. Jodeiri Shokri; Sh. Entezam
Abstract
This paper attempted to estimate the amount of flyrock in the Angoran mine in Zanjan province, Iran using the gene expression programming (GEP) predictive technique. The input data, including flyrock, mean depth of the hole, powder factor, stemming, explosive weight, number of holes, and booster were ...
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This paper attempted to estimate the amount of flyrock in the Angoran mine in Zanjan province, Iran using the gene expression programming (GEP) predictive technique. The input data, including flyrock, mean depth of the hole, powder factor, stemming, explosive weight, number of holes, and booster were collected from the mine. Then, using GEP, a series of intelligent equations were proposed to predict flyrock distance. The best GEP equation was selected based on some well-established statistical indices in the next stage. The coefficient of determination for training and testing datasets of the GEP equation were 0.890 and 0.798, respectively. The model obtained from the GEP method was then optimized using teaching– learning-based optimization algorithm (TLBO). Based on the results, the correlation coefficient of training and testing data increased to 91% and 89%, which increased the accuracy of the Equation. This new intelligent equation could forecast flyrock resulting from mine blasting with a high level of accuracy. The capabilities of this intelligent technique could be further extended to the other blasting environmental issues.
F. Hadadi; B. Jodeiri Shokri; M. Zare Naghadehi; F. Doulati Ardejani
Abstract
In this paper, we investigate a probabilistic approach in order to predict how acid mine drainage is generated within coal waste particles in NE Iran. For this, a database is built based on the previous studies that have investigated the pyrite oxidation process within the oldest abandoned pile during ...
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In this paper, we investigate a probabilistic approach in order to predict how acid mine drainage is generated within coal waste particles in NE Iran. For this, a database is built based on the previous studies that have investigated the pyrite oxidation process within the oldest abandoned pile during the last decade. According to the available data, the remaining pyrite fraction is considered as the output data, while the depth of the waste, concentration of bicarbonate, and oxygen fraction are the input parameters. Then the best probability distribution functions are determined on each one of the input parameters based on a Monte Carlo simulation. Also the best relationships between the input data and the output data are presented regarding the statistical regression analyses. Afterward, the best probability distribution functions of the input parameters are inserted into the linear statistical relationships to find the probability distribution function of the output data. The results obtained reveal that the values of the remaining pyrite fraction are between 0.764% and 1.811% at a probability level of 90%. Moreover, the sensitivity analysis carried out by applying the tornado diagram shows that the pile depth has, by far, the most critical factors affecting the pyrite remaining
B. Jodeiri Shokri; H. Dehghani; R. Shamsi; F. Doulati Ardejani
Abstract
This work presents a quantitative predicting likely acid mine drainage (AMD) generation process throughout tailing particles resulting from the Sarcheshmeh copper mine in the south of Iran. Indeed, four predictive relationships for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate ...
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This work presents a quantitative predicting likely acid mine drainage (AMD) generation process throughout tailing particles resulting from the Sarcheshmeh copper mine in the south of Iran. Indeed, four predictive relationships for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate concentration, and pH have been suggested by applying the gene expression programming (GEP) algorithms. For this, after gathering an appropriate database, some of the most significant parameters such as the tailing particle depths, initial remaining pyrite and chalcopyrite fractions, and concentrations of bicarbonate, nitrite, nitrate, and chloride are considered as the input data. Then 30% of the data is chosen as the training data randomly, while the validation data is included in 70% of the dataset. Subsequently, the relationships are proposed using GEP. The high values of correlation coefficients (0.92, 0.91, 0.86, and 0.89) as well as the low values of RMS errors (0.140, 0.014, 150.301, and 0.543) for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate concentration, and pH prove that these relationships can be successfully validated. The results obtained also reveal that GEP can be applied as a new-fangled method in order to predict the AMD generation process.
