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By David Humphreys

Assumptions about future commodity prices need to be made with close regard to the purposes for which they are being used. Prices generated for financial planning or trading purposes are unlikely to be suitable for the longer run purposes of project evaluation and reserve estimation. In this latter context, price assumptions need to be realistic, stable and rooted in a rigorous fundamental analysis of long run industry drivers. Amongst the factors to be considered in such analysis are price history, production costs and structural dynamics. It is also important that prices adopted are consistent with other information being used by a company in its decision-making.

Jan 1, 2003

By R. V. Ramani

Control is a vital management function by which operations are brought into compliance with predetermined standards to achieve the goals of an enterprise. Events that are characterized by a low degree of expectation, avoidability, and intent necessarily have a low degree of control. Such events are difficult not only to predict but to control as well. Fortunately, very few industrial situations fall into that category of events. It is an axiom of control technology that in order to control, one must first measure; further to measure, one must know the characteristics of the subject (or environment) upon which the measurement must be made. Finally, if measurements are to be made reliably, the influences that affect the measurement must be known and, if possible, understood. The complete control cycle therefore consists of the following: (1) defining the objective, (2) planning to achieve the objective, (3) putting the plan into practice with appropriate instructions, (4) measuring variables or parameters of interest, (5) transmitting and transforming the measurements to analyze the system's status and to predict the future outcome, (6) comparing these with the defined objective, and (7) determining and taking corrective actions to bring the actual results closer to the objective. The management principle underlying the control cycle is the principle of management by exception. The application of the principle and the frequency with which corrective actions are taken vary according to the purposes of the review and control. For example, the control of a machine operation (e.g., truck on a haul road) may require adjustments and therefore data, analysis, and decisions, in intervals of seconds whereas the review for ore-grade control may take several hours. Reviews, on the other hand, for cost control may be done in intervals of weeks or months. In any case, three specific purposes of the control cycle are: (1) to correct the operation so that it follows the accepted plan closely, (2) to evaluate the operation and predict future outcomes in light of changes in the environment or of more experience, and (3) to review the objectives and modify these to be reasonable and realistic.

Jan 1, 1979

By Andrew B. Cecala, Robert J. Timko, Christopher J. Pritchard

As mine venti1ation systems become more complex, it is increasingly difficult to supply fresh air to the working face. A high percentage of the fresh air entering a mine never reaches the working face because it short circuits through stoppings and doors, exiting the mine without being utilized. When ventilation limits are reached, the only option presently available to most U.S. mines is to sink a new shaft - an extremely expensive undertaking that many companies cannot afford. Reusing or recirculating a percentage of ventilating airflow may make it possible for some mines to remain operational while maintaining a safe environment for workers. Such a study was performed at a trona mine in Granger. Wyoming. A vane axial fan was used to recirculate approximately 9.4 m /s (20,000 cfm), 25% of the total ventilating airflow, back to the working face area of a three-entry continuous miner section. This study involved two evaluations: the first when the face was 305m (1,000 ft) from the recirculation fan, the second when the face was 610m (2,000 ft) from the fan. The evaluation consisted of monitoring the mine atmosphere for respirable dust, methane (CH4). carbon monoxide (CO), and nitric oxide (NO), at various locations in the intake and return entries. Several shifts were monitored, with and without the recirculation system in operation. With the recirculation system in operation. there was an increase in measured respirable dust concentrations in the intake along with a slight increase in the mine temperature (1.67°C). There were also some minor changes in CO and NO levels in the intake entry, but this seemed to depend on the proximity of the instruments to the turn-around point for the diesel haulage vehicles, than to whether the recirculation system was or was not in use.

