International Journal of Mining & Geo-Engineering
Volume:53 Issue: 2, Summer-Autumn 2019

A plausible case of collective and economical mining of diamondiferous kimberlite deposits of Wajrakarur and adjoining places in Andhra Pradesh, southern India along with the whole-rock geochemical evidences in support of their diamond potentiality are discussed in this article. The kimberlites/lamproites are mantle-derived ultrabasic rocks which rarely carry diamonds from mantle to the earth’s surface through carrot-shaped intrusions referred to as pipes. Even though few hundreds of diamondiferous kimberlite pipes were discovered in India so far, there is no other production unit than Panna diamond mine in the country where primary rock is mined. In ancient India, diamond mining in south India in the Krishna river valley was well-known to the world fascinated by famous gemstones like Koh-I-Noor, Hope, Darya-e-Noor, Noor-ul-ain etc. which were mainly extracted from alluvium or colluvium in Krishna river valley. Having bestowed with more than 45 kimberlite pipes, the Wajrakarur kimberlite field (WKF) forms a favourable region for initiating diamond mining in the country. Geochemically, majority of the WKF show low TiO2 content and considerably high diamond grade (DG) values (>3) except some pipes viz., P-5 (Muligiripalli), P-13 (Tummatapalli) and P-16 (Pennahobilam) are barren due to high TiO2 and ilmenite contents. The TiO2 content (0.66-6.62 wt%) is inversely proportional to the DG (3.33 to 22.13). The DG value of some of the WKF pipes is close to that of Panna (8.36). The cationic weight% values clearly portray the diamondiferous nature of these deposits. The WKF pipes were also proved to be diamondiferous by exploratory drilling and bulk sample processing results by the government organisations. In southern India, due to several reasons, diamond mining has not seen its initiation and impetus till now although it records a considerable number of fertile kimberlite pipes at Wajrakarur, Lattavaram, Chigicherla, Timmasamudram etc. Though the majority of WKF diamondiferous kimberlite deposits in Wajrakarur are small in their areal extent (0.06-4.48 Ha) some of them are large (>10 Ha up to 120 ha). They occur in close proximity to each other offering feasibility for collective mining and winning the precious stone through a central processing unit by deploying the latest processing technologies. The geographic conditions of this region such as availability of human resources, water resources, vast open lands, wind power generation etc. also support to initiate mining of kimberlite pipes in this area. The availability of rough diamonds produced from local mines will make the polishing industry to meet its business needs during circumstances of the shortage of rough stone influx from foreign. Hence, although it demands liberal investments, reviving diamond mining in southern India can be materialised with a meticulous evaluation of these deposits ascertaining profitability. This will certainly help to restore the past glory of diamond mining in the southern part of the subcontinent.

In Sangan iron mine nearly two million tons of low-grade iron ore has been extracted and deposited in the mining site and currently no action is made on them. On the other hand, the mining site is located in the semi-arid region and wet processing has been restricted due to water shortage. In this research, the upgradation of Sangan low-grade iron ore from mine B has been performed by dry low-intensity magnetic separation (DLIMS) to solve both problems of unprocessed low-grade iron ores and water scarcity. The X-Ray diffraction analysis showed that the ore minerals of the sample are magnetite and to less extent hematite and the main gangue minerals of the sample are quartz and calcite. The Fe, FeO and sulfur contents of the sample were determined to be 36.86%, 8.1%, and 0.12%, respectively. The scanning electron microscopy equipped with energy dispersive X-ray analysis showed that the full liberation of the iron minerals is achieved in the particle size less than 30 μm. The Davis Tube tests in three different magnetic field intensities of 1420, 2340 and 3800 Gauss confirmed the good amenability of the low-grade iron ore to low-intensity magnetic separation. A concentrate assaying 47.15% Fe with a yield of 68.56% was produced by DLIMS. The process development for the upgradation of Sangan low-grade iron ore by DLIMS was performed and a flowsheet was proposed. The results showed that after two steps of DLIMS it is possible to produce a concentrate with iron grade more than 50% which can be sold as high-grade iron ore or fed to the on-site processing plants.

