فصلنامه مهندسی معدن
پیاپی 55 (تابستان 1401)

Solvent extraction (SX) of rubidium (Rb) from leach liquor of central Iran metasomatic ores was studied with various macrocyclic crown ether ligands. Many crown ethers were used as extractants. Rubidium was quantitatively extracted from picrate media by dicyclobexano-18-crown-6 (DC18-C-6) in chloroform, and determined by flame emission spectrometry. Here, the factors affecting SX of Rb from synthetic solutions of rubidium (containing 100 mg L-1 Rb) in sulfuric acid medium were identified. 0.01 mol L-1 DC18C6 in chloroform, 0.01 mol L-1 picric acid in aqueous phase, aqueous to organic (A/O) ratio of 1, pH 5.5 and mixing time of 15 min are the optimum values for affective factors. The precipitation with sodium hydroxide was conducted to remove the leaching impurities such as Fe, Mg, Al. Complete extraction of Rb from aqueous solution and almost complete extraction of Rb (81%) from leaching filtrate containing 0.01 mol L-1 picric acid was performed using 0.01 mol L-1 DC18C6 in chloroform. The Rb complete strippings, was obtained with 4 mol L-1 and 5 mol L-1 hydrochloric and nitric acids, respectively, ‌with A/O ratio of 1:1.

This research aims at grade modeling, reserve classification, and assessing risk in grade-tonnage curves in a case study pertaining to Iron mineralization. The Darreh-Ziarat Fe deposit, located in west of Iran, is selected for this research work. Statistical studies were performed on the results of the analysis of ten boreholes. The ordinary kriging (OK) method is run to design a 3D model of grade by incorporating the drilling results. The indicator kriging (IK) method was used to remove waste blocks from the ore block model. Reserve classification was carried out in two ways. A grade-tonnage curve was displayed for different cut-off grades in accordance with the estimation results. In the second section, all the previous steps were performed by the simulation method. For this purpose, the sequential indicator simulation (SIS) method was utilized to remove waste blocks. A grade estimation block model was also generated using sequential Gaussian simulation (SGS). One hundred iterations of the SGS were run to investigate the uncertainty of the grade estimation, create models of the probability of exceeding the specified cut-off grades, and generate an E-type model. The kriging grade-tonnage curve showed that with respect to the cut-off grade of 20%, the tonnage and the average grade of iron are equal to 2.9 million tons and 40.79%, respectively. Also, the E-type grade-tonnage curve showed that with respect to the cut-off grade of 20%, the tonnage and the average grade of iron are equal to 3.1 million tons and 40.92%, respectively. Finally, Iron mineralization targets were recognized by comparing the kriging and the E-type methods.

Due to the widespread use of phosphate, there is a need for extensive studies and experiments in relation to its processing. In this research, flotation method was used to remove carbonate gang with the help of a collector made of G (j-mrap1). The grade of sedimentary ore of Charam phosphate mine used in this research is 13% P2O5 and it is considered as a low grade reserves and also its sub-minerals are 46% of calcite and 7% of silica. To achieve a commercial grade of 28% P2O5, a new collector with a hydroxamic acid operating group has been synthesized and used. By optimization by experimental design software (DX7), at pH equal to 6.2 and collector 1000 g / ton, the maximum recovery of 84.97% P2O5 with a grade of 27.71% P2O5 was achieved. By changing the pH to 6.5 and the amount of collector to 1200 g / ton, the concentrate with the highest grade was 29.47% P2O5 and 82.87% P2O5 was recovered. The use of plant-based synthesized collectors indicated that this type of collector could be a suitable alternative to chemical-based collectors.

The Remaining Useful Life (RUL) valuation of mining machinery is a principal to ensure the production/output and customer satisfaction in the mining zone. In many cases, it may be of attention to know the expected value of the remaining life of the item before it fails from an arbitrary time that known RUL. The system's failure is also evaluated with the reliability index, which describes up-times. An individual unit's reliability during field use is essential in many mining equipment applications. This index, especially in industrial systems, and being affected by the internal condition also affects operating environmental conditions. For example, the loader performance in cold weather will be different from that of warm, which will affect the machine's reliability and thus the RUL. In reliability engineering, operating environmental conditions are considered "Risk factors or Covariates". Therefore, in this paper, an approach is proposed first to analyze the system's reliability considering covariates' effect and then estimate the RUL for different scenarios. The proportional hazard model was used in reliability analysis to be realistic and take the operational influencing factors in calculation. Methods are presented for calculating the reliability function and computing the RUL as a function of the current conditions to guarantee the desired output. The remaining useful life estimation of a Komatsu PC-1250 from the Sungun copper mine was evaluated as a case study of this approach. Systems operating environmental factors such as shift, dump-truck kind, rock kind, … (known as covariates) are assumed to influence covariate in this context. As a result, the Weibull proportional hazard model was fitted to describe the failure behavior, and the RUL of four selected scenarios was evaluated. Presented results can be used, e.g., for developing operational performance, planning of maintenance activities, spare parts provision, and the profitability of the owner of an asset.