Jan 1, 1989

By H. Wesley Peirce

Although not generally recognized, Arizona has rock salt deposits unique in the United States. This uniqueness stems from a combination of two factors: (1) relatively youthful geologic age and (2) nonmarine origin. Although Arizona shares one deposit with Nevada (Figure 1, No. I) there are three others within the State (Nos. 2, 4, 5) that are believed to be both late Cenozoic in age (less than 15 million years) and nonmarine in origin. The only other recognized nonmarine salt deposits in the U.S. are much older, being in the early Cenozoic (Eocene) Green River Formation of Colorado and Wyoming. According to Johnson and Gonzales (1), the remaining rock salt deposits in the United States are marine and either Paleozoic or Mesozoic in age. Of the seven localities shown in Figure 1, six in the Basin and Range geologic province are believed to represent nomarine late Cenozoic phenomena and one (No. 7) in the Colorado Plateau geologic province is Paleozoic (Permian) in age and marine in origin. The following comments are intended to update the general geologic highlights of these deposits and to summarize the nature of their present utilization.

Jan 1, 1979

By M. Fig, A. Juganda, M. Zurhorst, J. Brune, C. Strebinger, G. Bogin

"Understanding the airflow patterns in and around the longwall panel can help to identify poorly ventilated areas that may be prone to methane accumulation. Ventilation network modeling provides quick simulation time, and flexibility in adjusting airway parameters and ventilation controls. Such modeling is effective for analyzing linear ventilation networks in underground mines, but cannot compute flows in caved areas of hard rock mines or longwall gobs. The use of Computational Fluid Dynamics (CFD) enables a more detailed investigation of the interaction between the airflows in these caved areas but requires much longer computational times, designing complex meshes and determination of a variety of modeling parameters. Airway friction factors and ventilation controls are simple to implement in a network model but can be difficult to model and adjust in CFD. This paper will focus on using mine ventilation network simulation to identify and analyze key parameters that are affecting airflow distribution in and around the longwall panels, prior to more detailed analysis with CFD modeling, using the example of a longwall coal mine. INTRODUCTION In longwall coal mine ventilation systems common in the U.S., the main purpose of bleeder ventilation is to dilute methane gas and prevent the formation of explosive gas mixtures in the caved, poorly ventilated gob areas and adjacent mine workings. In longwall operation, these critical areas include the development headings inby the longwall face, and the gob. Methane-air explosions in these areas may be ignited by spontaneous combustion, rock-on-rock friction and other sources. Although rare, ignitions due to rock-on-rock friction caused by roof falls caving into the longwall gob are possible and were suspected in the Willow Creek mine explosions in 1998 and 2000 (Elkins et al., 2001; McKinney et al., 2001). Rock-on-rock frictional ignitions are difficult to prevent, as they occur in inaccessible areas. Therefore, rendering the gob area inert is essential to prevent the formation of explosive mixtures. Research by Gilmore et al. (2015) and Brune et al. (2015) has shown that gob inertization can be a difficult or impossible when using bleeder systems, since the flow of gases through the gob depends on the gob permeability and the amount of fresh air leaking from the face and the immediate roof caving conditions cannot be controlled by the mine operator. Having the ability to predict the location where explosive gas zones (EGZs) form inside the gob will be helpful for designing the best ventilation setup to minimize explosion and fire risks."