This paper presents a detailed account of computational fluid dynamics (CFD) simulations undertaken to investigate the influence of intake (ventilation) air velocities on the flow patterns of diesel particulate matter (DPM) generated by a man-riding vehicle operating in a straight rectangular cross section tunnel in an underground coal mine. The simulation results are validated against an earlier experimental study.  At a sampling station 10 m downstream of the vehicle, the DPM concentration was seen to decrease rapidly with increasing intake air velocity. For air velocities of 0.5 m/s, 1 m/s, 2 m/s and 3 m/s, the DPM concentration was estimated to be 233 µg/m3, 131 µg/m3, 116 µg/m3 and 1 µg/m3 respectively. At 10 m downstream of the vehicle, if the intake air velocity is reduced from a base value of 1.26 m/s by 40% and 60% of the base value, the average DPM concentration increased to 58% and 123% respectively. If the intake air velocity is increased by 58% and 98% of the base case value, the average DPM concentration decreased to 44% and 78% respectively.

The classification of mineralized areas into different groups based on mineral grade and prospectivity is a practical problem in the area of optimal risk, time, and cost management of exploration projects. The purpose of this paper was to present a new approach for optimizing the grade classification model of an orebody. That is to say, through hybridizing machine learning with a metaheuristic algorithm called Harmony Search (HS), a proper model for the spatial distribution of the grade classes was obtained, while improving the computational cost of the traditional classification methods. The HS is an algorithm inspired by the simulation of the process where a composer tries to harmonize a piece of music. By interpolating the dataset of Cu and Mo concentrations in the surface rock samples taken from Kooh-Panj deposit district, the grid data of the two elements were extracted. To estimate the true number of groups in the dataset, five popular indices were used in this regard, which in turn determined two classes as the optimal number of groups. Harmony Search Learning (HSL) was used to classify the grid dataset of Cu and Mo. The comparison of the results of the proposed approach with the conventional k-means clustering suggested that the use of HSL method significantly reduced the cost function of the problem (up to 13%). The adaptation of the mineralization class derived from HSL and k-means clustering to borehole locations proved that the results of the HSL were more successful in the accurate estimation of the economic mineralization class identified by the exploratory excavations. In this respect, the HSL technique could significantly improve the k-means performance by 25%. Furthermore, the results of the HSL were more consistent with the lithological units and alteration zones involved in the ore-forming processes. The use of the HS-based learning rectified the disadvantages arising from the typical clustering methods regarding the entrapment in local optimums. It also led to the extraction of weak mineralization signals, numerically laid in boundary conditions. This approach can be extended to more than two geochemical variables and can be a valuable tool for the classification of the mineralized areas to design and optimally manage the mineral exploration projects.

Three Neuro-Fuzzy Inference Systems (ANFIS) including Grid Partitioning (GP), Subtractive Clustering (SCM) and Fuzzy C-means clustering Methods (FCM) have been used to predict the groutability of granular soil samples with cement-based grouts. Laboratory data from related available in litterature was used for the tests. Several parameters were taken into account in the proposed models: water:cement ratio of the grout, relative density of the soil, grouting pressure, soil and grout particle size dimenstions namely D15 soil , D10 soil, d85 grout and d95 grout and percentage of the soil to pass through a 0.6 mm sieve. A accuracy of the ANFIS models was examined by comparing these models with the results of the experimental grout-ability tests. Sensitivity analysis showed that ratios of D15 soil / d85 grout and D10 soil / d95 grout were the most effective parameters on groutability of granular soil samples with cement-based grouts and the grouet water:cement ratio of the grout was determined as the least effective parameter.

