JOURNAL OF MINERAL RESOURCES ENGINEERING
Year:2020 | Volume:4 | Issue:4 |Papers Count:8

Nowadays, artificial intelligence methods have been broadly developed and applied for variable estimation to facilitate decision making in many fields. Grade estimation is an important issue in evaluating mineral deposits. Geostatistical methods are among the most commonly used approaches for variable estimation. Since these methods are somewhat defective in relation to limited numbers of dispersed nonlinear data, in this study, the support vector regression, a machine learning method, has been used for grade estimation in Esfordi phosphate deposit. The modeling accuracy was 84% according to the test data. Based on the results obtained from the modeling using the support vector regression method, grade estimation has been made within the block model in Esfordi phosphate deposit. The proposed potential areas in the block model can be taken as the the additional borehole sites in the further exploration stage. The tonnage-grade model was also prepared based on the results obtained by using the support vector regression modeling procedure. For example, based on this model, for a 6% cutoff grade, the reserve is about 15. 36 million tons with an average grade of 13. 59%.

Designing of ventilation networks is known a manual and computerized methods. Computerized method is carried out based on approximate mathematical approaches. Several approximate mathematical methods such as the Newton-Raphson, Hardy Cross and its corrected models, optimization techniques, critical path, linear and nonlinear methods can be used to design ventilation networks. One of the techniques for solving nonlinear equations in mathematical science is the Newton-Raphson method that is based on the derivative definition and its correction. This method is unable to produce valid results and also instead of convergence leads to divergence in some models. In addition, utilizing the Newton-Raphson method for analyzing of large-scale ventilation networks requires a lot of calculations. Therefore, in this paper a new method is presented naming Newton-Raphson method without derivatives. This new method is always convergent, capble to reduce mathematical calculations and it reaches to the result fast.

Gas leakage through the cracks and tracks surrounding Underground Coal Gasification (UCG) is of criteria affecting the feasibility of economic methods of the UCG reactor. In terms of process control and groundwater contamination capacity, the sealing of the UCG reactor is very important. Various factors affect the gas leakage from the UCG reactor. In this paper, the parameters of pressure, temperature and joint characteristics (including opening, length and intensity) as the most important factors affecting gas leakage through rock mass fractures are examined using numerical modeling. For this purpose, The Mazino Tabas coal area is studied as a case study. The DFN-FRAC3D computer program is used to stochastically simulate joints and create an equivalent pipe network. Also, Water Gems software is used for flow analysis. The results are shown that increasing the reactor pressure and increasing the rock mass jointing around the reactor increases the flow rate and the gas leakage. On the other hand, increasing the reactor temperature does not have a significant effect on the output flow rate. Besides, jointing is the most effective factor in fluid leakage through the UCG reactor. Among the geometrical features of the joint, including the intensity, the opening and the length of the joint, the joint intensity factor has been introduced as the most important factor.

The Coal Mine Methane (CMM) process involves: exploration, extraction and drainage of methane gas in underground mining during extraction process. Various factors affect the CMM operation efficiency. Investigating the relationship between these factors and their effect on the process of CMM is very important which has been less attractive to researchers so far. In this research, the effective criteria on the efficiency of CMM were identified using previous studies and expert opinions. Then, the criteria were classified in different levels using the Interpretative Structural Modeling (ISM) method. Then, the underlying relationships between the variables were determined using the experience and practical knowledge of the experts. Finally, a multi-level structural model was created. The results of this study indicated that the coal rank, geomechanical parameters and technical specifications of drilling holes of gas drainage are in the first level with the maximum impact on the process of CMM of underground coal mines. The capital and operational costs related to the CMM process are also at the fifth level of the model and have the least effect on CMM operation. Eventually, by charting the “ Matrice d’ Impacts Croises-Multiplication Appliqú e an Classment” (MICMAC) and its review, the categorization was done for each criterion based on the degree of influence and dependency. The results of this research can be used for management purposes and methane optimization of underground coal mines.