Jan 1, 2018

By Rami Abousleiman, Gabriel Walton, Sankhaneel Sinha

"Although conventional coal mine designs are conservative regarding pillar strength, local failures such as roof-falls and pillar bursts still affect mine safety and operations. Previous studies have identified that discontinuous, layered roof materials have some self-supporting capacity. This research is a preliminary step towards understanding these mechanics in coal-measure rocks. Although others have considered broad conceptual models and simplified analogs for mine roof behavior, this study presents a unique numerical model that more completely represents in-situ roof conditions. The discrete element method (DEM) is utilized to conduct a parametric analysis considering a range of in-situ stress ratios, material properties, and joint networks to determine the parameters controlling the stability of single-entries modeled in two-dimensions. Model results are compared to empirical observations of roof-support effectiveness (ARBS) in the context of the coal mine roof rating (CMRR) system. Results such as immediate roof displacement, overall stability, and statistical relationships between model parameters and outcomes are presented herein. Potential practical applications of this line of research include: (1) roof-support optimization for a range of coal-measure rocks, (2) establishment of a relationship between roof stability and pillar stress, and (3) determination of which parameters are most critical to roof stability and therefore require concentrated evaluation.INTRODUCTIONRecent studies on pillar design have suggested that the assumptions utilized in traditional methods such as tributary area theory (TAT) (i.e. overburden has no self-supporting capacity, infinite pillar array, and CHILE material properties) are invalid in many geo-mining conditions (Esterhuizen, Mark, and Murphy, 2010; Frith and Reed, 2017). Although TAT is conservative with respect to pillar loading, and mine fatalities have steadily decreased since 1990, roof-falls and pillar bursts still occur and injure hundreds of miners per year (Mark, Pappas, and Barczak, 2011). Even though large-scale global mine failure has not occurred in a US coal mine since 2007, the mechanics of global and local stability as governed by the relationship between geologic parameters, mine geometry, and engineering design are still not well-constrained.Numerical methods are a highly effective tool that can be used to gain further insight into these relationships. When using such an approach, calibration to existing empirical relationships is imperative to ensure that numerical model results are realistic. In this study, we utilize the discrete element method (DEM) approach in Itasca’s Universal Distinct Element Code (UDEC v. 6.0) (Itasca, 2010).DEM accounts for explicit separation and contact of individual deformable blocks (Jing, 2003) and captures how discontinuities can affect system behavior. Discontinuous and anisotropic rockmass properties are typical features in coal-measure rock which contain various horizontal (bedding) and sub-vertical (joints) fractures depending on their depositional environment, diagenesis, and historic and current stress states."