In the last decades, helicopter-borne electromagnetic (HEM) method became a focus of interest in the fields of mineral exploration, geological mapping, groundwater resource investigation and environmental monitoring. As a standard approach, researchers use 1-D inversion of the acquired HEM data to recover the conductivity/resistivity-depth models. Since the relation between HEM data and model parameters is strongly nonlinear, in the case of dealing with simple 1-D models which the number of model parameters is less than the number of measured data, i.e. overdetermined system, implementation of regularized nonlinear least square methods is a common approach to recover the model parameters. Among the least square methods, Marquardt-Levenberg acts as an integrated optimization algorithm which comprises both the gradient-descent and Gauss-Newton strategies. This algorithm resolves the deficiencies of the slow convergence of gradient-descent and the singularity of the sparse matrix in the Gauss-Newton. Furthermore, involving the line search strategy improves the objective function to ensure that the algorithm converges to the global optimum point. In this research work, we implemented the Marquardt-Levenberg including the backtracking-Armijo line search for HEM data inverse modeling. Moreover, we used a linear filter of the Fast Hankel Transform (FHT) to figure out the forward operator for data simulation. Developing our algorithm via programming using MATLAB, we successfully obtained a resistivity model of layered earth. We employed the algorithm to recover a resistivity model from the HEM data acquired above the Alut region located at the northwest of Iran where is characterized by shear zone structure consisting of chlorite schist, Phyllite/Phyllonite, metamorphosed limestone and dolomite, mylonite and ultra-mylonite rock units. As a result, in accordance with the geological map the study area, we have successfully derived a resistivity-depth section of the subsurface along the HEM flight line and detected plausible shear zone and mylonitic granite as the favorite targets for the orogenic gold mineralization.

Cement-based materials are fundamental resources used to in construction. The increase in requests for and consumption of cement products, especially in Iran, indicates that more cement plants should be equipped. This study developed a geographical information system using pairwise comparison based on grey numbers to identify potential sites in which to set up cement plants. A group of five experts compared the effective criteria using the data for South Khorasan province. After filtering numerous sites, an area with potential locations for construction of a cement plant has been identified. The selection of a potential area considered the distance to mines, access roads, gas source, and faults. Classification maps were surveyed for land use, pedology, and topography. A potential area was resulted in the north of the province based on the importance weights of 0.307, 0.301, 0.17, 0.087, 0.082, 0.04, and 0.032 for the criteria of land use, proximity to mines, proximity to access roads, proximity to gas substations, topography, pedology, and proximity to faults, respectively.

Estimation of xanthate decomposition percentage has a crucial role in the treatment of xanthate contaminated wastewaters and in the improvement of the flotation process performance. In this research, the modeling of xanthate decomposition percentage has been performed by least squares regression method and Adaptive Neuro-Fuzzy Inference System (ANFIS). A multi-variable regression equation and ANFIS models with various types and numbers of membership functions (MFs) are constructed, trained, and tested for the estimation of xanthate decomposition percentage. The statistical indices such as Root mean squared error (RMSE), Mean absolute percentage error (MAPE), and coefficient of determination (R2) are used to evaluate the performance of various models. The lowest values of RMSE and MAPE and the closest value of R2 to unity were determined for ANFIS model with triangular membership function and number of input MFs 9 9 9 (0.766906, 3.553509 and 0.998793). This indicates that ANFIS is a powerful method in the estimation of xanthate decomposition percentage. The performance of new-adopted ANFIS data modeling was significantly better than the conventional least squares regression method.

In this paper, the ranking of joint roughness coefficients (JRC) profiles as well-known acceptable pattern for studying rough surfaces are investigated. For this purpose, dimension of digitized profiles was measured using fractal-wavelet based methods. Digitization of these profiles and detection of asperities has been done at a distance of 0.02 mm. The fusion of obtained results from various data fusion methods including Clone-proof Schwartz Sequential Dropping (CSSD) and graph theory with approach of scientific phenomenology showing that current trend of roughness profiles needs to be corrected. In fact, some of the exemplar profiles unlike the appearance, have a different roughness than others. This approach changes awareness about roughness as a challenging parameter. Therefore, robust answer was obtained with logical look of data fusion and presenting a new ranking for JRC profiles (JRCN).

