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Contents A-Metal Mining B-Minerals Beneficiation F-Coal

Jan 11, 1951

By W. A. Koehler, W. L. Eaton, Reynold Q. Shotts, M. E. Hinkle, M. R. Geer, Ralph M. Hunter, H. F. Yancey, Andrew B. Crichton, E. Zimmerman, S. H. Ash

R. Maize and H. P. Greenwald presiding) E. T. POWELL*-I would like to ask either Mr. Ash or Mr. Eaton if they know of any place where the Mine Inspectors' for¬mula has been used that water has flowed from one colliery to the other. W. L. EATON (authors' reply)-We have made considerable study but we have not gotten around to very many of the barrier pillars. I would not say that any or all of them have been designed to the strict figures of this formula. Most of those ex¬amined will not hold back the water that they are supposed to hold back. E. T. POWELL-If the pillar remains ac¬cording to the Mine Inspectors' formula? W. L. EATON-We have not found very many where the provisions were followed. E. T. POWELL-Where the Mine Inspec¬tors' formula has been fully complied with, have there been failures or not? W. L. EATON-I cannot say. Most of these barrier pillars probably were designed in accordance with this formula, but most of them, if they were so designed, are not in that shape now because a large number of them will not hold back the water; they have been reinforced, and they are punc¬tured in various places. Holes have been driven through them to relieve the hydro¬static pressure to avoid a failure. They do not dare to have a failure. In many instances, the barrier is punc¬tured to avoid a failure. In order to mine their coal safely, many companies must pump water from mines that were aban¬doned many years ago. R. MAIZE-That is true, Mr. Eaton, but do you know of any case where a barrier pillar actually failed and flooded an ad¬joining mine when the pillar had been left in accordance with the Mine Inspectors formula?

Jan 1, 1948

[Contents AH-Metal Mining and Industrial Minerals Pipeline Transportation of Phosphate. (Paper by I. S. Tillotson, R. B. Burt, and J. A. Barr, Transactions AIME, 193, 273; Mining Engineering. March 1952. Discussion by Howard Howie.) 1066 B-Minerals Beneficiation The Effect of Zinc Deleading Operations on Lead-Zinc Selectivity at the Parral and Santa Barbara Mines. (Paper by C. L. Boeke an G. C. Gunther, Transactions AIME, 193, 495; Mining Engineering. May 1952. Discussion by I. M. Symonds, L. H. Lange, Norman Weiss, H. S. Wildman, C. F. Zeuch, E. H. Crabtree.) 1067 Jaw Crusher Capacities, Blake and Single-Toggle or Overhead Eccentric Types. (Paper by D. H. Gieskieng, Transactions AIME, 190, 971; Mining Engineering. November 1951. Author s supplement.) 1069 Screened Ore Use for Fine Grinding at Lake Shore Mines. (Paper by Bunting S. Crocker, Transactions AIME, 193, 499; Mining Engineering. May 1952. Discussion by Fred C. Bond, J. Myers, Norman Weiss, Harlowe Hardinge, Oscar Johnson, L. E. Djingheuzian.) 1069 The Third Theory of Comminution. (Paper by Fred C. Bond, Transactions AIME, 193, 484; Mining Engineering. May 1952. Discussion by Milton C. Shaw and Donald R. Walker; R. J. Russell.)1071 Flotation Rates and Flotation Efficiency.t (Paper by Nathaniel Arbiter, Transactions AIME, 190, 791; Mining Engineering. September 1951. Discussion by Gregoire Gutzeit and Porter Clements.) 1074]

Jan 1, 1952

By R. T. Rudd

DISCUSSION R. T. RUDD I have the following comments on Mr Bosman's paper. Before giving the company permission to discharge neutralized mine drainage containing 1800 mg/l of dissolved solids into a public stream, my Department had to consider the matter from both the legal and the practical viewpoint.

Jan 4, 1974

The Role of the Drilling Contractor BY FERRIS E. SAINSBURY Ron Haxby, Occidental Minerals What is the best method for drilling a 12-in. diam hole to a depth of 1200 ft and maintaining the deflection within 25ft from vertical? Ferris Sainsbury: In order to drill a 12-111. hole down to 1200 ft, In most places you would have to employ a rotary tricone bit With a down-the-hole hammer, I expect by that depth significant Inflows of water would be experienced Even with foams, we would not be able to get the water out of a hole of that diameter So the down-the-hole tricone method of drilling would be best. To keep it straight, we would probably employ the largest square drill stem compatible with the diameter of the hole. Ron Haxby: Is it common in drilling contracts to put in a vertical hole clause? Ferris Sainsbury: You can put in the vertical hole clause Ron Haxby: Does it cost more? Ferris Sainsbury: Normally, if the hole deviates, the driller can correct it with deflection wedges and will charge it back to you at cost, plus 10% or somewhere along that range Any drill hole has a tendency to go perpendicular to the dip, and there are times when stabilization systems are not successful in preventing the hole from turning into the dip of the formation. The only solution, in this situation, is to go in with an oriented wedge and kick the hole back out.

