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By Allison

During the 1980’s, laboratories opened throughout the United States to serve the analysis needs of a rapidly growing asbestos abatement industry. The samples analyzed were either collected during the abatement of known asbestos-containing materials or were suspect asbestos-containing construction materials. Many asbestos analysts received minimal training and used analytical protocols that were industry specific. False positives (reporting a sample as containing asbestos when none is present) went unnoticed or had minor repercussions. The mindset of the industry was to err on the conservative side – it would be better to identify a non-asbestos fiber as asbestos than visa versa. With growing public awareness of the health hazards of asbestos, other materials began to be tested for asbestos content. Rock and ores began to be tested for naturally occurring asbestos by these laboratories using the same protocols or an in-house protocol. Analysts were given no additional training and accreditation, in most cases, it was not required. With no consistency in sample collection, testing protocol or expertise in analysis, significant discrepancies in final results can be expected. If we further complicate this scenario with misidentification of naturally occurring look-a-likes for asbestos, expensive false positives are likely. To avoid the repercussions of misidentification of naturally occurring chrysotile asbestos from look-a-like fibers in rock and ores, correct sampling and analysis protocols with more stringent scientific diagnostic criteria are needed.

Jan 1, 2005

By Francis Pitard

"The optimization of metallurgical processes requires an in-depth understanding of the heterogeneity carried by any constituent of interest. It emphasizes that knowledge of the Theory of Sampling (TOS) is the necessary step to ensure that valuable and valid research programs, pilot plants, feasibility studies, plant design and plant operations have been set on solid foundations. Too many publications today are solely based on the properties of the sample, with no mention of how this sample relates to anything in the environment where it was collected. Unfortunately sampling is an error-generating process that leaves no apparent tracks of its shortcomings. Indeed, it is easier to track down analytical errors than sampling errors. Consequently, bad sampling practices plague the scientific community and the industry and are responsible for severe money losses, unsatisfactory science and processing, and sometimes litigation. A point-by-point strategy is presented addressing sampling problems in early stages of a project. Sampling system requirements for metallurgical accounting and process control are listed. The use of Chronostatistics is suggested to determine if sampling systems are performing satisfactorily and to identify the various types of process variability that may not be easily detected by a conventional inspection of available chronological data. This strategy will help engineering firms, manufacturers, managers and supervisors to provide, install, use and maintain key sampling systems located at very important points in a process. Equipped with the appropriate sampling and statistical tools it becomes easier to minimize reconciliation problems with the mine, with suppliers and with clients. Contrary to the beliefs of many people, good sampling systems are not expensive because in the long run they help to reduce hidden cost and better regulate plant productivity; they most certainly minimize the number of unnecessary meetings looking for solutions where there are none. The suggested strategy will help specialists to extract maximum information from the available data, as large investments and crucial decisions depend on it."

Jan 1, 2014

By L. Adler

Determining whether a railroad locomotive will skid under varying conditions is a fairly routine, pervasive problem. However, some of the equations provided are wrong, others are at best murky. All require correction or clarification. Skidding during heavy braking is a crucial safety problem, possibly resulting in loss of control and collision. It can also be an economic detriment, ruining wheels and tracks and, by inhibiting acceleration, causing reduced full-speed times. While skidding is less likely for rubber-tired equipment, a comparable problem exists for tractor-trailers operating on poor surfaces. The primary reference is to the SME Mining Engineering Handbook (pp. 14-11, Eqs. 7 and 8), which is taken as authoritative and normative (Brantner, 1973). Fundamentally, the physical weight of the locomotive can never be negative (-). However, based on this reference it can be negative on adverse grades, during braking (Eq. 7), and on favorable grades, whether accelerating or braking (Eq. 8). Additionally, it should be noted that the term rW should be Rw. To avoid having the locomotive weight turn out negative, "absolute value" brackets must enclose the right-hand expression of both equations. Also, the weight of locomotive can be greater than the right-hand side value introducing the common "greater than or equal to" symbol (>_). While these alterations are easy to make, the use of the algebraic sign associated with each term is still confusing, as

Jan 1, 1991

By M. L. Free

Laboratory scale extraction of metals from heterogeneous materials such as copper ores is often performed using fine material to increase kinetics and reduce sample size requirements and costs. In contrast, commercial extraction is usually accomplished using much larger material sizes than are used in laboratory settings. The large size difference between laboratory testing and commercial application materials makes it difficult to use laboratory test data to accurately predict performance for commercial material. One solution adopted widely in industry is the use of large test columns. Large test columns cost considerably more to set up and run and take much longer periods of time to evaluate than laboratory scale tests with small particles. Consequently, from a cost and data collection efficiency perspective, the development of accurate scale up methods offers significant potential for saving both time and money. This paper provides details for a new approach to correct for scale-up phenomena in heterogeneous material processing.

Jan 1, 2007

By T J. Howard

At BHP Billiton Iron Ore operations, run of mine ore from the several Newman Joint Venture (NJV) mines is railed in 12 kt rakes to Port Hedland. Rakes are processed sequentially, with the lump and fines components from each rake sampled after crushing and screening. The grade of ore from the individual mines varies around consistently different means. Although a discrete sample is attributed to each rake, the sample contains some material from the following and/or the preceding rakes due to dilution by ore from the neighbouring rakes during the dumping, screening and crushing processes. Individual rake grades at the port are therefore biased according to the mine sources of adjacently processed rakes. Dilution does not affect the grade of the overall NJV product or the accuracy of port stockpile grades; however, it does limit the ability to use port rake grades to review the quality performance of individual mines and provide meaningful lump-fines algorithms and port to mine biases for individual mines, information that is useful when scheduling ore for mining. A non-linear regression model has been used to estimate the average dilution from the preceding and following rakes. It has been shown that the lump and fines samples for 12 kt rakes are each diluted within the Port process with about 8.3 per cent of the sample coming from the preceding rake and about 3.6 per cent from the following rake. This can bias the reported rake grade by up to one per cent iron. The results of this analysis have been incorporated into the daily grade scheduling system to provide more accurate individual mine grade data and consequently better decision making.

