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By J. N. Van der Merwe

The data base of failed coal pillars in South Africa was analysed with regard to the time of failure in an effort to develop a method to predict how long after their formation coal pillars will fail. It was found that meaningful results could only be obtained by splitting the data base up into groups that displayed similar behaviour. It was postulated, based on observation and experience, that coal pillars fail by a process of progressive weakening progressing into the pillar from the edges. Minimum pillar dimensions to ensure failure were calculated?these were the dimensions that corresponded to a minimum ?real? factor of safety. From the scaling distance required to reach the minimum safety factor, rates of scaling for the failed pillars were calculated based on the observed real lives of the pillars. It was found that the rates of scaling were unique for different mining areas and that they correlated directly with mining height and inversely with time. Using the derived scaling rates, the predicted pillar lives were back calculated for the pillars in the data base of failed pillars. In two of the three groups, the predicted lives correlated well with the observed lives on a one-to-one comparison. In all cases, the predicted lives were in the same time range as the observed lives. Overall, the frequency of predicted lives closely replicated the observed frequency of observed lives of the pillars in the data base of failed pillars. The method was successful in distinguishing between the data base of failed and a representative sample of intact pillars. While the predictions matched the observed lives of failed pillars, the predictions of expected lives for intact (hitherto unfailed) pillars were orders of magnitude greater. It is now possible to estimate the frequencies of future pillar failures for the purposes of mine closure and undermining of surface structures within broad bands.

Jan 1, 2003

By K. Woodward, Y. Potvin, B. McFadyen, M. Grenon

Open stoping has become a popular mining method in hard rock mines, not only due to the safety of the method as a non-entry approach, but also because of the high extraction rate and low costs. At mine sites, stope performance is evaluated by calculating stope overbreak using the stability chart. However, limitations of the stability chart regarding the precision of the predictions, nonconsideration of factors such as the influence of blasting, and the exclusion of underbreak have led to non-optimal designs. The capabilities of today's computers have increased the amount of data being collected and the power of models being built. This article presents a step towards a new stope design approach where stope overbreak and underbreak are measured and georeferenced using octrees at an approximately cubic metre resolution and predicted using multivariate statistical models (partial least square, linear discriminant analysis, and random forest). Results show that overbreak and underbreak location along the design surface and their magnitude are predicted with good precision using a random forest model. These predictions are used to build the expected geometry of the open stope. The resolution of the data and the use of multivariate analysis has enabled the prediction of variation in stope performance along the design surface, going well beyond the simple qualitative per stope face prediction provided by a traditional stability chart approach.

Jun 5, 2023

By H. Schunnesson, H. Fredriksson, D. Johansson, E. Söderström, S. Manzoor, A. Gustafson, M. Danielsson

Fragmentation analysis is an essential part of the optimization process in any mining operation. The costs of loading, hauling, and crushing the rock are strongly influenced by the size distribution of the blasted rock. Several direct and indirect methods are used to analyse or predict fragmentation, but none is entirely applicable to fragmentation assessment in sublevel caving mines, mainly because of the limitations imposed by the underground environment and the lack of all the required data to adequately describe the rock mass. Over the past few years, measurement while drilling (MWD) data has emerged as a potential tool to provide more information about the in-situ rock mass. This research investigated if MWD can be used to predict rock fragmentation in sublevel caving. The MWD data obtained from a sublevel caving mine in northern Sweden were used to find the relationship between rock fragmentation and the nature of the rock mass. The loading operation of the mine was filmed for more than 12 months to capture images of loaded load-haul-dump (LHD) buckets. The blasted material in those buckets was classified into four categories based on the median particle size (X50). The results showed a stronger correlation for fine and medium fragmented material with rock type (MWD data) than coarser material. The paper presents a model for prediction of fragmentation, which concludes that it is possible to use MWD data for fragmentation prediction.