Jan 1, 1979

By Katzer R. Klimpel, J. Sewell

Dr. Roussey Roussev The use of chemical additives on grinding is of significant importance considering the high energy consumption of grinding processes. My research indicates that the dispersant approach explains the effects, in this particular case of wet grinding. Further, the dispersant approach combined with torque measurements give the possibility of clarifying the mechanisms involved when using grinding aids. I find the conclusions derived from the data obtained to be correct and useful for grinding. In general, I agree with the explanations. However, I would like to discuss several additional points that I consider to be important, and in the same time to mention some of my own results from investigations similar to those presented in the study under discussion. The "bed volume concept" and the change of slurry viscosity explain adequately the data plotted on fig. 3, in conjunction with Figs. 1 and 2. However, the significant increase of the average torque in the case of 83% solid with XFS-4272 introduces the necessity of establishing the optimum of percent solid when dispersant is used. For example, the optimum percent of solid for different materials varies. One of the reasons is the material's mass density. On the other hand, it is necessary to determine the concentration of the chemical used. Because of this, it would be interesting to know how the cited concentration was selected. The supportive discussion and verification by Dr. Roussev, with results similar to that of the paper, is appreciated. I will try to answer the additional questions he has raised. To begin with, the mill was fitted with six lifters of approximately one-quarter inch height. There has been extensive lab and plant level work to understand downstream influences of using dispersants like XFS-4272*. In sulfide ore processing, which is the area in which most industrial scale testing has been performed with this approach, neutral to positive influences have been observed. For example, positive effects (e.g., increased recovery) have been noticed in flotation operations involving ores that have a tendency to produce excessive slimes upon grinding. (1) Also, the use of a dispersant has been shown to demonstrate a positive influence on classification by improving the sharpness of separation and preventing a decrease in d50 value that usually accompanies increasing flow rates to classifiers.(2) The biggest potential downstream problem is in thickening, which at the dosages involved, has not been a problem in plant scale usage. The commercial price of dispersants like XFS-4272 is quite variable depending on source, concentration, etc, but a typical cost of XFS-4272 to achieve a 10% increase in throughput in industrial mills has been between 20 and 35- US per ton of feed ore (1981).

Jan 12, 1982

By K. Hanumantha Rao, A. Nayak, K. S. Narasimhan, S. N. Mahapatra

B.A. Hancock It is not at all surprising that causticized potato and potato derived amylopectin starch solutions performed much better than their parent starches. Some preparation is required to rupture the starch granules to effect the polymeric adsorption and interparticle bridging necessary for selective flocculation. In laboratory work comparing the deslime performance of causticized and autoclave cooked laboratory corn starch solution preparations, it was found that higher deslime weight rejections, with attendant proportionally greater iron unit losses, occurred with the causticized starch. These results may be specific to the ore involved but they do suggest that cooking and causticizing cause different starch granule rupture and/or starch breakdown, which have an effect on desliming response. I calculated from the data in the article that the slimes product grades were high - 24.3% and 20.3% Fe when 53.7% and 54.5% Fe concentrate products were obtained, respectively, in Table 4, and 21% Fe with a 62.6% Fe concentrate in Table 5 - using the natural tailings sample, which had a head of 34.3% Fe. It may be advisable for the authors to consider different starch preparations in future investigations. The combination of upgrading and selectivity results presented in Table 4 are not as good as the authors suggest. The authors' claim that a system has been developed to produce saleable concentrates from the Kudremukh tailings is quite disconcerting. There are many hurdles yet to be crossed before commercial application of selective flocculation becomes possible because differences between the very small-scale laboratory tests conducted and commercial application are rather large. Among the many differences are varying circuit feed grades that will occur from use of tailings, the apparent face that much lower tailings grades will be encountered in practice (it is much easier to achieve a high concentrate grade with reasonable recoveries using 34.3% Fe tailings as in the study rather than 25.3% Fe tailings grades that the plant apparently averages), the hydraulic nature of the thickeners used in operations compared to the static system used in laboratory tests, the different size distributions that will be obtained from a plant closed grinding-classification circuit, and differences in water used in a plant operation and the laboratory. The authors wrote that it was necessary to overgrind to be sure that the coarse gangue would not settle with the iron oxide floccules. This situation is likely to be exaggerated in commercial operations where it is assumed cyclones would be used for classification. Because cyclone classification is greatly influenced by particle densities, there will probably be an even greater difference in size between the iron and gangue particles in the plant, which would make the gangue slightly coarser still in relation to the iron. This would make the selective flocculation-desliming separations using the procedure employed by the authors even more difficult and, using the dispersant system the authors employed, greater overgrinding would be required. To grind finer to minimize the coarse gangue in the flocculated iron oxides is quite inefficient and appears not to broach the problem. The actual problem appears to be insufficient dispersion of the ground pulp. In this situation, addition of a dispersant would likely be required to attain a sufficiently high pulp dispersion level to efficiently effect a selective flocculation-desliming separation. Although the very coarse particles would still have a tendency to settle with the floccules, it probably would be found unnecessary to overgrind as much as indicated. Use of an optimum combination of dispersant and pH modifying reagents may also significantly improve the selectivity of desliming. Additionally, although it is possible that sufficient dispersion may be obtained by pH control alone in some situations, it is quite probable that added dispersity was obtained in the reported work from using distilled water. It is research experience that distilled water enhances dispersion. In commercial operations it may not be expected that sufficient dispersion will be obtained by pH control alone, unless the water used in the process is by nature quite dispersive. Overall, a change in the Kudremukh tailings dispersant scheme appears necessary where a dispersant is used in conjunction with a pH modifying reagent. With this change, different dispersion-flocculation responses will result that would have to be further evaluated. Therefore, it is still an open question whether an efficient and effective selective flocculation separation using Kudremukh tailings may be obtained that will produce saleable concentrates.