Lithium is a very strategic metal and the source of energy for the world in the future. Due to the lack of lithium mineral resources, in this study, the extraction of lithium from the bittern of salt extraction ponds from seawater was evaluated. In order to selectively adsorb lithium from the bittern solution, magnetite nanoparticles were first synthesized by co-precipitation in the presence of ammonium hydroxide. Due to the instability and the tendency of these nanoparticles to agglomerate in solution, their surface was coated with silicate surfactant. Silica surface coating on magnetite nanoparticles was performed using tetraethyl orthosilicate precursor with a concentration of 1 ml by sol-gel process. Magnetite nanoparticles stabilized by organosilane aminopropyl thromotoxy silane (C6H17NO3Si) were functionalized and used for adsorption experiments. At a stirrer speed of 30 rpm, a duration of 40 min and a solid to liquid ratio of 9 g / l, about 97.1% lithium was extracted from the bittern solution. Under these conditions, the extraction of K and Mg elements was negligible and 4 and 6%, respectively, but the extraction of sodium was equal to 34%. Washing and separation of lithium and sodium from the surface of nanoparticles was performed by the ammonium compound NH4OH and NH4Cl, which washed 99% of lithium in 150 min, but only about 55% of sodium was washed at the same time.

Roughness is one of the geometrical properties of rock joints that can be expressed through various methods. In this paper, eight different parameters were used to estimate the joint roughness coefficient (JRC) of 112 joint roughness profiles. The range of variation of these parameters in a given roughness class is relatively large. These roughness values overlap with their adjacent classes. In order to use two parameters simultaneously to estimate the JRC matrix, the interaction of these parameters on the JRC value were evaluated. The resolution of different roughness classes in different scenarios was evaluated using Pearson correlation coefficient and using engineering judgment. So in this paper, a new method based on the classification of joint roughness coefficient (JRC) by support vector machine (SVM) is purposed. So in this paper, a new method based on the classification of joint roughness coefficient (JRC) by support vector machine (SVM) is purposed. Different joint roughness parameters including Z2, RP, Grasselli2D, standard deviation of asperities height (SDH), standard deviation of profiles height variation (SDPHV), standard deviation of asperities angle (SDA), and geostatistical parameters including range (a), sill (C), CA and SRv were evaluated for 112 joint roughness profiles. Using these 8 parameters, an 8 by 8 interaction matrix was created which consequently resulted in 28 individual two-dimensional JRC classification scenarios. A graph with SDH and SDA was selected for the Statistical classification of JRC (SCJRC) because of the relatively obvious boundary between JRC classes and easy calculation. Finally, data classification was performed by SVM. The estimation of SCJRC was checked by 20 experimental direct shear test data. A good agreement is observed between SCJRC and experimental results. The results illustrate that SCJRC is an appropriate method for the estimation of JRC.

Identification of geochemical anomaly areas is a major aim in analyzing the stream sedimentary geochemical data. In this paper, a novel integrated method based on the principal component analysis (PCA) and U- statistical method has been used for Au mineral potential mapping in the Saghez area. Firstly, the PCA as a multivariate dimension reduction method has been applied for extracting the multivariate geochemical signatures and detecting the paragenesis elements in the Saqqez sheared gold mineralization area. The mineralization principal factor was determined based on the rotated component matrix and the paragenesis elements of Sn, W, As and Sb relevant to the ore-forming process were identified. The obtained mineralization principal factor calculated based PCA method has been modeled for increasing the exploration success and enhancing the geochemical halos. For mapping the multi-element mineralization targets, the U statistical method was applied on the mineralization principal factor and the geochemical populations were obtained and delineated. This scenario as a geochemical population modeling method properly could mapped the sheer Au anomaly areas and 5 mineralization zones amongst the 6 Au occurrences were identified. The conventional U spatial method has also been utilized on the Au element and the obtained geochemical sub-populations were interpreted and the geochemical anomalies were mapped. Finally, the U-values modeling of mineralization principal factor was compared to the results of U spatial statistical method of Au elements. The novel proposed approach enhanced the exploratory prediction rate and displayed more proper results for mineral potential mapping.