Jan 1, 2019

By R. W. Barbaro, R. V. Ramani, K. V. K. Prasad, P. T. Luckie

Discussion by A. Unal Barbaro et al. (1990) implemented a tedious study aimed at delineating the short-scale spatial variability of sulfur in a coal seam. It is not possible, however, to extend their conclusions to any other coal seam or use their in any other fashion, because the background geology was not presented. Nor were the conclusions accompanied by geological interpretations in the paper. In addition, an unfortunate printing error occurred where the total sulfur (determined by High Temperature Combustion) variogram in Fig.3 was duplicated in Fig. 2 instead of the seam height variogram. A more serious error, however, has been committed in the definition of the variable, seam height. What is defined as seam height by Barbaro et al. (1990) is, in essence, mining height, and is not a regionalized variable that should be studied by geostatistical methods directly. Despite the fact that the variable, seam height, is not defined in the paper explicitly, the following quotation discloses the fallacy: "All roof rock that exceeded 1.8 m (6 ft) from the floor was not taken because the longwall was operated to not mine more than 1.8 in (6 ft) unless the coal seam height exceeded 1.8 m (6 ft)." From this definition, the seam height, and therefore the sampling height, is equal to the actual coal thickness, if the coal thickness is greater than 1.8 m (6 ft). Otherwise, it is equal to the sum of the coal thickness plus the thickness of the roof rock that complements the thickness to 1.8 m (6 ft). In the latter case, the seam height is constant and equal to 1.8 m (6 ft). It is possible to conduct a variogram study on a pool of samples that are realizations of two different variables. But the conclusions derived would not belong to any one of the two variables uniquely and, therefore, do not possess any significance. Geostatistical analysis is irrelevant for the sum of multiple regionalized variables formed by arbitrary selections. In a two-seam setting, for example, the mining height, as defined by the thickness of one seam at one location and the thickness of both seams at another location (due to quality and/or minimum thickness considerations perhaps), should not be used in the calculation of one common variogram. The two seams should be modeled separately. They can then be combined according to the specific purposes of the study. On the other hand, if a constant is added to a regionalized variable (to incorporate dilution perhaps), the variogram of the new variable will not change. Barbaro et al. (1990), surprisingly, does not give any geological interpretation of their results despite the fact that most of them can be explained by the origins of sulfur in a coal seam. The presentation of the results of a geostatistical study with no reference to the geology of the deposit is uninformative. It may also be misleading for the potential users of geostatistics, It is not unusual to find nuggets of pyrite in coal seams. In such cases, pyritic sulfur will probably display a spatial structure for only a very small distance that will appear in the experimental variogram as no spatial correlation. This very well known phenomenon is called the pure nugget effect in geostatistics (Journel and Huijbregts, 1978) and perhaps can explain the lack of correlation found for pyritic sulfur content. The lack of correlation found for the total sulfur content may also be explained in the same way because the total sulfur content is dominated by the pyritic sulfur content in this case study. One should notice, however, that the situation may completely be reversed after cleaning the coal. Not all of the inorganic sulfur should be expected to be in the form of pyrite nuggets in a coal seam. It may also be disseminated in the coal seam and it is expected that it follows a certain spatial structure. However, an existing spatial structure may be masked by including a part of the roof rock, rich in sulfides, into the seam thickness in an arbitrary fashion because areas having a sandstone roof sometimes are known to show a higher sulfur content due to the downward percolation of solutions rich in iron sulfides (Clark, 1979). Plants use sulfur in their growth processes. Much of this sulfur is bound organically during peat accumulation and coal formation (Cecil et al., 1978.) This suggests a spatial structure of some sort for the organic sulfur. However, it is not possible to test this hypothesis because the results obtained by the authors for the organic sulfur content are not given in this paper. For this reason, the conclusion that simple average of the nearby samples would provide the best unbiased estimates is questionable for organic sulfur and is not based on any substantial supporting evidence. It is suspected that no spatial structure was detected and this was due to high sampling and laboratory analysis errors. Before concluding that sulfur variability in the seam at the location of study was random, a more detailed study for the disseminated non-pyritic sulfur should have been conducted, not for the sake of scientific curiosity only, but also due to its utmost importance with regard to coal cleaning and emission control. Pyritic sulfur can be cleaned to a considerable extent, whereas organic sulfur can not, making the emission control strategies highly dependent on the spatial distribution of the organic sulfur (Knudsen, 1981). In the light of these facts, one wonders why Barbaro et al. (1990) did not present the results of their study for the sulfate and organic sulfur content. References Barbaro, R.W.. et al., 1990, "Short-Scale Spatial Variability of Sulfur in a Coal Seam,' Mining Engineering, Vol. 42, No. 11, pp. 1267-1269. Cecil, C.B., et al., 1978, "Geology of Ccontaminants in coal," report prepared for Environmental Protection Agency, North Carolina, 123 pp. Clark, W.J.. 1979, "An interfluve model of the upper Freeport coal Bed in part of western Pennsylvania," unpublished MS thesis, University of South Carolina, 57 pp. Journel, A.G., and [Huijbregls], Ch. J., 1978, Mining Geostatistics, Academic Press, London, 600 pp. Knudsen, H.P., 1981. "Development of a Conditional simulation model of a coal deposit," unpublished PhD dissertation, The University of Arizona, 109 pp.

Jan 1, 1992

By D. G. Ball

Other than being used to convey fill material for the gob stowage or building of ribside packs on advancing longwell faces, pneumatic conveying has not been used for the transport of materials on a large scale in mining operations. The technique is extensively used in the wood products industry for conveying wood chips, limestone and other ingredients for the manufacture of paper, for the transfer from railcars, barges or ships to silos or stockpiles and further transfer into the pulp mills. This method of material transfer has the distinct advantage of transporting suitable material over horizontal and vertical distances within the same system. In considering material associated with mining, this includes, ore, coal and rock up to 4 inch cubed size, throughputs of 200 tons per hour over horizontal distances of 3000 feet or vertical lifts of 2000 feet. In recent years tests have been carried out to determine the physical and economic limitations of pneumatic hoisting, the results of which have led to the installation of two fully productive coal hoisting systems at mines of the National Coal Board in Great Britain. The equipment used for the pneumatic hoisting of coal or waste rock is described in this paper, together with a brief history of the tests which led up to the installation of the two hoisting systems in Great Britain.

Jan 1, 1977

By Lana Alagha, Keitumetse Cathrine Monyake

The abstract discusses the synthesis and investigation of polyacrylamide functionalized chitosan polymers (CS-PAMs) as selective depressants for pyrite in the flotation of galena and chalcopyrite from complex sulfide ore. The study employed response surface methodology (RSM) to model and optimize the flotation process, varying parameters like the degree of deacetylation of chitosan, weight ratio of chitosan to acrylamide, and CS-PAM dosage. The developed RSM models effectively predicted iron and lead grades and recoveries, while optimization showed optimal pyrite separation from galena and chalcopyrite using CS-PAM synthesized from chitosan with a high degree of deacetylation and a specific chitosan:acrylamide weight ratio.

