نشریه مهندسی منابع معدنی
سال نهم شماره 4 (زمستان 1403)

One of the most crucial steps in ore exploration is the recognition of geological patterns and features. These features contain mineralogy, lithology, alteration, rock texture, etc. This stage has always been associated with many challenges. Among the challenges of this stage, we can mention the time-consuming and costly nature of this stage and the need for high expertise and human resources to recognize these patterns and features. In recent years, deep learning and machine learning have been adopted in earth sciences. In this research, by using the architecture of U-net, ore and waste were separated, and the grade pattern was identified using the core box images. For this purpose, iron minerals were segmented using binary image segmentation, trial-and-error methods were used to optimize the network, and finally, the model's accuracy for identifying ore was 91%. The IoU metric was utilized for further evaluation; this metric is a suitable criterion for the final evaluation of the image segmentation model, which has reached 75% in recognition of iron ores. For the final evaluation of the obtained model, the grade outputs of the model and the XRF analysis results of one core were compared. The network error was evaluated at 9%, which shows the good accuracy of the obtained model according to the real data.

The 1:100,000-scale Porang sheet in South Khorasan province is prone to skarn, massive sulfide, and sedimentary mineralization due to the presence of intermediate to ultrabasic volcanic and plutonic rocks and the variety of sedimentary rocks. This paper introduces the Principal Coordinate Analysis (PCoA) method. The PCoA method, along with the Principal Component Analysis (PCA) and Correspondence Analysis (CA) methods, has been used to identify the possible type of mineralization in the study area. Geological and mineralogical data and the analysis results of 25 elements from 314 stream sediment samples, taken from the study area, have been used for this purpose. The results of the data analysis show that the D1 coordinate, PC1 score, and location in the first cluster maps of the samples are most likely related to the mineralization in ultrabasic, basic, and listivinite rocks. After that, the D2 and D3 dimension maps, the PC2 and PC5 score maps, and the sample location map in the fifth cluster related to sedimentary rocks attribute the most probability to sedimentary mineralization, especially of Mn and Fe mineralization types, in the study area. Finally, there is the possibility of skarn and massive sulfide mineralization, whose locations can be predicted by the D4 dimension maps, the PC3 score map, and the sample location maps in second, third, and fourth clusters. Also, the comparison of data analysis results with two multivariate statistical methods shows that by choosing the number of dimensionality reductions, the principal components method can cover more variability than the principal dimensions method. While connecting the principal coordinate maps to the mineralization is easier and more reliable than the principal component score maps. Therefore, the proposal of this paper is the simultaneous use of PCoA and PCA methods to analyze geochemical data in an exploration region.

In order to investigate the relationship between the management and executive components of the mine and to improve the safety level of the system, this paper introduces a new method for evaluating and managing the safety of the mining system. For this purpose, game theory has been used to solve mining safety issues. Game theory is a structured tool that can examine interactions between two or more players to understand their actions in certain situations. In this  paper, evolutionary game is used because of expressing the dynamics of relationships between players. The introduced model simulates the interactions between the government, managers, and miners under different conditions; also,the influence of regulatory factors on them in the system is evaluated. The results show that the effect of each factor on system safety is different and players affect the system safety according to each regulatory factor. Among these factors, the influence of punishments applied by game supervisors on the behavior of the mine management and mine workers was the greatest. So that with the increase of fines, the players adopted strategies aligned with safe production in a short time. Subsequently, the impact of government incentives was also investigated and it was determined that in exchange for increasing the reward factor to the management and mine workers, a change in strategy and safe production will be established in the mine.

Considering the consumption of more than 25% of energy by raw mill materials in cement plants, providing a suitable plan for blending different mining zones can be an effective approach in reducing the energy consumption. The purpose of this paper is optimizing the plan of blending limestone mining zones to reduce milling energy consumption raw mills (ball mill and HPGR). The geomechanical properties of different mine zones have been identified by performing the bond work index, drop weight, and uniaxial compressive strength (UCS) tests. Also, to comply with cement production standards, cementing modules including S.I.M, A.L.M, and L.S.F have been calculated for different mine zones. Mining production planning with the aim of achieving the minimum comminution energy and transportation costs has been developed by the Goal programming method. Planning constraint equations are also considered to regulate cement modules. Finally, utilizing windows Quantitative System for Business software, the necessary calculations have been carried out. Based on the results, the extraction order and blending values of different mine zones are adjusted regarding cement modules and minimizing transportation costs, leading to a reduction in energy consumption of raw mills.