Jan 9, 1986

T. T. READ, BUREAU OF MINES: Mr. McAuliffe has asked a very practical question. Expressed in another way, the question is: If we have decided to use rock dust to prevent coal dust from exploding, have we got to take rock, that is, solid material,, break it up and finally grind it into dust (and fine grinding is very expensive), or can we take material that is already in a pretty fine state of subdivision and thereby save the expense of pulverizing it? Most of the preceding discussion has been from the view-point of practical experience, and I should like to suggest that we have not had enough practical experience in rock dusting to be able to answer this question, from the viewpoint of experience, with the degree of definiteness that Mr. McAuliffe desires. With your permission I will discuss the matter briefly from the theoretical standpoint, because after all the theoretical considerations, insofar as we understand them correctly, will apply to every' case, whereas in coming to a decision on this matter it will be necessary to take into consideration local conditions, which will necessarily vary in every case. Considering the matter in a rather elementary way, the first point is: What makes coal dust explode? The air of this room is full of dust at the present moment, even though we do not notice it. If you were to close up the room for a week, enough dust would settle on the floor so you could write your name on it. Of course, the dust which is in the air of this room is not coal dust but even if it were coal dust and you were to bring an open light in' here we all know that the dust would not explode. The reason for that is that the dust particles are not close enough together for the flame to propagate itself from one to the next. You will probably be able to see in the demonstration in the gallery on Saturday that the coal dust explosion is practically a very rapid burning of the dust. When you light one piece of dust it burns and gives out heat and if there is another piece of dust close enough to it so that the heat from the first piece is able to bring a second piece up to the burning temperature, then the flame will propagate itself through the mixture. But, if the particles are so far apart in the air that the' heat from one particle is dissipated before it reaches an adjoining one, and is therefore not able to bring the second particle up to the burning temperature, then the flame will not propagate. To get a coal dust explosion, it is not sufficient to have coal dust suspended in the air. The coal dust particles must be close enough together in the air so that the heat from one burning particle is able to heat the adjoining particles up to the point at which they will burn. There has been some discussion of the size of dust particles that will cause an explosion to propagate, and it has been pointed out that the minus 200-mesh dust is the most dangerous, and that particles coarser than 20-mesh will not propagate an explosion. There are two reasons for that. A large particle has very much smaller surface in proportion to its weight than a small particle has. You know the old story about the Indian who when asked how he could go around in cold weather without any clothes on said that an Indian was all face. A 200-mesh particle is almost all surface and there= fore is able to burn almost instantaneously, which means that it gives out its heat almost instantaneously, whereas a large particle with less surface in proportion to its weight burns more slowly, gives out its heat more slowly, and the heat has more time to be dissipated. The second reason why coarse dust does not easily propagate a flame is because it is so hard to make it

Jan 1, 1924

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 Jay F. Smith

The Alan Wood BOF Shop consists of two 140 ton furnaces with a rated yearly capacity of 1-1/4 million ingot tons, he hot metal for the BOF Shop is supplied by two 18 foot blast furnaces which produce an average of 2000 tons per day, To achieve the rated ingot ton capacity with this limited supply of hot metal, the preheating of scrap is necessary. This is accomplished in the furnace by using an oxygen-natural gas burner. Piping Arrangement The natural gas and oxygen are piped into the Shop through 8-inch lines to their respective valving stations. As can be seen from Figure 1, the flow of natural gas passes through a flow control valve which is common to both furnaces, after which it divides into two 6-inch lines forming two identical and independent gas control lines, one for each furnace.

