فصلنامه مهندسی معدن
پیاپی 59 (تابستان 1402)

Significant amounts of minerals accumulated in depots are scattered by wind and will cause environmental pollution. The potential of microbial induced calcium carbonate (Biogrouting) is investigated in stabilization of mineral accumulations against wind. In this method, the components of the unstable geomaterials (minerals or crushed and accumulated tailings) are bonded together using calcium carbonate deposition created by the action of the Sporosarcina pasture bacteria, and will be stable against wind. In this study, laboratory scale accumulations of Leach waste from Angouran lead and zinc mineral processing plant were tested under different amounts of grout base materials (different volumes of bacterial solution, calcium chloride and urea) and different wind speeds. In order to design laboratory models, the response surface method with 5 levels of variation was used and led to 31 laboratory tests. In the following of the research, the optimal consumption values of the grout components were calculated and presented. The results of tests showed that 22% of the accumulated soil are scattered with a wind speed of 10 meters per second. This depot can be stabilized using biogrout and scattered materials can be reduced till 0.54% in one stage improvement process (under bacterial solution, calcium chloride and urea about 0.56 ml/cm2).

Geochemical halos are regions around mineral deposits where the concentration of elements decreases to a constant level called the background. In the meantime, in order to find out the unusual concentration of elements (anomaly), the upper limit of the background limit (threshold value), must be determined. To achieve this goal, it will be effective and efficient to use new exploration methods. In this article, to draw the potential areas of stream sediments, modeling the data of element enrichment index with the U-spatial statistic method has been used. According to the morphological characteristics of the stream sediments environment, the origin of the samples taken from these sediments goes back to the upstream of the watersheds, so the analysis of the geochemical data of these samples is different from the rock environments. A preliminary modeling should be done on the data to consider the geological features upstream of the samples. The isotropic nature of the window in which the weighted averaging is performed in the algorithm of the U-spatial statistics method is such that all the upstream and downstream samples participate in this averaging and this case can be the point considered a significant weakness for this method. To reduce this weakness and to improve the method, first by considering the upstream rock units, the element enrichment index was obtained on the raw data, and then the U statistics was modeled on these indices. With this method, while the indices of the area were more accurately identified, the false potential areas obtained from the U-spatial statistic method were also removed and the results were acceptable and close to the field realities for determining mineralization potential areas.

With the advent of big data in geosciences, exploration studies have entered new dimensions. Big data means high resolution image information. Since these data in geosciences have a very large volume and variety, it is necessary to use big data analysis approaches in this field. In this study, the application of support vector machine in machine vision in the field of mineral potential exploration is investigated. In recent years, image classification has attracted a lot of attention in machine vision, whose processes include pre-processing and segmentation, feature extraction and related class identification. In this study, geological maps and remote sensing images are used to model the exploration of minerals potentials, and Alexnet architecture is used to automatically extract features, and field information is used to learn the algorithm. In the next step, support vector machine is used for modeling in order to identify structure factors in the occurrence probability of minerals potentials. Algorithms and evaluation indicators are programmed in MATLAB environment at each stage. The accuracy obtained using this method is 71% on the test data. According to the previous study conducted by the authors in identifying mineralization structures, the average accuracy of image data classification using convolutional neural network algorithms is 65%, the spectral angle mapper method in identifying alteration zones is 70% and applying filters in identifying faults is 28%. As can be seen, the method used in this research is highly accurate. Its advantages include reducing costs and speeding up the decision-making processes.

Prediction of gas emissions is a calculation based on empirical processing in the coal mining. In this study, methane diffusion was investigated coal exploration on parameters such as coal gas content, mining, depth, etc., in two faces in the Tabas Parvadeh central coal mining. Therefore, in the beginning, the initial and final values for the studied faces were modeled based on the neural network. The neural network was calculated methane for P8/TG face, between 0.4476%-0.9921% and P8/MG face are between 0.1636%-0.3379%. The intensity of methane gas emission from the studied mine was done based on the level of coal and the amount of gas volume using MATLAB coding. Also, the type of extraction method in the mine was done on the amount of gas emission caused by mining. According to the amount and aggregate of induced fractures, the permeability shows several degrees of increase. These amounts can evacuation the gas caused by the underground extraction of the mine faster. In this study, the amount of gas emissions due to mining was calculated using coding to the content of the remaining gas matrix In this study, the amount of gas emission due to drilling was 0.04%, blasting was 0.045%, support was 0.040%, muckout was 0.06% and jiggring was 0.25%. Small errors in the measurement of gas content can lead to large errors in the estimated calculations, which were determined using the software. The correlation coefficient of input and output of the studied data was estimated as 0.984%. The modeling done for the 2 face current shows a good result which can be used to improve mine safety.