In the blast phenomenon, the explosives release a lot of energy, which causes damage and fractures the rock mass. One of the influencing parameters in the blasting results is the way the blast hole is charged. Power Deck blasting has been introduced as a blasting method to reduce or eliminate sub-drilling, optimize crushing, and reduce undesirable results. The purpose of this research is to investigate the impact of the Power Deck blasting method on the borehole pressure and damage to the rock mass compared to the conventional method. In this regard, the numerical simulation of a single blast hole was carried out using conventional and Power Deck methods by using LS-DYNA software. The results showed that in the Power Deck method, in the area of the air deck, the initial pressure of the hole is reduced compared to conventional blasting, and secondary pressures are produced, which leads to a reduction in the expansion of the hole along the air deck by 45% and increases the destruction in multi-stage. On the other hand, the generation of secondary pressures in the Power Deck method causes a 30% and 48% increase in rock mass destruction in the area of the bottom of the hole compared to the conventional method. As a result, the Power Deck blasting method improves the amount of expansion and destruction by using less explosive, which indicates the optimal use of the explosive energy.

In the present study, the impact of immobilizing a BiOI-CuO heterojunction on clinoptilolite zeolite as a support for photocatalytic degradation of the organic pollutant methyl orange (MO) was investigated. To this aim, BiOI-CuO heterojunction photocatalysts with varying weight ratios of BiOI:CuO (2, 3, and 4) were synthesized and embedded in clinoptilolite matrix. Characterization techniques confirmed the successful synthesis of the photocatalysts. FESEM analysis revealed that immobilization of the heterojunction structure on the zeolite support reduced the number of agglomerations. This immobilization not only preserved the morphology of the semiconductors but also led to the formation of a more homogeneous and uniform structure. The results demonstrated enhanced photocatalytic performance for MO degradation due to the formation of a heterojunction between BiOI and CuO semiconductors and their immobilization on clinoptilolite. The combination of 20 wt.% BiOI and 10 wt.% CuO immobilized on clinoptilolite exhibited the highest removal efficiency for MO. This superior performance was attributed to the favorable dispersion and distribution of the active-phase semiconductors on the zeolite support, lower charge carrier recombination and an appropriate bandgap, as confirmed by characterization analyses. Under 2 h of UV light irradiation with a MO concentration of 20 ppm and a photocatalyst dosage of 0.5 g/L, a maximum MO removal efficiency of 85% was achieved under UV light irradiation.

This paper studies the extraction of alumina from kaolin through an acidic leaching process. The kaolin sample used in this study was taken from the Zonuz kaolin mine (Iran Porcelain Industries Company) at particle sizes smaller than 150 microns. Calcination of the kaolin sample was conducted at 700 ºC for 2 hours. Aluminum was leached from calcined kaolin with an H2SO4 acid solution. Temperature and time of leaching, acid concentration, and liquid-to-solid ratio were investigated as leaching parameters. The optimal leaching conditions were considered at 90 °C, 3 hours duration, 2.5 M acid concentration, 7 ml/g liquid-to-solid ratio, and 600 rpm stirring speed, and under these conditions, aluminum leaching recovery was obtained at 94.87%. To remove the iron impurity that is transferred to the solution during leaching, the pregnant leaching solution was purified. Purification was done by solvent extraction with an organic phase containing D2EHPA as an extractant and oil as a diluent, iron removal was achieved at 88.71%. By precipitating the aluminum hydroxide from a solution of aluminum sulfate with sodium hydroxide followed by calcination of the produced aluminum hydroxide at a temperature of 1200 °C for 2 hours, alumina with a purity of 97% was produced. The total recovery of alumina obtained from the studied kaolin under optimal conditions was 89.46%, which is desirable.