Mar 2, 2022

By L. Ntlhakana, K. Khoza-Shangase, G. Nelson, I. Maposa

Occupational noise-induced hearing loss (ONIHL) is associated with various risk factors in South African mines. We investigated the association between standard threshold shifts (STSs) and exposure to noise and platinum mine dust (PMD), using demographic data, five years of annual audiometry screening results, and noise and dust exposure data. Miners’ age, sex, percentage hearing loss (PLH), and dust and noise exposure data were gathered, and a linear mixed effects regression model used to predict STS. Average occupational exposure levels to noise and dust were calculated from recorded measurements. A total of 12 692 records were analysed. Most miners were male (89.6%) and more than 50% were younger than 41 years. More than 70% were exposed to > 85 dBA noise and 58% were exposed to 1.5–2.99 mg/m3 PMD. Changes in hearing levels ranged from 8.3 dBHL at baseline (2014/2015) to 10 dBHL in 2016 (STS), with no changes thereafter. The model showed that the estimated effect on STS for males was 27% and 21% higher than for females, for the left and right ear, respectively The estimated effect of age, PLH, noise exposure and years of exposure on STS was < 10% for each variable. There was no statistically significant association between PMD and STS. The combined effects of age, sex, years of exposure to noise, and noise exposure levels, and strength of associations can be used to predict STS for this group of miners. Our findings may be used to measure the efficacy of the mine’s hearing conservation programme.

Aug 1, 2021

By S. Kahraman

The prediction of rock properties from indirect testing methods is important, particularly for preliminary investigations since indirect tests are easier and cheaper than the direct tests. In this study, we investigate the predictability of the uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) of rocks from the indentation hardness index (IHI) obtained using point load apparatus. Forty-six different rock types, 14 of which were igneous, 15 were metamorphic, and 17 were sedimentary were tested in the laboratory. The UCS and BTS values were correlated with the corresponding IHI values and the results were statistically analysed. The influence of rock classes on the relationships was also investigated. A strong correlation between UCS and IHI was found for all data. The correlation between BTS and IHI is not as strong as the correlation between UCS and IHI. However, it is in the acceptable limits. When the regression analyses were repeated for igneous, metamorphic, and sedimentary rocks, the correlation coefficients were generally increased. The results show the UCS and BTS can be estimated from IHI. In addition, the effect of rock classes on the relationships between IHI and both UCS and BTS is important.

Jan 1, 2012

By S. Kahraman

The prediction of rock properties from indirect testing methods is important, particularly for preliminary investigations since indirect tests are easier and cheaper than the direct tests. In this study, we investigate the predictability of the uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) of rocks from the indentation hardness index (IHI) obtained using point load apparatus. Forty-six different rock types, 14 of which were igneous, 15 were metamorphic, and 17 were sedimentary were tested in the laboratory. The UCS and BTS values were correlated with the corresponding IHI values and the results were statistically analysed. The influence of rock classes on the relationships was also investigated. A strong correlation between UCS and IHI was found for all data. The correlation between BTS and IHI is not as strong as the correlation between UCS and IHI, However, it is in the acceptable limits. When the regression analyses were repeated for igneous, metamorphic, and sedimentary rocks, the correlation coefficients were generally increased. The results show the UCS and BTS can be estimated from IHI, In addition, the effect of rock classes on the relationships between IHI and both UCS and BTS is important.

Jan 1, 2012

By D. Hoffmann, S. Plumb

IRUP is an iron-rich ultramafic pegmatite rock that formed due to hot ironrich fluids and gases replacing local stratigraphic zones of the Bushveld Complex. This study focuses on the estimation of the probability of IRUP occurrence on the Merensky Reef at the Marikana Karee Mine. A 2.15 million centare IRUP-rich domain at the Karee Mine was initially defined from the interpretation of a surface aeromagnetic anomaly, and exposures in the mine workings. Surface boreholes (spaced 250-500 m apart) within this IRUP domain contain approximately equal numbers of IRUP-replaced and IRUP-free intersections. Owing to the uncertainty in the continuity of the IRUP alteration (a result of the wide borehole spacing), the risk associated with the development of mining infrastructure within this domain is unquantifiable. Semi-quantitative data comprising visual estimates of IRUP replacement from reef development mapping data and surface borehole reef intersections was interpolated into blocks using indicator kriging estimation. Comparative analyses of the estimate of IRUP occurrences were made by changing the block size and declustering the data systematically. Reconciliations of the probability of IRUP predicted from the block models derived from the different data-sets and sequential indicator simulation models were analysed for four mining study blocks. A quantitative approach to modelling the occurrence of IRUP can be an additional tool for refining the estimate of the geological losses that inform the mine plan in such high-risk zones.

Jan 1, 2015

By A. Ward, A. Smith, D. Harpley

Methods for the prediction of seepage quality from waste-disposal facilities are evaluated for input to the design of desalination treatment facilities. This is done by reference to a waste dump seeping into a near-surface aquifer, the contaminated seepage being collected by scavenger wells. As the quality of contaminated water intercepted by the wells will vary with both space and time, it is necessary for predictive methods to accommodate such variations. One-dimensional and two-dimensional predictive solute transport models are described and applied to the seepage and scavenger-well example mentioned above. The advantages and disadvantages, interims of overall applicability, data requirements, and possible accuracy, are described and discussed. While one-dimensional models have the advantages of speed, simplicity, and cost, the frequent requirement of prediction in both space and time necessitates more complex, two-dimensional simulation.