Apr 9, 2022

By D. Rajkovic

Geothermal sources are exploited in two basic forms, i.e., the primary form of thermal energy and that converted into electrical energy by using an adequate thermodynamic cycle. There is a possibility of applying several processes for the conversion of thermal into mechanical or electrical energy, which depend on the thermodynamic characteristics of the geothermal water. Geothermal sources in Croatia do not contain steam of relatively high temperature, like in Italy, although both countries are nearly in the same position with respect to geothermal belt. Geothermal sources in Croatia are mostly so-called low-temperature geothermal sources with water temperatures lower than 100°C (Cubric and Jelic, 1993). A few of them have been exploited, although the percentage of utilization of their available power is still very small. The low-temperature potential is a major disadvantage when it comes to power generation, since state-of-the-art geothermal power plants require reheated steam or hot water to operate. There is a common belief that the optimum way of exploiting low-temperature geothermal sources is heat generation. Therefore, this analysis is especially aimed at potential users and showing the technical and economical possibilities of using low-temperature sources for conversion of thermal energy into mechanical work and electrical energy.

Jan 1, 2003

By J. H. Tu

The technical paper correctly points out that the kriging variance is not a good measure of the uncertainty of the estimated (i.e., kriged) value of individual blocks. The au thors claim that their proposed jackknife method, which is a rekriging of each block by eliminating, in turn, one sample from the original sample set and then taking the average of the rekriged estimates, not only gives good block estimates, but the resulting jackknife kriging standard deviation is a useful indicator of the "true uncertainty associated with block estimates." However, they immediately abandon the idea of using the block-by-block standard deviations, reasoning that these standard deviations are not independent and that there is no easy way to utilize them. There may be another reason for not using them. The jackknife standard deviations for individual blocks given in their example are mostly in the range of 0.004 to 0.005 oz/st (0.14 to 0.17 g/t) with only one block having a high value of 0.012 oz/st (0.41 g/ t). These individual block standard deviations are as low as the jackknife standard deviation for the mean grade of the entire shape, i.e., 0.0041 oz/st (0.14 g/t). Do they represent the "true uncertainty" of the individual block estimates? Could the authors explain this? In a global shape consisting of a large number of blocks, any given sample will affect the kriged estimate of only those few blocks within its vicinity. This is the rationale for the authors' selective rekriging, making the jackknife algorithm more efficient. On second thought, why not do away with jackknifing altogether? Just cumulate and normalize, if necessary, the kriging weights of each sample used during the ordinary block kriging process, and then compute the global variance from these kriging weights and their respective sample grades? After all, isn't the global mean grade nothing but the weighted average of the samples used in the estimation? Reply by G.S. Adisoma and M.G. Hester The jackknife is one of the many tools in a practitioner's toolbox to solve estimation problems. The strengths of the technique lies in its simplicity, i.e., it uses the concept of mean and standard deviation and the fact that it can be easily combined with other tools, in this case kriging. Because the jackknife kriging (JK) estimate is also the mean of the pseudovalues, the JK standard deviation is attractive just as the standard deviation of the mean explains the variability of the data. The difference is that the pseudovalue calculation in jackknife kriging uses the ordinary kriging (OK) weighting scheme instead of simple arithmetic averaging. The data used to illustrate the jackknife technique in the paper consist of high values that are roughly three times the low values. The resulting JK estimate of the block grades show that the highest estimate is roughly twice the grade of the lowest estimate. The contrast between the low and the high estimate is more evident in the JK estimate than in the OK estimate, even though the mean grades of the blocks for the two estimates are very similar. Nonetheless, in this paper, we are concentrating more on the need for a more realistic measure of uncertainty, or precision, for the estimate. Unlike its OK counterpart, the JK standard deviation of the blocks clearly reflects the original data variation. The highest JK standard deviation of the blocks is three times its lowest value. This follows our intuition that, when the samples used to estimate a block is more variable, the resulting estimation variance (or standard deviation) should be higher than the case where the samples are more uniformly valued. However, block-by-block standard deviation or variance is of little practical value in reserve estimation and classification, as well as in mine planning. One is usually more interested in quantifying not the variance of the individual block estimate, but the uncertainties associated with a much larger dimension, such as the minable reserve. Thus, the thrust of the paper is to find a simple way to obtain a single estimation variance or standard deviation associated with the reserve grade estimate. The discussion by J.H. Tu did not mention how one would obtain the global variance from the OK weights and the sample grades. As a technique that offers a data valuebased measure of uncertainty for its estimate, the "leave-one-out" jackknife fills this need nicely through the block kriging shortcut approach described in the paper. Note: The first column and the last two columns of Table 3 in the paper should have contained a single number each, namely, an OK estimate of 0.0317, a JK estimate of 0.0333 and a JK standard deviation of 0.0041 oz/st, respectively, for the shape, as are obvious from the text. ?

