نشریه مهندسی منابع معدنی
سال هشتم شماره 4 (زمستان 1402)

In this study, a hybrid approach is established for clustering the most favorable regions  in association with magnetite-apatite mineralization at the Esfordi district in the central Iran. An optimum number of clusters is derived from a data-driven methodology through a concentration-area (C-A) fractal model of a synthesized geospatial data set. According to the metallogenic characteristics of the sought deposits, nine evidential layers deriving from geological, geochemical, geophysical, and remote sensing data were extracted. Prediction-area curve (P-A) was used as a data-driven method to determine the weight and importance of those evidences; then an index overlay method integrated them into a single propsectivity map. The number of clusters significantly affects the mineral potential modeling results in clustering algorithms. To determine an optimum number of clusters, the C-A fractal curve of the overlaid map indicated the correct population within this district, and then used as the optimal number to run the unsupervised clustering algorithms. Assuming five clusters, three clustering algorithms, including K-means, fuzzy C-means, and self-organizing map (SOM), were used to identify and localize iron-bearing favourable areas. The K-means algorithm had the highest accuracy in identifying those potential areas, by which 8% of the whole area could predict 65% of known deposits in the main favorable region.

In exploratory projects, the identification of geochemical anomalies in different areas may become complicated under the influence of geological processes. To solve these ambiguities, different methods should be used for a correct understanding of the available information. In this research, by expressing the concept of hierarchical clustering to identify elements related to mineralization, singularity, and how to draw singularity maps in the form of multifractal models and support vector machine method, the anomalous areas where there is a possibility of mineralization are seprated from the context regions. At first, two elements, gold and copper, were identified as elements related to mineralization in the created clusters using the hierarchical clustering method and Ward's method. To calculate the singularity index of these two elements, the method based on the window and the power relation of grade area was used at each point. Finally, by separating the singularity index values into two parts, training and testing, and with the help of the SVM method, the process of classification and estimation of singularity index values was done to identify anomalous areas for unknown areas. A case study has been carried out on the data of the porphyry copper deposit rich in Dali gold with an area of 900×800 meters located in the Urmia-Dokhtar magmatic belt. The data is related to surface soil samples in the target area. The results of this method are consistent with the previous studies conducted in the region. The results of the hybrid method used in this research show good agreement with previous studies. As a result, the use of these introduced hybrid methods can be a suitable guide for the production of geochemical maps in unknown areas.

Recently, water injection has been widely studied as one of the most popular enhanced/improved oil recovery methods. Most of the studies focus on low salinity and seawater injection, emphasizing the quality of the injection fluid. In addition, receiving the required quantity of injection water and high-quality injection fluid is crucial. Produced water with oil and urban wastewater can be regarded as appropriate resources for water injection projects to mitigate the environmental concerns and the cost of water treatment. This study examines several water resources suggested for injection into an oil reservoir near the Persian Gulf to find the optimal choice. The studied resources are low-salinity water, seawater (from the Persian Gulf), produced water, and urban wastewater. Injection fluid quality, compatibility with formation brine, scale formation, and usability were the primary factors considered when selecting the optimal water resource. Accordingly, geo-chemical software OLI Scale Chem was used for compatibility check and estimation of scale type and amount. As a result, the least amount of scale formed after urban wastewater injection, whereas seawater was the most incompatible water source. It should be emphasized that various non-technical factors, such as the climate of the region, the water transmission route and associated costs, economic conditions of the country, environmental concerns, and the priority of wastewater usage may significantly impact resource selection.

Loading and haulage operation in open pit mines is the last stage of the mining process. truck- shovel system, due to its many advantages  including high flexibility, is preferred for this operation. Due to high operating costs, proper fleet management and optimization can significantly affect the project economics. Truck allocation and dispatching issue is a very complex problem, especially in large mines with numerous loading and dumping points. Because of the problem size and complexity, employing mathematical methods is not justified due to very high solution time which leads to employing super computers. To overcome the aforesaid shortcoming, heuristic algorithms can be applied. In this paper, in MATLAB environment, a heuristic algorithm was developed to solve allocation and dispatching problem of transportation fleet of a real mine. According to the obtained results, a running time of 39 seconds was computed for the heuristic algorithm. Finally, the same problem was solved with an available mathematical model with a running time of 24 hours which shows the superiority of the proposed algorithm over the mathematical modeling.