Jan 1, 1987

Instability in rockpasses at deep levels often initiates as a result of stress induced fracturing of the rock on the walls of the pass. This may then progress to a stage at which the geological structure of the rock mass becomes dominant and permits large joint-defined blocks and slabs to become unstable. In this paper, a technique is described in which jointing parameters and their variabilities?orientations, spacings and lengths?are taken into account in estimating the extent of failure that may develop around a typical unsupported deep level gold mine rockpass. The results of this prediction are compared with the results obtained from a review of pass performance in the gold mining industry.

Jan 1, 2005

By I. Ince, S. Kahraman, M. Rostami, B. Dibavar

The density, porosity, and strength of rocks are fundamental in the design of geo-engineering projects. Determining these properties requires the preparation of smooth core samples, which is usually impossible for soft rocks. Besides, only fragmented samples may be available in some projects. A method for predicting rock properties from rock particles such as drilling debris would therefore be useful. This study was undertaken to develop prediction equations for compressive strength (UCS), tensile strength (BTS), density, and porosity values from the crushability index (CI) obtained from rock fragments. The results showed that UCS, BTS, density, and porosity were strongly correlated with the CI. The physical characteristics and strength of rocks may be predicted with the help of the derived equations. The CI test is a valuable tool for estimating rock properties in drilling projects and in situations where core specimens are not available.

Mar 14, 2024

By I. C. R. Gilchrist

A principal requirement in the design of a backfill transportation pipeline is the accurate prediction of the values of key slurry flow parameters. Input variables, such as particle size and solids concentration, are usually determined by the load-bearing requirements of the placed fill. It is therefore necessary to identify the optimum conditions of transport for a particular combination of input variables. This paper critically reviews the methods available to predict the values of flow parameters in slurry pipeline systems in the light of experimental data generated at a test facility. Particular reference is made to rod milled waste and deslimed tailings transport systems and, in the case of the former, it is shown that both pumping and load-bearing requirements can be optimized.

Jan 1, 1988

By I. O. Ondicho, E. K. Mutinda, B. O. Alunda, E. Agyekum

Driven by the necessity to improve blast performance regarding fragment size distribution in limestone mines, this paper introduces the prediction and measurement of blast fragmentation distribution through the modified Kuz-Ram (MKR) and Kuznetsov-Cunningham-Ouchterlony (KCO) models. Precise prediction and measurement of rock fragmentation resulting from blasting play a critical role in hard rock ore extraction. This is due to the substantial influence of post-blast ore size distribution on the efficiency of all subsequent downstream rock handling and processing operations. Additionally, employing secondary breakage of boulders, when necessary, leads to an unjustified rise in production costs. In this paper, the MKR and KCO models were utilised to identify the most appropriate model for application in Bisil and Simba quarries situated in Kajiado County, Kenya. Split-Desktop was used for blast fragment measurement by processing digital images obtained after blasting and computing the fragment size distribution of the blasted rock. As per the investigation, the recorded percentages of boulders generated at the sites ranged from 7% to 12.3% for the Simba quarry and from 11.2% to 23.9% for the Bisil quarry, respectively. Regression, correlation, and root mean square error (RMSE) were employed as the primary performance indicators. The study concluded that KCO outperformed MKR in accurately predicting blast fragment sizes in the examined quarries.