Jan 1, 1972

By R. J. Pindred

Discussion by R.J. Pindred In his paper, Davis presents an overview of risk. He also introduces the Capital Asset Processing Model (CAPM) as a foundation for selecting the appropriate discount rate for a mining project. While applying portfolio theory is more defensible than the ad hoc adjustment of discount rates, the CAPM is not a panacea. CAPM shortcomings [The CAPM, as Davis stated, is expressed in the equation: ri=rf+pi4) where ri is the project discount rate rf is the risk free interest rate (3i is the project beta, and 0 is the market risk premium (rm - rf)] Application of the CAPM is more difficult than Davis indicates. Valuation is prospective, while the CAPM parameters are historical. Beta is determined from a regression analysis of historical data, while the beta needed for valuation is the expected beta. Betas are known to be unstable and the regressions that generate them often have low explanatory power. The difficulty of estimating a "project" beta must also be considered. Thus, the beta that is used in the CAPM will be based on the analyst's judgment. Like Cavender's discount rate, this judgment can lead to different project NPVs. Subjectivity in valuation cannot be avoided by a mechanical application of the CAPM. The risk-free rate, which Davis identifies as a short-term real rate of 4%, is also subject to scrutiny. A mining project is not a short-term investment and no single risk-free rate is appropriate for all of the cash flows. The hypothetical mine discussed in Cavender's paper is a six-year project. One might argue for the application of a risk-free rate from the Treasury yield curve at the duration of the project (in a bond-duration sense). This, too, is inappropriate. The risk-free rate should be matched to the timing of the cash flow. These rates can be determined by calculating the implied forward rates from the yield curve using a procedure known as "bootstrapping." It is likely that each of the project's cash flows would be discounted at a different rate. Commodity prices Davis criticizes the "ad hoc adjustment to the discount rate." Yet, in his discussion of the value of stochastic simulation, he suggests that the gold price be modeled as a "random walk, with or without a trend." This is essentially an arbitrary modeling of price risk. Consider that a liquid market in gold futures exists. The futures' price curve, which is closely related to the market's estimate of future spot gold prices, should be used to provide inputs to the model. This is especially true of a relatively short six-year project. Alternatively, as Davis correctly points out, a risk-averse investor can sell the commodity short to hedge price risk. Is it any more correct, in the portfolio sense, to account for price risk at all ?? References Cavender, B., 1992, "Determination of the optimum lifetime of a mining project using discounted cash flow and option pricing techniques," Mining Engineering, Vol. 44, No. 10, pp.1262-1268 Fabozzi, F.J., 1993, Bond Markets, Analysis and Strategies, Second Edition, Prentice Hall, Inc. Higgins, R.C., 1992, Analysis for Financial Management, Third Edition, Richard D. Irwin, Inc. Solnik, B., 1991, International Investments, Second Edition, Addison Wesley Reply by G.A. Davis Pindred discusses two issues related to my paper, the shortcomings of the Capital Asset Pricing Model (CAPM) and which commodity price values to use in the valuation exercise. Even though these topics are not directly related to the use or misuse of Monte Carlo simulation, they are important points to take into consideration in valuation exercises. Since I do not appear to have addressed these issues satisfactorily in my original paper, I will comment on each here. Pindred agrees with me that applying portfolio theory, and specifically the CAPM, to the selection of project discount rates is more defensible than ad hoc methods. But he then points out that the application of the CAPM to project valuation is more difficult that I indicate. It is true that the CAPM is a difficult tool for project valuation in general,. But the application of the CAPM to mining projects is one of the easiest I can think of. The biggest problem with using the CAPM for project valuation is coming up with an expected project beta. I suggest a project beta for gold projects of 0.45. The "true" value might be 0.35, 0.55 or whatever. Pindred correctly notes that the selection of the appropriate project beta is based

Jan 1, 1996

By M. E. Gershon

I would like to congratulate Professor Gershon on an idea that is obviously valuable and is certainly new to me. It has, however, to be further developed, in that one can get fallacious results by limiting oneself to positional weights. In the example problem, the positional weight of one block was 8.6 (the 8.0 value in the paper was incorrect) for 36 units in the cone, for a mean block value of 0.239. The positional weight of the second block was 4.4 for 15 units, for a mean block value of 0.293. Surely one would expect the second block to be rated higher. Additionally, the time value of money adjustment cannot be determined by depth, because the time that a block is mined is determined by the mining sequence, not depth. Figure 1 shows a slightly altered version of Professor Gershon’s pit. The overall value of the pit is the same, as are the total values under each of his blocks. The values in the right-hand cone have been reversed top to bottom, and the values in the rest of the pit adjusted to keep the central cone total unchanged. All values have been multiplied by 10. Figure 2 shows the sequence of mining, at three blocks per year, for Professor Gershon’s pit, and Fig. 3, the sequence for a pit under the other block. Table 1 shows the value and discounted value (157. ) of Professor Gershon’s pit, and Table 2 shows similar values for the second pit. It is apparent that the second pit schedule is more valuable than the first.