In the current study, the two samples were taken kindly from the Sungun copper ore. Initially, more contemporary preparations on the samples were made to intensify their X-Ray patterns. Then, clay minerals were identified by inspecting their XRD patterns. To determine the quantity of clay minerals their XRD profiles were simulated using Rietveld method based on their crystallographic information. In addition, standard samples using high purity components of interest clay minerals were made by mixing for a given weight per cent. The obtained results indicated that the main types of clay minerals in the Sungun ore samples are Illite, Kaolinite and Muscovite. The comparison of the results revealed that the Rietveld method yields more valid and reliable results rather than the standard. The maximum error in determination of clay minerals for standard samples by using the Rietveld method was 3.12%. The results proved that applying whole profile fitting on XRD patterns give more reliable data than using single line fitting of XRD patterns. Further, the separation of clay minerals via contemporary preparations from the samples is crucial commission since other phases into the samples decrease dramatically the intensity and broadening of clay minerals peaks in XRD patterns, subsequently increasing the simulation error.

Predicting penetration rate of tunnel boring machines (TBM) is a decisive factor in scheduling and budgeting of tunnelling projects. This paper is aimed at predicting the TBM penetration rate (ROP) in the southern lot of Kerman water conveyance tunnel by means of Group method of Data Handling (GMDH) neural network. Having compiled the database using the geomechanical data of rock mass and machine performance data, correlations between ten various parameters were explored and two linear and nonlinear multivariate regression equations for the penetration rate were set up. Multi-objective Genetic Algorithm in the form of bi-objective optimization was applied for designing the optimal structure of the network and the dataset was randomly divided into training subset (70% of the total data) and test subset (the remaining 30%) as two objective functions. A multi-layered polynomial penetration rate function in terms of the parameters having the strongest correlation with ROP, i.e. compressive strength of the rock mass, quartz content, the angle between plane of weakness and TBM-driven direction, and the average force acting on the single cutter was obtained. Application of the rock mass compressive strength led to reducing the number of involving parameters and making the prediction model simpler. The comparison of the observed and predicted values showed high determination coefficient (R2) of 0.81 (R2=0.6 for nonlinear multivariate regression) which reveals high prediction capability of the proposed GMDH model. Unlike other neural network prediction models which produce their outputs as a “black box”, the suggested ROP GMDH model was expressed as a recurrent polynomial function in terms of the inputs. This outstanding feature of the GMDH model enables the proposed prediction model to be used in other projects as well as future research.

Selection of tunnel excavation method is one of the most important issues raised in the tunneling projects Because experience has shown that the results of incorrect choice of excavation method can lead to catastrophic situations. Various parameters are effective in choosing the right option for excavation a tunnel. The most important parameters include geological and geotechnical, geometric, executive, economic parameters and parameters related to infrastructure and necessary conditions for tunneling methods. in this research, a model for comprehensive evaluation and selection of the most suitable tunnel excavation method from the methods of drill and blasting, controlled drill and blasting, cut and cover, Austrian drilling method, road header machine and conventional shield excavation (TBM) is presented.In this model, after studying and reviewing the relevant topics, the indicators that are effective in evaluation were considered and since the importance of the indicators in the evaluation is not the same, a questionnaire was prepared to determine their weight and their weight was determined by aggregating the opinions of experts. Scoring for each of the indicators for each of the excavation methods was done by evaluation questionnaires containing questions in numerical quantification. Finally, the simple weighting method (SAW) was used hierarchically to calculate the final score of each method. In this method, according to the geological and geotechnical conditions and the geometric parameters of the existing tunnel, the score of each index is multiplied by the final weight of that index and from the sum of them, the final score of each method is obtained. Finally, the method with the highest score is selected as the appropriate excavation method.