Mar 5, 2025

By A. Singh, R. D. Govender

The current method employed to quantify the efficiency of separation of ferromagnetic ores by wet low intensity magnetic drum separators (WLIMS) incorporates the use of a conventional benchscale Davis Tube (DT) operating at a magnetic field intensity that is equivalent to that of the drum’s surface. However, empirical research has indicated that the magnetic force, or force index, rather than the magnetic field intensity, is the critical factor in the operation of the magnetic separator (Murariu and Svoboda, 2002). Furthermore, the current aforementioned practice would yield incorrect information as the magnetic field gradients produced by a typical WLIMS unit differs considerably from that of a bench-scale DT. In light of this, research on magnetite focusing primarily on the scale-up of DT results to pilotscale WLIMS were undertaken. The investigation entailed a comparative study between that of a typical WLIMS circuit and a bench-scale DT. The purpose was to practically evaluate the performance of a WLIMS, bench-marked against the traditional laboratory-scale DT unit. Moreover, the secondary objective was to produce a magnetite concentrate graded at 69% Fe, 95% -45μm, as a suitable source of separation media for use in the Dense Medium Separation (DMS) industry. Fundamental findings of previous works provided a framework for the study, however, a realworld approach was explored. Preliminary testwork conducted at Mintek investigated the performance of a DT and a typical multi-stage laboratory-scale WLIMS circuit (operating at 1000 Gauss at the surface). The results attained indicated that a DT operating at high intensities is comparable to a single rougher WLIMS stage. Additionally, it was observed that the three stage laboratory-scale WLIMS circuit performed similarly to the DT operating at 1000 Gauss. Moreover, piloting testwork was undertaken, carried out in larger 380mm drums, and the results attained showed a strong correlation with laboratory-scale findings. Keywords: Davis Tube, wet low intensity magnetic separation, magnetite

Jan 1, 2020

By M. Kamali

In this research, in order to predict the peak particle velocity (PPV)(as vibration indicator) caused by blasting projects in the excavations of the Karoun III power plant and dam, three techniques including statistical, empirical, and neural network were used and their results were interpreted and compared. First, multivariate regression analysis (MVRA) was used as statistical approach. Next, PPV was predicted using some widely used empirical models. Lastly, an artificial neural network was used. In the ANN model, maximum charge per delay, total charge per round, distance from blast site, direction of firing, blasthole length, number of blastholes, total delay in milliseconds, number of delay intervals, and average specific charge were taken into consideration as input parameters and consequently the PPV as output parameter. The results of the techniques were interpreted from two points of view. Firstly, the correlation between the observed data and predicted ones, secondly the total error between observed data and predicted ones. The MVRA had a satisfactory correlation but its error of estimation was comparatively very high. The empirical model had reliable correlation and a small error of estimation; in total the results of empirical method were more reliable than those of MVRA. Generally, the ANN approach showed very high correlation and a very small error. The results of this research indicated that the ANN model is the best predicting model for PPV in comparison with other approaches.

Jan 1, 2010

By F. Sawyerr

A bubble population balance model is proposed for the prediction of Sauter mean bubble diameters in mechanical flotation cells. In the model, the flotation cell is treated as two separate, statistically homogeneous zones?the impeller zone, and the bulk tank zone. Balance functions for the rates of bubble breakage, coalescence, recirculation and inlet-outlet events are proposed based on classical chemical engineering research in two- and three-phase fluid mixing systems. An initial simulation of the population balance model shows trends in bubble size distributions comparable to measured distributions in industrial mechanical flotation cells.

Jan 1, 1998

By G. Akdogan, Q. G. Reynolds, S. J. Baumgartner

A computational model of a submerged arc furnace (SAF) used in the production of ferrochrome is presented. The model‘s intended use is to investigate the extent to which intrinsic and extrinsic properties of the raw materials affect burden distribution and electrical resistance within the furnace. The model is built on the discrete element method and calculates the mechanical interactions of particle distributions resulting from the flow motion of typical raw materials used in the smelting of chromium ore. This model excludes the effects of thermodynamics, furnace chemistry, and heat transfer. It illustrates how the consumption of materials (chromite pellets, flux, and reductant) is affected by changes in electrode length, reductant fractions, and reductant sizing and density during the formation of a reductant bed. The resistance calculation algorithm developed by Mintek was applied to construct networks developed from particle contacts, which can quantitatively generate estimates of the electrode-to-electrode and electrode-to-bath electrical conduction conditions.

Apr 16, 2024

By A. Adeli, A. V. Metcalfe, E. Addo, E. Sepulveda

"The accurate modelling of geometallurgical data can significantly improve decision-making and help optimize mining operations. This case study compares models for predicting copper recovery from three indirect test measurements that are typically available, to avoid the cost of direct measurement of recovery. Geometallurgical data from 930 drill core samples, with an average length of 19 m, from an orebody in South America have been analysed. The data includes copper recovery and the results of three other tests: Bond mill index test; resistance to abrasion and breakage index; and semi-autogenous grinding power index test. A genetic algorithm is used to impute missing data at some locations so as to make use of all 930 samples. The distribution of the variables is modelled with D-vine copula and predictions of copper recovery are compared with those from regressions fitted by ordinary least squares and generalized least squares. The D-vine copula model had the least mean absolute error. IntroductionIn this paper we compare the use of D-vine copula, generalized least squares (GLS), and ordinary least squares (OLS) for modelling geometallurgical data from an orebody in South America. The first objective is to construct models for predicting copper recovery (Rec) from the Bond mill index test (BWi); resistance to abrasion and breakage index (A*b); and semi-autogenous grinding (SAG) power index test (Spi). This involves fitting a D-vine copula and regression models fitted by OLS and GLS. The second objective is to investigate the performance of the fitted models for predicting Rec (Willmott and Matsuura 2005).Traditional resource model approaches either ignore the mineral processing characteristics of extracted tonnages or treat processing as an independent component of a mining operation. The net present value (or any other objective) can be truly optimized only by considering the mining operation as an integrated system in which net value is defined as the end-product that the company sells. This approach requires the resource model to be extended to include all relevant rock properties and processing responses."