To design an open pit mine, geological operations must be conducted, followed by the preparation of a three-dimensional model and mineral block model. The ultimate pit limit can be determined through accurate methods and artificial intelligence techniques. The problem of determining the ultimate pit limit is considered to be NP-hard, making it challenging to solve. While exact methods provide better and optimal results, they may require significant time to answer the problem due to the large number of blocks involved. In such cases, it is more suitable to use collective algorithms or a planned approach to determine the final range. Optimizing the determination of the ultimate pit limit is similar to other optimization problems that can be addressed using logical algorithms in MATLAB software. In this study, Keshtel algorithm, implemented in MATLAB, is utilized to optimize the final range. Initially, Keshtel algorithm is employed to solve the problem. Subsequently, the Songun copper mine is chosen as a case study for the two-dimensional and three-dimensional implementation, and the results of determining the ultimate pit limit are compared with both Keshtel algorithm and NPV Scheduler software. The findings reveal that Keshtel algorithm, used to determine the final limits of the Songun copper mine, differs by only 0.47% compared to the NPV Scheduler software. Moreover, the comparison of Keshtel algorithm with the results of Lerch Grossman in determining the two-dimensional final range, as well as the comparison with NPV Scheduler software in three-dimensional problems, demonstrates its efficiency in solving these issues effectively.

In recent decades, the application of microorganisms and their products for the bioseparation of minerals, bioflotation, and bioflocculation, has been extensively recognized by researchers and industry. Considering several benefits of the bioseparation, in the current paper, a detailed review has been conducted on the bioseparation of galena from its most common accompanying minerals i.e. sphalerite, chalcopyrite, and pyrite using microorganisms and their extracellular products. Based on the findings, the bacterial cells of the Thiobacillus species have a good ability to depress and selectively flocculate galena, but the cells of the Polymyxa species have a lower ability. Therefore, they depress and flocculate most of the sulfide minerals present in the pulp. In addition, the adaptation of bacteria, especially polymyxa  species with galena and other minerals will increase extracellular secretions of protein or polysaccharides. Adapted Bacillus subtilis and Bacillus megatrium can separate galena. Due to the hydrophobic nature of extracellular proteins, their less absorption on the surface of galena compared to sphalerite, causes the second mineral to be floated and the galena to be depressed. On the other hand, adaptation leads to more protein secretion in the presence of galena compared to pyrite, which will cause galena to float and the second mineral to be depressed. Also, it can be said that the tendency of extracellular polysaccharides to adsorb on galena and the tendency of extracellular proteins to adsorb on sphalerite causes that when the mixture of these two minerals comes into contact with bacterial EPS, galena is usually depressed or flocculated and sphalerite floats to some extent.

The reprocessing tailings of iron ore processing plants is important from economic (as a secondary source of iron) and environmental points of view. The aim of the present study is to determine the appropriate method for recovery iron from the tailings of Balestan iron ore processing plant (located in the northwest of Iran). For this purpose, after taking samples from the tail piles of this plant, a representative sample was selected, and chemical, mineralogical, size distribution, and bond’s work index analyses were performed on it. The total iron grade in the tailings was measured to be about 10%, of which 8.5% was identified as magnetite mineral (with a degree of liberation less than 40%). The d80 value of the sample was 7 mm, and the bond index was measured as 11.84 (kWh/ton). To determine the appropriate method for iron recovery, pre-processing with magnetic drum, grinding, and Davis tube tests were performed. With the magnetic pre-processing of tailings with a particle size of 0-10 mm, under a field of 2000 G and in a dry method, about 80% of the load entering the drum with a total iron grade of 5% was transferred to the tailings. 20% of the input load with an iron grade of approximately 24% was recovered to the concentrate. In order to remove the gangue minerals and enrich the pre-processed concentrate, stepwise grinding and separation had the best results. Comminution was done in two stages to produce products with smaller dimensions of 250 and 45 microns, respectively. Magnetic separation was also done in three stages with 3000 G, and two stages of separation with 1000 G, which finally resulted in a concentrate with a total iron grade of more than 66% and iron recovery of 44.88%.