Zohreh Nabavi; Mohammad Mirzehi; Hesam Dehghani; Pedram Ashtari
Abstract
Back-break is one of the adverse effects of blasting, which results in unstable mine walls, high duration, falling machinery, and inappropriate fragmentation. Thus, the economic benefits of the mine are reduced, and safety is severely affected. Back-break can be influenced by various parameters such ...
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Back-break is one of the adverse effects of blasting, which results in unstable mine walls, high duration, falling machinery, and inappropriate fragmentation. Thus, the economic benefits of the mine are reduced, and safety is severely affected. Back-break can be influenced by various parameters such as rock mass properties, blast geometry, and explosive properties. Therefore, during the blasting process, back-break must be accurately predicted, and other production activities must be done to prevent and reduce its adverse effects. In this regard, a hybrid model of extreme gradient boosting (XGB) is proposed for predicting back-break using gray wolf optimization (GWO) and particle swarm optimization (PSO). Additionally, validation of the hybrid model is conducted using XGBoost, gene expression programming (GEP), random forest (RF), linear multiple regression (LMR), and non-linear multiple regression (NLMR) methods. For this purpose, the data obtained from 90 blasting operations in the Chadormalu iron ore mine are collected by considering the parameters of the blast pattern design. According to the results obtained, the performance and accuracy level of hybrid models including GWO-XGB (R2 = 99, RMSE = 0.01, MAE = 0.001, VAF = 0.99, a-20 = 0.98), and PSO-XGB (99, 0.01, 0.001, 0.99, 0.98) are better than the XGBoost (97, 0.185, 0.132, 0.98, 95), GEP (96, 0.233, 0.186, 0.967, 0.935), RF (97, 0.210, 0.156, 0.97, 0.94), LMR (96, 0.235, 0.181, 0.964, 0.92), and NLMR (96, 0.229, 0.177, 0.968, 0.93) models. Notably, the GWO-XGB hybrid model has superior overall performance as compared to the PSO-XGB model. Based on the sensitivity analysis results, hole depth and stemming are the essential effective parameters for back-break.
J. Shakeri; H. Amini Khoshalan; H. Dehghani; M. Bascompta; K. Onyelowe
Abstract
In this research work, a comprehensive study is conducted to predict flyrock as a typical and undesirable phenomenon occurring during the blasting operation in open-pit mining. Despite the availability of several empirical methods for predicting the flyrock distance, the complexity of flyrock analysis ...
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In this research work, a comprehensive study is conducted to predict flyrock as a typical and undesirable phenomenon occurring during the blasting operation in open-pit mining. Despite the availability of several empirical methods for predicting the flyrock distance, the complexity of flyrock analysis has resulted in the low performance of these models. Therefore, the statistical and robust artificial intelligence techniques are applied for flyrock prediction in the Sungun copper mine in Iran. For this purpose, the linear multivariate regression (LMR), imperialist competitive algorithm (ICA), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods are applied to predict flyrock with effective parameters including the blasthole diameter, stemming, burden, powder factor, and maximum charge per delay. According to the attained results, the ANN model with the structure of 5-8-1, Levenberg-Marquardt as the learning algorithm, and log-sigmoid (logsig) as the transfer functions are selected as the optimal network with the RMSE and R2 values of 5.04 m and 95.6% to predict flyrock, respectively. Also it can be concluded that the ICA technique has a relatively high capability in predicting flyrock, with the LMR and ANFIS models placed in the next. Finally, the sensitivity analysis reveal that the powder factor and blasthole diameters have the most importance on the flyrock distance in the present work.
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.
H. hadizadeh Ghaziania; M. Monjezi; A. Mousavi; H. Dehghani; E. Bakhtavar
Abstract
The production cycle in open-pit mines includes the drilling, blasting, loading, and haulage. Since loading and haulage account for a large part of the mining costs, it is very important to optimize the transport fleet from the economic viewpoint. Simulation is one of the most widely used methods in ...
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The production cycle in open-pit mines includes the drilling, blasting, loading, and haulage. Since loading and haulage account for a large part of the mining costs, it is very important to optimize the transport fleet from the economic viewpoint. Simulation is one of the most widely used methods in the field of fleet design. However, it is unable to propose an optimized scenario for which the appropriate metaheuristic method should be employed. This paper considers the Sungun copper mine as the case study, and attempts to find the most feasible transportation arrangement. In the first step, in this work, we compare the flexible dispatching with the fixed allocation methods using the Arena software. Accordingly, the use of flexible dispatching reveals the increase in the production rate (20%) and productivity (25%), and the decrease (20%) in the idle time. The firefly metaheuristic algorithm used in the second step shows that the combined scenario of the 35-ton and 100-ton trucks is the most suitable option in terms of productivity and cost. In another attempt, comparing different heterogeneous truck fleets, we have found that the scenarios 35-100 and 35-60-100-144 increase the production rate by 39% and 49%, respectively. Also, in both scenarios, the production cost decreases by 11% and 21%, respectively.