Jan 12, 1987

By C. J. Hall

I would like to congratulate Professor Gershon on an idea that is obviously valuable and is certainly new to me. It has, however, to be further developed, in that one can get fallacious results by limiting oneself to positional weights. In the example problem, the positional weight of one block was 8.6 (the 8.0 value in the paper was incorrect) for 36 units in the cone, for a mean block value of 0.239. The positional weight of the second block was 4.4 for 15 units, for a mean block value of 0.293. Surely one would expect the second block to be rated higher. Additionally, the time value of money adjustment cannot be determined by depth, because the time that a block is mined is determined by the mining sequence, not depth. Figure 1 shows a slightly altered version of Professor Gershon's pit. The overall value of the pit is the same, as are the total values under each of his blocks. The values in the right-hand cone have been reversed top to bottom, and the values in the rest of the pit adjusted to keep the central cone total unchanged. All values have been multiplied by 10. Figure 2 shows the sequence of mining, at three blocks per year, for Professor Gershon's pit, and Fig. 3, the sequence for a pit under the other block. Table 1 shows the value and discounted value (15%) of Professor Gershon's pit, and Table 2 shows similar values for the second pit. It is apparent that the second pit schedule is more valuable than the first.

Jan 1, 1988

By E. Tulcanaza

M.S. Azun I do not at all agree with the basic points of the author's conclusion. The use of lognormal or normal model to respond to the attribute distribution function should be carefully questioned. If fitting a distribution function of regionalized variables is the main purpose, one may employ one of certain well-known families of distributions, such as Tukey's lambda functions. For instance, the distribution function of gold assay values studied by Krige (Krige, 1978) may fit the lognormal model well, but one should not exclude that the basic idea of using the distribution function of observed data is to have insight of the physical mechanism that gives rise to the regionalized variables. In Applied Geostatistics, there are many examples considering only lognormal or normal model for the distribution function of geostatistical data even if the observed distribution function has more than one mode. Of course, using lognormal or normal model always brings easy computation for the statistical properties of attribute under investigation. Still, I object to adopting any of those models without having any "statistical inference." As seen from the author's paper, lognormal and normal model produce the same average grade (0.746% copper) and almost the same sample variance. Therefore, which model explains the probabilistic behavior of ore deposit is always an important question that should be replied by the geostatistician using not the common sense but the statistical and probabilistic methods. For kriging procedures, such as linear kriging, one should describe either second order stationary properties or bivariate properties of regionalized variables. Second order properties, such as correlogram function or semivariogram function, are the inputs of the so-called kriging equations. Selecting one of any useable models for the observed second order properties, such as spherical model, is not easy since the models are not based on the correlations structure of the regionalized variables. How those models being used in Applied Geostatistics can be distinguished is another important problem. One may develop many fitting models to respond to the observed second order properties of regionalized variables. However, I suggest that the model should be based on the probabilistic behavior of geologic process. The author used spherical model for copper, wolfram, and silver grades from isotropic sector (Fig. 2, 4, 3b) and exponential model for silver grades obtained along raises in isotropic sector (Fig. 3a). It seems that those semivariograms given in Fig. 3b for isotropic sector and Fig. 4 may be described by the random model implying that regionalized variables do belong to renewal process (Azun, 1983). If I knew the total number of data points used in estimation, I may prove that my conclusion is correct. Recalling the test for the first order sample correlogram value, the test procedure is introduced such that the independence is rejected if [ ] where Za/2 is the a/2 percentile of standard normal random variable and N is the total number of samples used in estimation (Azun, 1983). For the other observed semivariogram functions I suggest the author might try to use the so-called "Markovian model" describing not only the correlation structure of regionalized variables, but also the physical mechanism that produces the regionalized variables. The Markovian model for correlogram function is, [ ] The Markovian model for the other second order properties of ReV are also derived. T ve B, in the above equation, show, for example, structural change and mineralizational variation in a considered deposit, respectively (Azun, 1983). In selecting any model, it is not easy to search for the "best" response to the observed second order properties of ReV's. The Markovian model, based on a theoretical understanding of underlying mechanism, gives more information about the occurrence of regionalized variables and respond to all properties. Random model and the so-called hole effect structure can be easily defined as a special form of the Markovian model (Azun, 1983). An estimation of any second order properties of regionalized variables may be computed through (N-1) lag. However, the dependency between the regionalized variables may be deemed to have "died out" after the so-called stationarity width (range). In practice, the estimation is carried out through 25% of the total number samples available. Therefore, the large fluctuations around the zero level and variance for correlogram or covariogram function and semi-variogram function, respectively, cannot be observed. Since the number of pairs involved in the estimation of higher second order properties is small, the estimation variance at those lags is large. There is no need to show higher order estimated second order properties. After using one of any kriging procedure for block estimation, the distribution function of average grades has the same mean as the drilling sample grades but