Mar 1, 2019

By N. Snyders, I. Mwandawande, S. M. Bradshaw, M. Karimi

"Computational fluid dynamics (CFD) was applied to predict the average gas holdup and the axial gas holdup variation in a 13.5 m high cylindrical column 0.91 m diameter. The column was operating in batch mode. A Eulerian-Eulerian multiphase approach with appropriate interphase momentum exchange terms was applied to simulate the gas-liquid flow inside the column. Turbulence in the continuous phase was modelled using the k-_ realizable turbulence model. The predicted average gas holdup values were in good agreement with experimental data. The axial gas holdup prediction was generally good for the middle and top parts of the column, but was over-predicted for the bottom part of the column. Bubble velocity profiles were observed in which the axial velocity of the air bubbles decreased with height in the column. This may be related to the upward increase in gas holdup in the column. Simulations were also conducted to compare the gas holdup predicted with the universal, the Schiller-Naumann, and the Morsi-Alexander drag models. The gas holdup predictions for the three drag models were not significantly different. IntroductionColumn flotation is an important concentration technology that is used in the mineral processing and coal beneficiation industries. The growing interest in the use of column flotation in mineral processing has been attributed to the simpler flotation circuits and improved metallurgical performance compared to conventional flotation cells (Finch and Dobby, 1990). Flotation columns have also found other applications outside mineral processing, such as de-inking of recycled paper (Finch and Hardie, 1999).In column flotation, a rising swarm of air bubbles generated by means of air spargers is employed to collect the valuable mineral particles and separate them from the gangue minerals in a countercurrent process. Wash water, which is continuously fed at the top of the column, is used to eliminate entrained particles and stabilize the froth. The column volume can be divided into two sections – the collection zone in which the bubbles collect the floatable mineral particles, and the cleaning zone (or froth zone) where product upgrading is enhanced through the removal of unwanted particles entrained in the water mixed with bubbles from the collection zone."

Jan 1, 2019

By R. Nyirenda, B. Besa, N. Dzimunya

The accurate estimation of peak particle velocity (PPV) is crucial during the design of bench blasting operations in open pit mines, since the vibrations caused by blasting can significantly affect the integrity of nearby buildings and other structures. Conventional models used to predict blast-induced vibrations are not capable of capturing nonlinear relationships between the different blasting-related parameters. Soft computing techniques, i.e., techniques that are founded on the principles of artificial intelligence, effectively model these complexities. In this paper, we use the random forest (RF) algorithm to develop a model to predict blast-induced ground vibrations from bench blasting using 48 data records. The model was trained and tested using WEKA datamining software. To build this model, a feature selection process using several combinations of Attribute Evaluators and Search Methods under the WEKA Select Attributes tab was performed. The correlation coefficient of the actual data and RF model-predicted data was 0.95, and the weighted average of the relative absolute error (RAE) was 10.9%. The RF model performance was also compared to the equivalent-path-based (EPB) equation on the testing data-set, and it was seen that the RF model can effectively be used to predict PPV. The study also demonstrates that the EPB equation is a suitable empirical method for predicting PPV.

Mar 30, 2023

By M. Karimi

Artificial neural networks (ANNs) have found their applications in the modelling of unit operations of mineral processing plants. In this research, laboratory-scale tests were conducted, using a three-inch diameter Mozley hydrocyclone. Main parameters included pressure drop at inlet, solid per cent, vortex and apex diameter were adjusted. The corrected cut size (d50c) and the flow rates of underflow and overflow were determined. Multi layers perceptron (MLP) feed forward network architectures were designed to predict the responses. The results showed a good correlation between experimental and network output, for corrected cut size and flow rates. Keywords: hydrocyclone, artificial neural network, corrected cut size, flow rates.

Jan 1, 2010