Jan 1, 1998

By Zoheir Khademian, Gabriel S. Esterhuizen, Morgan Sears

Based on the 2010-2019 Mine Safety and Health Administration (MSHA) accident report database, 91% of reported ground control accident in the US longwall mines were caused by gateroad roof instability. This can be related to the significant changes in loading conditions that gateroads are subject to from the development to the longwall abutment loading phases, combined with the thinly bedded shale roof, found in many US longwall coal mines. One of the challenges in designing efficient support systems is to understand the roof instability mechanisms along with support-roof interactions under this complex loading condition. Field monitoring of roof displacement and stress changes coupled with detailed analyses using numerical models are well-documented techniques to study the causes of roof instabilities. However, traditional modeling techniques require additional calibration to simulate delamination and buckling of shale roof, one of the main roof instability mechanisms in longwall mines. One alternative modeling technique is the UDEC Bonded Block Method (BBM) where bedded roof and support interactions can be explicitly captured. This paper presents the development of a BBM model of a longwall entry at a 180 m depth of cover with a three-entry gateroad layout along with roof extensometer monitoring results. The models are validated against the monitoring data, showing the potential for BBM numerical models capturing the complex roof and support system interactions to assist with optimizing gateroad layouts and support systems.

By J. L. Kohler

The knowledge-based expert systems (KBES) is a potentially powerful technique to the mining engineer in traditional areas of mine design and operations, as well as in the emerging areas of mining automation and robotics. Since KBES is still experimental in many respects, caution must be exercised as this "high tech" tool is developed and applied to mining. The objective of this paper is to demystify expert systems by introducing the elements of KBES, discussing the strengths and weaknesses, and identifying some areas of application in mining, based on the authors' experiences.