Jan 3, 1985

By M. S. Azun

I do not at all agree with the basic points of the author's conclusion. The use of lognormal or normal model to respond to the attribute distribution function should be carefully questioned. If fitting a distribution function of regionalized variables is the main purpose, one may employ one of certain well-known families of distributions, such as Tukey's lambda functions. For instance, the distribution function of gold assay values studied by Krige (Krige, 1978) may fit the lognormal model well, but one should not exclude that the basic idea of using the distribution function of observed data is to have insight of the physical mechanism that gives rise to the regionalized variables. In Applied Geostatistics, there are many examples considering only lognormal or normal model for the distribution function of geostatistical data even if the observed distribution function has more than one mode. Of course, using lognormal or normal model always brings easy computation for the statistical properties of attribute under investigation. Still, I object to adopting any of those models without having any "statistical inference." As seen from the author's paper, lognormal and normal model produce the same average grade (0.746% copper) and almost the same sample variance. Therefore, which model explains the probabilistic behavior of ore deposit is always an important question that should be replied by the geostatistician using not the common sense but the statistical and probabilistic methods. For kriging procedures, such as linear kriging, one should describe either second order stationary properties or bivariate properties of regionalized variables. Second order properties, such as correlogram function or semivariogram function, are the inputs of the so-called kriging equations. Selecting one of any useable models for the observed second order properties, such as spherical model, is not easy since the models are not based on the correlations structure of the regionalized variables. How those models being used in Applied Geostatistics can be distinguished is another important problem. One may develop many fitting models to respond to the observed second order properties of regionalized variables. However, I suggest that the model should be based on the probabilistic behavior of geologic process. The author used spherical model for copper, wolfram, and silver grades from isotropic sector (Fig. 2, 4, 3b) and exponential model for silver grades obtained along raises in isotropic sector (Fig. 3a). It seems that those semivariograms given in Fig. 3b for isotropic sector and Fig. 4 may be described by the random model implying that regionalized variables do belong to renewal process (Azun, 1983). If I knew the total number of data points used in estimation, may prove that my conclusion is correct. Recalling the test for the first order sample correlogram value, the test procedure is introduced such that the independence is rejected if [Ir(1) I > Za/2 N1/2] where Za/2 is the a/2 percentile of standard normal random variable and N is the total number of samples used in estimation (Azun, 1983). For the other observed semivariogram functions I suggest the author might try to use the so-called "Markovian model" describing not only the correlation structure of regionalized variables, but also the physical mechanism that produces the regionalized variables. The Markovian model for correlogram function is, [p (h) = T 1ih , h>1 ,] The Markovian model for the other second order properties of ReV are also derived. [T veß], in the above equation, show, for example, structural change and mineralizational variation in a considered deposit, respectively (Azun, 1983). In selecting any model, it is not easy to search for the "best" response to the observed second order properties of ReV's. The Markovian model, based on a theoretical understanding of underlying mechanism, gives more information about the occurrence of regionalized variables and respond to all properties. Random model and the so-called hole effect structure can be easily defined as a special form of the Markovian model (Azun, 1983). An estimation of any second order properties of regionalized variables may be computed through (N-1) lag. However, the dependency between the regionalized variables may be deemed to have "died out" after the so-called stationarity width (range). In practice, the estimation is carried out through 25% of the total number samples available. Therefore, the large fluctuations around the zero level and variance for correlogram or covariogram function and semivariogram function, respectively, cannot be observed. Since the number of pairs involved in the estimation of higher second order properties is small, the estimation variance at those lags is large. There is no need to show higher order estimated second order properties. After using one of any kriging procedure for block estimation, the distribution function of average grades has the same mean as the drilling sample grades but

Jan 1, 1986

The authors provide a discussion of the paper, ?Coal workers? pneumoconiosis prevalence disparity between australia and the united States,? a peer-reviewed paper that appeared in the technical papers section of the July 2012 issue of Mining Engineering magazine. The original authors, G.J. Joy (gjoy@cdc.gov), J.F. Colinet (jcolinet@cdc.gov) and D.D. Landen (dlanden@cdc.gov), respond.