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.
Mine Economic and Management
H. Dehghani
Abstract
Forecasting the prices of metals is important in many aspects of economics. Metal prices are also vital variables in financial models for revenue evaluation, which forms the basis of an effective payment regime using resource policymakers. According to the severe changes of the metal prices in the recent ...
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Forecasting the prices of metals is important in many aspects of economics. Metal prices are also vital variables in financial models for revenue evaluation, which forms the basis of an effective payment regime using resource policymakers. According to the severe changes of the metal prices in the recent years, the classic estimation methods cannot correctly estimate the volatility. In order to solve this problem, it is necessary to use the artificial algorithms, which have a good ability to predict the volatility of various phenomena. In the present work, the gene expression programming (GEP) method was used to predict the copper price volatility. In order to understand the ability of this method, the results obtained were compared with the other classical prediction methods. The results indicated that the GEP method was much better than the time series and multivariate regression methods in terms of the prediction accuracy.
H. Dehghani; A. Siami; P. Haghi
Abstract
One of the most important steps involved in mining operations is to select an appropriate extraction method for mine resources. After choosing the extraction method, it is usually impossible to replace it with another one because it may be so expensive that implementation of the entire project could ...
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One of the most important steps involved in mining operations is to select an appropriate extraction method for mine resources. After choosing the extraction method, it is usually impossible to replace it with another one because it may be so expensive that implementation of the entire project could be economically impossible. Choosing a mining method depends on the geological and geometrical characteristics of the mine. Due to the complexity of the process of choosing an appropriate mining method and the effect of the parameters involved on the results of this process, it is necessary to utilize the new decision-making methods that have the ability to consider the relationship between the existing parameters and the mining methods. Grey and TODIM (an acronym in Portuguese, i.e. Tomada de Decisão Interativa Multicritério) decision-making methods are among the existing ones, which in addition to the convenience, show high accuracy. The proposed models are presented to determine the best mining method in the Gol-e-gohar iron ore mine in Iran. The results obtained are compared with the methods used in the previous research works. Among the decision-making methods introduced, the open pit mining method is the most appropriate option and the square-set mining is the worst one.
H. Dehghani; N. Mikhak Beiranvand
Abstract
One of the most important parameters used for determining the performance of tunnel boring machines (TBMs) is their penetration rate. The parameters affecting the penetration rate can be divided in two categories. The first category is the controllable parameters such as the TBM technical characteristics, ...
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One of the most important parameters used for determining the performance of tunnel boring machines (TBMs) is their penetration rate. The parameters affecting the penetration rate can be divided in two categories. The first category is the controllable parameters such as the TBM technical characteristics, and type and geometry of the tunnel, and the second one is the uncontrollable parameters such as the intact rock properties and characteristics of the rock mass discontinuities. The aim of this work was to investigate the effects of rock mass properties on the penetration rate, and to present a new mathematical equation based on a statistical approach to estimate the TBM performance. To achieve this aim, the Monte-Carlo (MC) simulation method was used to model the TBM performance. Accordingly, the database consisting of the rock mechanics information such as the uniaxial compressive strength, Brazilian tensile strength, toughness and hardness of rock, spacing and orientation of discontinuities, and measured TBM penetration rate in 151 points out of a water tunnel was collected. Next, using the dimensional analysis, a comprehensive mathematical equation was obtained to calculate the TBM penetration rates using the developed database. Finally, using the MC simulation method, the probability distribution function of the TBM penetration rate was studied. The validation results obtained showed that the root mean square error (RMSE) of the proposed relationship was less than 0.3. The MC simulation results showed that hardness and density had the most and least effects on the penetration rate, respectively.
Amid Morshedlou; Hesam Dehghani; Seyed Hadi Hoseinie
Abstract
Utilizing the gathered failure data and failure interval data from Tabas coal mine in two years, this paper discusses the reliability of powered supports. The data sets were investigated using statistical procedures and in two levels: the existence of trend and serial correlation. The results show that ...
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Utilizing the gathered failure data and failure interval data from Tabas coal mine in two years, this paper discusses the reliability of powered supports. The data sets were investigated using statistical procedures and in two levels: the existence of trend and serial correlation. The results show that the powered supports follow the Gamma reliability function. The reliability of the machine decreases to almost zero after 520 operation hours and after 80 hours the probability of failure of powered supports increases to 60 percent. The failure rate of powered support shows an improving behavior and therefore a decreasing failure rate. In the beginning of the process, the failure rate is 0.021 failures per hour. This reaches the rate of 0.012 after a sudden decrease, thence forward on a gently decreasing rate and after 100 hours gets to the rate of 0.01. Regarding the maintenance policy and to protect the machine’s operation continuity, preventive maintenance strategy can be chosen. The reliability of the discussed machine can be maintained on a descent level by inspecting and controlling the parts in short term intervals. With regard to reliability plots of powered supports operation, preventive reliability-based maintenance time intervals for 80% reliability levels for powered supports is 15 hours.