Jan 1, 1986

By J. H. Tu

The technical paper correctly points out that the kriging variance is not a good measure of the uncertainty of the estimated (i.e., kriged) value of individual blocks. The authors claim that their proposed jack-knife method, which is a rekriging of each block by eliminating, in turn, one sample from m the original sample set and then taking the average of the rekriged estimates, not only gives good block estimates, but the resulting jackknife kriging standard deviation is a useful indicator of the "true uncertainty associated with block estimates." However, they immediately abandon the idea of using the block-by-block standard deviations, reasoning that these standard deviations are not independent and that there is no easy way to utilize them. There may be another reason for not using them. The jackknife standard deviations for individual blocks given in their example are mostly in the range of 0.004 to 0.005 oz/st (0.14 to 0.17 g/t) with only one block having a high value of 0.012 oz/st (0.41 g/ t). These individual block standard deviations are as low as the jackknife standard deviation for the mean grade of the entire shape, i.e., 0.0041 oz/st (0.14 g/t). Do they represent the "true uncertainty" .of the individual block estimates? Could the authors explain this? In a global shape consisting of a large number of blocks, any given sample will affect the kriged estimate of only those few blocks within its vicinity. This is the rationale for the authors' selective rekriging, making the jackknife algorithm more efficient. On second thought, why not do away with jackknifing altogether? Just cumulate and normalize, if necessary, the kriging weights of each sample used during the ordinary block kriging process, and then compute the global variance from these kriging weights and their respective sample grades? After all, isn't the global mean grade nothing but the weighted average of the samples used in the estimation? Reply by G.S. Adisoma and M.G. Hester The jackknife is one of the many tools in a practitioner's toolbox to solve estimation problems. The strengths of the technique lies in its simplicity, i.e., it uses the concept of mean and standard deviation and the fact that it can be easily combined with other tools, in this case kriging. Because the jackknife kriging (JK) estimate is also the mean of the pseudovalues, the JK standard deviation is attractive just as the standard deviation of the mean explains the variability of the data. The difference is that the pseudovalue calculation in jackknife kriging uses the ordinary kriging (OK) weighting scheme instead of simple arithmetic averaging. The data used to illustrate the jackknife technique in the paper con¬sist of high values that are roughly three times the low values. The resulting JK estimate of the block grades show that the highest estimate is roughly twice the grade of the lowest estimate. The contrast between the low and the high estimate is more evident in the JK estimate than in the OK estimate, even though the mean grades of the blocks for the two estimates are very similar. Nonetheless, in this paper, we are concentrating more on the need for a more realistic measure of uncertainty, or precision, for the estimate. Unlike its OK counterpart, the JK standard deviation of the blocks clearly reflects the original data variation. The highest JK standard deviation of the blocks is three times its lowest value. This follows our intuition that, when the samples used to estimate a block is more variable, the resulting estimation variance (or standard deviation) should be higher than the case where the samples are more uniformly valued. However, block-by-block standard deviation or variance is of little practical value in reserve estimation and classification, as well as in mine planning. One is usually more interested in quantifying not the variance of the individual block estimate, but the uncertainties associated with a much larger dimension, such as the minable reserve. Thus, the thrust of the paper is to find a simple way to obtain a single estimation variance or standard deviation associated with the reserve grade estimate. The discussion by J.H. Tu did not mention how one would obtain the global variance from the OK weights and the sample grades. As a technique that offers a data value-based measure of uncertainty for its estimate, the "leave-one-out" jackknife fills this need nicely through the block kriging shortcut approach described in the paper. Note: The first column and the last two columns of Table 3 in the paper should have contained a single number each, namely, an OK estimate of 0.0317, a JK estimate of 0.0333 and a JK standard deviation of 0.0041 oz/st, respectively, for the shape, as are obvious from the text.

Jan 1, 1997

By Rahul Bhaduri

Mercury levels in Twin Creeks Vista pit ore (previously Chimney Creek, North pit ore) increased substantially since plant start-up in November 1987. Consequently, modifications were necessary to minimize industrial hygiene hazards and potential operational problems. The original plant design included mercury control measures. With progressive increases in mercury levels, the issue had to be re-addressed. Modifications to operational practice, industrial hygiene and management philosophy will be discussed. Additionally, a detailed examination of the mercury vapor suppression system currently employed at Twin Creeks will be presented.

Jan 1, 1994

By John W. Wilson

Every operation connected with the working of a deep-level mine has, to a greater or lesser extent, a direct bearing on the costs of production, and hence, profitability of the mine. If adequate mine planning is not done in the early stages in the life of a mine, considerable expenditure can result from the cost of maintaining mine service tunnels, production delays in stopes caused by poor strata control, distortion of fittings in hoisting shafts, rockbursts and so on. In fact, the optimum profitability of a deep-level, hard-rock mine can only be achieved if the layout of the shaft systems, ore passes, tunnels, stoping areas and other underground excavations are designed to withstand or accommodate the rock stress conditions that will be created by mining operations. In South Africa where the deepest mines in the world are located, access to the underground workings of most deep-level gold mines is via vertical shaft systems and a network of tunnels connecting the shaft systems to the stoping areas. In current practice, there are two basic methods of extracting the gold-bearing reef,* namely, the scattered stoping method and the longwall stoping method. The choice of the stoping method is governed largely by the anticipated gold distribution and payability of the reef in the area, the geological conditions, the degree and complexity of faulting and dyke intrusions, the depth at which the gold-bearing reef is to be mined and the inclination and width of the reef. The longwall system was first introduced at the East 717 Rand Proprietary Mines Limited in 1942 in an effort to combat the rockburst hazard which appeared to increase with increase in depth. This mining method became standard practice on that mine for stoping operations 700 ft. below the surface. Since that date, several deep mines on the Central Rand, East Rand and West Wits gold mining areas have adopted this method.