Jan 1, 2013

By V. Rajaram

R.C. Rex, Jr. V. Rajaram's paper does not accurately reflect the status of Eastern oil shale development as it stands today. Today, there are numerous firms and research organizations pursuing development of the Eastern oil shale resource. Details of the current projects can be found in the symposium proceedings for the 1985 Eastern Oil Shale Symposium held in Lexington, KY, in November 1985. Additionally, the comparison of the Paraho and HYTORT processes is invalid because the HYTORT process design used it not an accurate representation of the HYTORT process. I will briefly address this point. It is disconcerting to see the author reference hydroretorting work conducted at the Institute of Gas Technology (IGT) (circa 1978) in the introduction, which indicates hydroretorting (the HYTORT process concept) yields of up to 250% of the Fischer Assay oil yield, and then turn around and base the remainder of the paper on the 1981 "Synthetic Fuels From Eastern Oil Shale Report," which depicts the HYTORT process as yielding only 115% of Fischer Assay. Indeed, work conducted at IGT to 1981 indicates commercial oil yields for the HYTORT process would be in the range of 200% to 210% of Fischer Assay. A more thorough literature search by the author would have yielded a 1981 publication by Schora et al., entitled "Update on the HYTORT Process" presented at the Symposium on Synthetic Fuels From Oil Shale in Nashville, TN, October 1981. This paper thoroughly discusses the shortcomings of the referenced study with respect to the HYTORT process. For the interested reader, I can also recommend a June 1984 report entitled "An Economic Comparison of Five Process Concepts for Using Eastern Oil Shale," published by Los Alomos National Laboratory (LA-10106-MS, Parkinson et al., NTIS No. DE84-015981). This study applies a realistic HYTORT design (circa 1982) and indicates that HYTORT is the most econom¬ical method of processing Eastern oil shales (about $20/bbl less than Paraho). Additionally, completion of the HYTORT feasibility study in June 1983 by Phillips Petroleum Co., Bechtel Group Inc., HYCRUDE Corp., and IGT has resulted in development of designs that will make the use of HYTORT on Eastern oil shales even more economic. HYCRUDE Corp., a second-tier subsidiary of IGT, is involved in commercialization and licensing of the HYTORT process and is active in efforts to develop the Eastern oil shale resource. As such, I am disappointed to see outdated material that has the potential for adversely affecting development of Eastern oil shale resources published in MINING ENGINEERING. I can only hope that this response will serve to mitigate any negative opinions that may have been formed. Today, activity in commercializing Eastern oil shales dominates the oil shale scene in the US and indicates a realization of the potential for these shales. Reply by V. Rajaram I appreciate the updates on the HYTORT process provided by R.C. Rex, Jr. I hope that Hycrude's work will lead to the funding of a pilot plant to demonstrate that yields of greater than 200% of Fischer Assay can be achieved in a commercial plant using eastern US oil shales.

Jan 9, 1986

By V. Rajaram

R.K. Leininger, N.R. Hasenmueller, and D.D. Carr The content of the paper by V. Rajaram does not fulfill the expectations of the title. But Rajaram submitted the article in October 1983, and he could not have foreseen the numerous developments that would occur before his paper was published more than two years later. Nevertheless, Rajaram failed to mention the interest in oil shale development in southern Indiana beginning in late 1979 and continuing through the present as a result of special financial encouragement of three oil shale projects in May and August 1984 by the Indiana Energy Development Board and the Indiana Corp. for Science and Technology. A session at the 1985 Eastern Oil Shale Symposium in Lexington, KY, Nov. 18-20, 1985, gave the current status of oil shale developments in the eastern United States. Speakers at this session reported on the three Indiana projects. First, Gary D. Aho, Cliffs Engineering Inc., spoke on a feasibility study by Cliffs Engineering and Allis Chalmers for a site-specific pilot plant using the Allis Chalmers process. The plant site is in Clark County, IN, on property of the Midwest Energy Resources Co. The project is funded by $240,908 each from the Indiana Energy Development Board/Corp. for Science and Technology (EDB/CST) and the US Department of Energy (DOE) and $120,454 from the corporate sponsors (total $602,270). Completion is scheduled for mid-1986. Next, Edwin M. Piper, Stone and Webster Engineering Corp., discussed the American Syn-Crude/Indiana Oil Shale Project. This effort followed the completion of two smaller projects funded by the Indiana EDB/ CST: "Testing of Indiana Oil Shale in a Petrosix Pilot Plant" (total funding by EDB/CST at $50,000) and "Assessment of the Petrosix Process for an Indiana Shale Oil Plant" (total funding by EDB/CST at $100,000). The status at the time of the report at the Oil Shale Symposium was that a request for an extension of time for securing nonfederal support had been submitted to the US Synfuels Corp. The proposal included building an 11-m (36-ft) diam retort in southeastern Indiana to process the New Albany Shale and to produce 366 m3 (2300 bbl) of shale oil per day by the PETROSIX process. The Indiana EDB/CST had contracted with Stone and Webster at $401,100 from EDB/CST and $245,835 from Stone and Webster for activities to advance the project in the negotiations with Synfuels Corp. At the time of the report, this project was the only eastern oil shale proposal that was still on the agenda of the Synfuels Corp. As a result of Congressional action in late December 1985, federal support from the Synfuels Corp. is no longer possible. Finally, Victor H. Carr, Eastern Shale Research Corp., described his firm's project, which is jointly supported by DOE ($227,749) and the Indiana EDB/ CST ($73,850) and is entitled "Feasibility Study to Determine Suitability of an In-Situ Process to Recover Hydrocarbons from Eastern Shale." An area in Scott County, IN, had been chosen, but not a specific 9 x 15-m (30 x 50-ft) site. One burn of a small in situ retort is contemplated as part of the project. Besides these three projects, several reports on shale research were presented. Joseph Damukaitis, American Syn-Crude Corp., reported that a pilot plant using the hydrogenation-extraction (H-E) process (described at the 1984 Eastern Oil Shale Symposium) was 94% built and would go through shakedown with oil shale but would then shift mechanical devices to process coal mine waste. Current status of research on the HYTORT process was then presented by Raymond C. Rex, Jr. Oil shale beneficiation research at the University of Alabama/Minerals Research Institute was reported by R. Bruce Tippin. Scott D. Carter discussed continued research on fluidized-bed retort¬ing of shale at the Kentucky Center for Energy Research Laboratory. Carl E. Roosmagi of DOE, Morgantown, reported on the oil shale research at the Morgantown Energy Technology Center (METC) laboratory. Henry J. Gomberg, Ann Arbor Nuclear Inc., discussed "Radiation Combined with Donor Solvents for Extraction and Up-Grading of Kerogen." Aurora M. Rubel and coauthor Eileen Davis presented results of research at the Kentucky Center for Energy Research Laboratory under the title "The Effect of Shale Particle Size on the Products from the Bench Scale Fixed Bed Steam Pyrolysis of Kentucky Sunbury Shale." Lastly, Maria Rockwell, Technical University of Nova Scotia, presented "Processing and Up-Grading of Low Grade Nova Scotia Oil Shale for Potential Use." A review of shale oil prospects by Gerald Parkinson in Chemical Engineering for Feb. 3, 1986, covers both western and eastern projects and includes a report that the US DOE is funding a few relatively small projects; most of the fiscal 1986 budget of $12 million for shale oil is for fundamental research. Projects include a $3.2 million three-year contract with Hycrude Corp. (Chicago) for development of the HYTORT process and a $1.2 million three-year contract with the University of Alabama's Mineral Resources Institute on beneficiation of eastern oil shale by froth flotation. The report incorrectly states that current projects include a total investment of $466 million in a pair of 18-month technical and economic feasibility studies for proposed projects in southern Indiana. The correct figure is $468,657 [$240,908 for the Cliffs Engineering/