In this research, parameters affecting the recovery of titanium dioxide from Iron slag sample collected from Esfahan Steel Company, located in Esfahan province, Iran, were studied. Roasting with sodium carbonate followed by sulfuric acid leaching was used in this study. The best condition for roasting with sodium carbonate was obtained at the temperature of 500oC with Na2CO3/slag ratio of 1/4, and in 120 minutes. The optimum condition for titanium dioxide recovery from iron blast furnace slag was achieved at temperature of 85oC, Solid/Liquid ratio of 1/15, sulfuric acid concentration of 2 M in 240 minutes time, for -106µm sized sample. The maximum recovery was 53%. The kinetic study showed that the leaching process was controlled by the diffusion, in which the activation energy was 5.85kJ/mol which confirmed the diffusion control of the process.

In recent years, scraped tires have become an environmental and economic problem. Reusing waste tires for reinforcing slope can be a suitable solution for the disposal and reduction of the number of scrap tires. In this paper, a series of experimental model tests have been carried out to investigate the behavior of horizontal elements of waste tire (HEWT) in stabilizing sandy slopes. Digital images taken of the side of the model during incremental loading and particle image velocimetry (PIV) were used to investigate the slope under surcharge loading. Some important parameters such as the length, number, and location of the reinforcing tire layers were studied in this paper. There is an obvious plastic zone on the unreinforced and reinforced sandy slope shown using PIV. It shows that scrap type reinforcement highly improved the strength of the sandy slopes model resulting in bearing capacity about 3.5 times higher and settlement about 3 times lower in comparison with the unreinforced sandy slope.

To improve the efficiency of truck-shovel transportation systems and to decrease the relevant operational cost, an appropriate truck dispatching strategy have to be utilized. In this paper, a modified Li model is used to cover heterogeneous fleet size. An extra goal of “minimizing the truck operating cost” was appended to the objective function of the modified Temeng model. The Sungun copper mine transportation system was considered as a case study to evaluate the performance of three dispatching models: extended Li model, Temeng model, and developed Temeng model. In the different routes of mine transportation network, using these models the truck flow rates and the number of truck trips were determined. In order to implement these models, the CPLEX software was utilized. The results indicated that extended Li model, Temeng model, and developed Temeng model improves the total production above 38%, 25%, and 25% in comparison with the current mine production plan, respectively. Each of three dispatching models satisfies various operational constraints such as ore grade quality and stripping ratio.

Static modeling of heterogeneous reservoirs remains as an important challenge in petroleum engineering applications which requires more investigations. Ordinary Kriging (OK), sequential Gaussian simulation (SGS) or multilayer perceptron neural network (MLP) are the common methods which are utilized in modeling different type of reservoirs. However, it is well known that these methods are inapplicable for heterogeneous reservoirs. In this paper, wavelet neural network (WNN) is introduced for modeling heterogeneous reservoirs. In order to investigate the applicability of WNN, two exemplar heterogeneous reservoirs were generated. The first model, represents a heterogeneous reservoir being divided into three homogeneous subzones. The second model simulates a heterogeneous reservoir composed of randomly distributed data with wide range of variability. The applicability of methods for porosity modeling in a heterogeneous carbonated reservoir in south-west of Iran has also investigated. The OK, MLP and WNN methods were applied to model both synthetic reservoirs. The results showed that in the second model, all three methods presented biased solutions. However, in the case of first model, the MLP resulted in biased solution, whereas the OK and WNN models presented unbiased and acceptable solutions. The results also showed that the WNN was more accurate with lower range of error in comparison to the OK. In addition, it was noted that the CPU time of the WNN was approximately 15% of the CPU time of the OK, and 5% of the CPU time of the MLP. In the case of the real reservoir, all three methods resulted in unbiased solutions, because heterogeneity was less than both synthetic data. By the way, the error for WNN was less than OK and MLP, meanwhile, WNN resulted in a lower range of error in comparison to other methods. However, similar to synthetic data, the CPU time of WNN was approximately 20% of the CPU time of OK, and 7% of CPU time of the MLP. Considering the complexity associated with up-scaling in heterogeneous reservoirs and the difficulty of history matching in large blocks which introduces large uncertainty, the results of this study suggests that the WNN, with faster running time, can handle more blocks (finer grids) and offer advantages in modelling heterogeneous reservoirs.