Jan 1, 1977

By S. Afrouz, C. Keles, E. Sarver, A. Greth

While respirable dust monitoring has long been done on the basis of mass concentration, it is increasingly recognized that specific dust characteristics may play an important role in health outcomes. Scanning electron microscopy with energydispersive X-ray (SEM-EDX) can be used for detailed particle analysis, but the quality of results can be dependent on the sample substrate; ideally, particles should be deposited on a smooth, uniform substrate like polycarbonate (PC). While direct air sampling onto PC filters is possible, this is not the standard approach in many situations. For example, in coal mines, polyvinyl chloride (PVC) filters are often used. They enable conventional mass-based analysis — of the total respirable dust, and the content of crystalline silica in this fraction — but are fibrous and thus not ideal for SEM-EDX or other microscopy. To address this issue, the current work tested a simple method to recover respirable dust particles from PVC filters and redeposit them to PC. Results generally indicated that recovered particles are representative of the original dust sample in terms of mineralogy distributions. Where this was not the case, the issue appeared to be with high particle loading density during SEM-EDX work rather than related to the recovery/redeposition method itself. This bodes well for applications where a single PVC filter sample could be used for nondestructive analysis to determine respirable dust mass and/or respirable silica mass, followed by preparation for SEM-EDX analysis to evaluate particle characteristics. The recovery and redeposition method might also be adapted for other applications, such as to enable the analysis of respirable dust samples collected by the continuous personal dust monitor (CPDM).

Oct 1, 2024

By R. L. Pozzo

The electrochemical nature of mineral-grinding media interaction in the wet grinding of one and two sulfide mineral ores was investigated with regard to the flotation behaviors of the minerals. Two iron sulfides (pyrite and pyrrhotite) and mild steel were used to represent the sulfide minerals and the grinding media, respectively. Galvanic currents in two-and three-electrode combinations under abrasive and non-abrasive conditions were correlated with the floatabilities of these minerals under corresponding conditions.

Jan 1, 1988

By Kenneth A. Pietila

The thickening and dewatering characteristics of refuse slurries are as variable as the coal mining operations and preparation plants that create them. This paper discusses these characteristics based on field studies at a number of mine sites. The connection between bench and full scale performance is confirmed with the design, construction and operation of an integrated 18m (60 ft) diameter thickener and 2.5 meter belt filter press system in Western Kentucky. The data from this system are presented.

Jan 1, 1984

By J. A. González-Anaya

This laboratory work presents a study of the effect of ultrafine grinding on the gold recovery of a highly refractory gold concentrate. The particle size of the concentrate was P80 = 46 µm and the main species present were quartz (28.58%), dolomite (25.63%), pyrite (20.93%), arsenopyrite (15.39%) and 112 g/t gold. After optimizing hydrated lime and lead nitrate additions, standard cyanidation with 1% NaCN, pH = 12.3, 25% S/L permits to recover only 9.4% of the gold. Different particle sizes were submitted to cyanidation. When the concentrate was ground to ultrafine sizes (P80= 4.6 µm), the gold recovery for the same cyanidation conditions increased to 67%, with cyanide consumption of 66.7 kg NaCN/t. Additional tests were performed in order to reduce the cyanide consumption. The reduction of the cyanide concentration in the leaching solution from 1 to 0.5% and the addition of 100 g Pb(NO3)2/t allowed the reduction of the cyanide consumption to 27.6 kg NaCN/t (58% reduction), while maintaining the same gold recovery of 67%. The remaining gold could be present as very fine inclusions or in solid solution in the matrix.

Jan 1, 2011