Jan 10, 1986

By G. Johnston, P. K. Chatterjee, S. Holguin

M. Gershon The article is an excellent one, providing a basis for the conduct of the major tasks in underground mine planning and scheduling. I object to only one word. Since that word is featured in the title, it is a strong objection. The word used erroneously is optimization. There is no optimization performed in the article. By definition, an optimization is an approach that yields the best of all possible solutions. We can limit this definition to its narrowest sense, the optimization techniques of operations research such as linear programming, or broaden it to include less sophisticated search procedures that find the best mine plan or schedule. No such claim is made in the article. No optimization of any kind is performed. The major tool used is a simulation, which, in truth, is probably more appropriate for this problem than any optimization. But simulation does not optimize. It only allows for quick evaluation of many options, leading to the selection of the best plan "of those tried," not "of all possible plans." This article, of course, is not the first mining paper to make this error. It seems that the word optimization is used more to impress people than to refer to any act of optimizing. It is used in a way that implies "try to optimize"or "come close to optimize," but not to really do it. It is usually appropriate to quietly ignore this error. But when Dr. Chatterjee, who has done so much good work in true optimization projects and whose work I have come to admire, makes such an error, it is time to publicly dispute it. Having discussed this very point with him in the past, I am sure that he will agree. If he can support this position in his response, perhaps people will start to listen and we can reduce the number of false claims of optimization. This should be easy for him in this case since the approach he used, simulation, is the more appropriate one anyway.

Jan 8, 1988

By M. Gershon

The article is an excellent one, providing a basis for the conduct of the major tasks in underground mine planning and scheduling. I object to only one word. Since that word is featured in the title, it is a strong objection. The word used erroneously is optimization. There is no optimization performed in the article. By definition, an optimization is an approach that yields the best of all possible solutions. We can limit this definition to its narrowest sense, the optimization techniques of operations research such as linear programming, or broaden it to include less sophisticated search procedures that find the best mine plan or schedule. No such claim is made in the article. No optimization of any kind is performed. The major tool used is a simulation, which, in truth, is probably more appropriate for this problem than any optimization. But simulation does not optimize. It only allows for quick evaluation of many options, leading to the selection of the best plan "of those tried," not "of all possible plans." This article, of course, is not the first mining paper to make this error. It seems that the word optimization is used more to impress people than to refer to any act of optimizing. It is used in a way that implies "try to optimize" or "come close to optimize," but not to really do it. It is usually appropriate to quietly ignore this error. But when Dr. Chatterjee, who has done so much good work in true optimization projects and whose work I have come to admire, makes such an error, it is time to publicly dispute it. Having discussed this very point with him in the past, I am sure that he will agree. If he can support this position in his response, perhaps people will start to listen and we can reduce the number of false claims of optimization. This should be easy for him in this case since the approach he used, simulation, is the more appropriate one anyway.

Jan 1, 1987