نشریه مهندسی منابع معدنی
سال نهم شماره 3 (پاییز 1403)

This research aims to simultaneously use geochemical modeling and geological parameters for gold grade estimation to identify promising zones of epithermal gold mineralization in the Zailik region, northwest of Iran. For this purpose, the employed geological evidence includes lithology and alterations like silicification, iron oxides, phyllic, and propylitic. For  geochemical modeling two methods were utulized: 1) artificial neural network (ANN),  2) integrating ANN with the Firefly algorithm. Geological evidence after quantification, along with the estimated amounts of gold in artificial intelligence methods, was entered into the hierarchical system in Expert Choice software for weighting. In this method, the weighting and determination of the degree of relative importance of geological parameters were attempted after consulting geological and exploration experts. Subsequently, artificial intelligence methods were also compared with each other using quantitative criteria such as the coefficient of determination and the root mean square error function. The results showed that the combined method of artificial neural networks with the Firefly algorithm provides better results due to the higher coefficient of determination (R2=0.643) and lower error function (RMSE=0.754). Therefore, it has a higher degree of importance to identify promising areas for mineralization. Finally, all the above parameters were combined with each other in the Arc GIS software using the fuzzy overlay method, and the optimal exploration targets were detected in the north and northeast of the region, enabling to continue the exploration targets along the root of gold mineralization in the neighboring areas according to the introduced model.

Ardestan area is located in parts of Central Iran and Urumieh-Dokhtar zones, and due to its special geological features, is one of the most promising areas for Pb-Zn mineralization in Iran. The purpose of this research is to model the mineral potential using the hybrid AHP-TOPSIS method, to increase the accuracy of the results and thus to reduce the cost and the risk of exploration operations. For this, in the first step, the evidential layers, which include the geochemical anomaly map, the phyllic, argillic, and iron oxide alterations, the fault density map, and finally carbonate rock units, were transformed in the range of 0 to 1 using the Logistic function. In the next step, all the evidential maps and their corresponding classes weighted using the Analytical Hierarchy (AHP) method and incorporated with the technique of prioritization similar to the ideal solution (TOPSIS). The results show the positive spatial correlation of Pb-Zn elements with lithologies prone to mineralization. Finally, the Prediction-Area (P-A) curve was used to validate the applied method. According to this curve, the intersection point of the prediction rate curves and the corresponding occupied area showed the value of 68. The results of this curve proved the acceptable ability of this method in identifying promising areas.

Mineral exploration necessitates a comprehensive approach that involves analyzing various geophysical, geological, and geochemical datasets, in addition to employing efficient and effective methodologies. Successfully addressing this challenge involves integrating and analyzing diverse geographic data, which often come in different formats and possess distinct features, with the aid of innovative applications. One promising technique involves utilizing artificial intelligence to convert low-resolution drone-collected data into high-resolution ground data. For this particular investigation, three supervised regression models—linear regression, random forest, and enhanced gradient—were implemented in the Python programming environment using magnetometric data obtained from both UAV and Proton ground devices. After evaluating the statistical results, including metrics such as mean square error and mean absolute error, it was determined that the enhanced gradient model outperformed the others. This model exhibited respective values of 0.0004 and 0.01 for training data, 0.001 and 0.02 for experimental data, and 0.001 and 0.01 for validation data. Additionally, the enhanced gradient model demonstrated stability, leading to its selection as the preferred model for prediction purposes.

This study presents a tool for evaluating sustainable development in the mining industry. We identified indicators related to four dimensions: growth and learning, economy, environment, and society. A total of 75 primary indicators were determined. Indicators with a mean weight of over 3.6 were considered suitable, resulting in the selection of 28 final indicators. The top-ranked indicator was "number of lost days" with a mean weight of 10.9, followed by "total amount of waste produced" with a mean weight of 7.73. The indicators of "waste management" and "efforts to restore the mine" had mean weights of 5.9, ranking third. To assess the weight of each dimension in evaluating sustainability performance, the researchers used the FANP method and a pairwise comparison questionnaire. The inconsistency rate of the pairwise comparisons, estimated using the Gogos and Butcher's method, was below 0.1 for all questionnaires, indicating consistency. The results showed that the "growth and learning" dimension had the highest mean weight of 0.48, followed by the "community" dimension with a mean weight of 0.24. The "environment" dimension ranked third with a mean weight of 0.22, while the "economy" dimension ranked fourth with a mean weight of 0.16 among all dimensions.

The occurrence of settlement induced by tunneling operations in urban environments is an inevitable phenomenon. The risk of damage to nearby infrastructures and surface structures can be greatly reduced by predicting and controlling this event. This paper uses multiple linear regression (MLR) model and random forest (RF) algorithm to predict the maximum surface settlement (Smax) due to shallow tunnel excavation. Nine input parameters, including the distance of the tunnel center from the ground surface (H), height of the underground water level above the tunnel (W.T), tunnel diameter (D), elastic modulus of soil (E), undrained shear strength of soil (Cu), earth pressure coefficient (K0), unit weight of soil (γ), gap parameter (g), and stability number (N) were selected from 24 data sets related to 14 tunneling projects. The MLR and RF techniques were then implemented for predicting Smax. Three performance indicators of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) were employed for the training and test phases to evaluate the efficiency of the suggested models. The coefficient of determination values for MLR and RF for training data were 0.814 and 0.957, respectively, while for test data were 0.793 and 0.96, indicating that the RF approach is more efficient than MLR. Moreover, the findings reveal that the RF algorithm exhibits lower RMSE and MAE values in both the training and testing phases compared to the MLR method. This suggests that the RF algorithm exhibits reduced error and higher reliability and accuracy when compared to the MLR model. Also, the performance study demonstrates that among the input parameters, the gap parameter (g) and the undrained shear strength of soil (Cu) have the greatest and least influence on Smax, respectively.

In the iron ore mines of Gol-Gohar, talc presents along with magnetite mineral, which goes to waste thickeners after recovery of magnetite. Considering that talc constitutes more than 20% of waste, it can be sold as a by-product with a high added value, if it is recovered and purified. In this study, the extraction of talc from the tailings of the Gol-Gohar complex has been investigated. Sampling was done from the central thickener underflow, which contained 65% of dust factory tailings and 35% of concentrate factory tailings. Based on characteristic studies, the tailings from the central thickener had about 23% talc. Flotation tests were performed using different chemicals, such as pine oil and polypropylene glycol as frothers, oleic acid as collector, Armac C and Armac T, and pH 7 and 11. In the best condition, pH=11, polypropylene glycol as a frother, and not using a collector, a talc concentrate with a purity of more than 95%, a weight recovery of 10.4%, and a talc recovery of 42% was produced. Also, flotation kinetic tests were performed under optimal conditions, and it was shown that most of the first-order kinetic models and the second-order kinetic model excellently match the talc flotation recovery over the time.