The importance and necessity of using underground spaces in the present era is not overlooked. Caverns are used as an important bunch of underground spaces for the storage of radioactive waste, hydrocarbons, and hydroelectric power plant projects. Construction of caverns has particular complexity and water seepage into the caverns is one of the most important problems. The purpose of the present study was to investigate the 3D modeling of the fracture network of a cavern for the pumped storage power plant of Rudbar Lorestan dam by using DFN method and 3DEC software and then determining the tensor of hydraulic conductivity by using the model. To achieve the goal, statistical analyses were performed on the in situ data extracted from the area. Power law distribution was determined as a suitable statistical distribution proportional to the data. Then, the DFN model was created and validated using the Watson-Williams test. In the following, a block model of DFN made from the fractures was constructed to determine the Representative Elementary Volume for different dimensions from 1 to 12 m. It was found that a 7-meter model was appropriate. Furthermore, the tensor of hydraulic conductivity was determined by using a hydraulic head on a generated REV.

The cyanide is a highly toxic compound that is normally found in industrial wastewaters. Due to its high environmental risks, its removal is of the great importance. The clay minerals are considered to be good candidates of cyanide removal from the wastewaters. In this paper, the effect of absorbent quantity and time on the cyanide absorption using smectite clay in a batch system has been studied as well as the kinetics modeling. The clay sample from Mehrjan area with the dimension of smaller than 53 μ m was used to remove the cyanide from industrial wastewaters that contains cyanide content [500± 10 ppm]. The result of the XRF and semi-quantitative XRD analysis showed that the clay mineral used in this study is sodium montmorillonite with a weight percentage of 76. The effect of parameters such as different contact time and the clay quantities were investigated by clay absorbent. A 10 times increase in the clay quantity within the solution results in 38% more reduction of the cyanide content. The maximum absorption (about 80%) was observed at 6 hours of contact time using 2 grams of clay in a 50mm solution. The result of kinetics modeling showed that process was a pseudo second order adsorption reaction and the isotherms could be described by the Freundlich equation. Based on the results, several advantages of clays such as fast preparation procedure, accessibility and availability, and the reasonable price makes them appropriate replacement for activated carbon for cyanide removal from the industrial wastewaters in the future.

In the recent years, a wide range of contaminated soil by chromium has been reported in the mining and industrial area. Soil contamination by chromium is one of the most important environmental problems that adversely affects the health of living organisms and human beings. In this regard, electrokinetic is an effective method for soil remediation. This research, aims to investigate the effect of time and pH control on increasing the efficiency of the electrokinetic to remove chromium from contaminated soil. Pourbaix diagram used to interpret the results of experiments. According to the Pourbaix diagram, at pH = 6 trivalent chromium cationic species are dominant at this pH. As a result, these species have been migrated to the cathode. On the anode side, there is also an oxidation environment, which is caused the anionic chromium species transferred and migrated to the anode side. As a result, the phenomenon of electro-migration is the dominant mechanism for the transmission of chromium. At pH less than 7, chromium occurs in the form of cationic species and at pH greater than 7, occurs as anionic or hydroxide species. Results revealed that, reservoir pH control is an acceptable method for increasing chromium extraction efficiency. The results obtained from electrokinetic experiments indicate that this method is more efficient at five days compared to three days. The best removal efficiency of chromium from soil is 68% in 5 days experiment without pH control and 63% in 5 days test with pH control.

The purpose of this research was to study various plant species and to model the absorption behavior thereof in relation to the various elements in and around of Darreh Zereshk Copper Mine to find the most suitable indigenous species for phytoremediation study. The goal is reducing the impacts and environmental pollution of the pollutant elements. For this purpose, 36 plant samples (including 17 different species) and 32 soil samples (in two different size fractions) were taken. After preparation and application of different digestion methods, the samples were analyzed by atomic absorption method for copper, iron and lead elements. By using four different solvent extraction methods, the absorption behavior of the plants was modeled and the best method was identified as extracted by organic solvent EDTA. This solvent could better detect the absorption behavior of the plants and distinguish contaminated areas from virgin and non-contaminated areas. The soil with smaller size fraction is more enriched of the metals and the copper presented in this fraction is absorbed by the solvent at higher concentrations. This suggests that the fine particles of the soil scavenged a higher content of metal by absorption property. By calculating the bioconcentration factor, the absorption behavior of different plant species was compared in contaminated and non-contaminated areas, and accordingly, the species of “ Hertia Angustifolia” , “ Glycyrrhiza Globra” and “ Euphorbia” were identified as accumulator species and “ Tamarix” , “ Alhagi” , “ Astragalus” and” Artemisia Sieberi Besser” determined as hyper-excluder species. “ Hertia Angustifolia” species has a fairly good abundance in the region, therefore it can be introduced and used as an indigenous indicator species for the purpose of phytoremediation.