Jan 1, 2007

By J Gresham, A Turichshev

Ore from Henderson’s 7700 Southwest panel cave is transported between the production and truck haulage levels through a series of orepasses. A typical orepass complex consolidates dump points from four production drifts into two long bored raises feeding into a single truckloading chute. The system typically covers a vertical extent of 200 m and a lateral extent of 90 m. In the process of commissioning several initial orepass complexes, challenges with preferential draw in the long bored raises were observed. Preferential draw occurs when one long orepass mobilises much sooner and/or flows significantly faster than the other orepass. This has negative implications for orepass availability in the production drifts. As future mining at Henderson relies on the construction and use of such orepass systems, the development of appropriate design criteria and practical solutions to existing flow problems was necessary.A series of PFC3D numerical models were developed to assist in identifying the factors influencing preferential draw and possible corrective measures. The models helped to identify two primary factors that caused the preferential effect: the relative elevation of the brow at the bottom of each long bored raise, and the location of the bin used to funnel fragmented rock from both raises into the chute. This paper covers the initial design of the orepass complexes at Henderson, results of the numerical models, mitigation work done to date on existing orepass complexes, and design criteria for future construction.CITATION:Gresham, J and Turichshev, A, 2016. Correcting preferential flow problems in long orepass systems at Henderson Mine, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 549–554 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2016

Redetermination of the Chromium and Nickel Solvuses in the Chromium-Nickel System by C. J. Bechtold and H. C. Vacher, p. 14 The text of pages 16 and 17 is in improper sequence. The correct version will appear in the bound volume.

Jan 1, 1962

By J. Richard Kyle

On page 1617, the author's box was incorrect. It should have read: J. R. Kyle, member SME, is an assistant professor of geology. Dept. of Geological Sciences, The University of Texas at Austin. Austin, TX. On page 1616, the italic line 22, beginning "Murray, R.C. ... " should have been deleted. On the same page, the first two lines (27 and 28) referring to Norris, A.W., 1965 . . . should have been deleted. Column 2 should have preceded column 1.

Jan 1, 1981

By AIME

The following corrections (in italics and bold face) were received too late for the September issue of Transactions: Page 270, Abstract, line 1, spelling should be goethite. Page 270, column 1, authors' box, authors' affiliations should read: L. A. BAKER, formerly Principal Research Scientist, is Pellet Plant Superintendent, Cliffs Western Australian Mining, Cape Lambert, W. A., Australia. C. G. THOMAS is Experimental Officer, Minerals Research Laboratories, C.S.I.R.O., North Ryde, N.S.W., Australia. R. J. CORNELIUS and K. S. LYNCH are General Superintendent, Product and Process Development, and Superintendent, Pellet Plant, respectively, Hamersley Iron Pty. Ltd., Dampier, W. A., Australia. G. J. ARMSTRONG, formerly Chief Metallurgist, Hamersley Iron Pty. Ltd., is Mill Superintendent, Samin Ltd., Burra, S. A., Australia. Page 271, column 2, line 41, should read "highest possible spalling temperature and to adjust the. . :' Page 275, column 1, line 1, should read "(LOI) figures are shown in Table 1 and vary from 2.61..." Page 275, column 1, line 16, spelling should be goethite. Page 276, column 2, line 6, should read "Predicting Grate Speed and Production Rate: The cor-..." Page 276, column 2, line 8, should read "basis of Blends A, B, C, D, and Plant Ore 1, shown in Table..." Page 276, Table 3, heading under "Prediction Method" should read Blend E. Page 277, column 1, line 38, should read "=293 tph of specification pellets (see Table 1 for loss-..." Page 277, column 2, replace caption for Fig. 14 with the following: Fig. 14-Example of calculation of hood temperature profile during downdraft free-water drying, given plant free-water drying time. Page 277, column 2, replace caption for Fig. 15 with the following: Fig. 15-Example of calculation of hood temperature profile during drying, given plant goethite drying time.

Jan 1, 1974

By L. V. Wade

US Bureau of Mines efforts under longwall research programs to develop the capability to predict support requirements for longwall/shortwall support systems are discussed. Ground control studies are being conducted in conjunction with several longwall/shortwall demonstration projects. Geologic, support performance, and strata behavior are being documented on these projects. A hypothesis is presented for the prediction of support requirements.

Jan 1, 1979

By Joseph Cervik, Fred Garcia

Gob holes are routinely used by the mining industry to vent methane from gobs and to prevent it from entering the mine ventilation system. In some cases, flows from gob holes are measured by centering an anemometer on the end of the discharge pipe. These measurements are erroneous and tend to be high by as much as 30%. The Bureau of Mines determined correction factors (method factors) for anemometer measurements taken in this manner. Method factors for 10.2, 15.2, and 20.3 cm (4, 6, and 8 in) ID pipe are 0.68, 0.71, and 0.78, respectively. Comparisons between horizontal and vertical flows indicated no significant change in method factors. Consequently, the method factors for horizontal pipe flows can be applied to vertical upward flows.

Jan 1, 1987

By R. W. Baldwin

On page 309, column 1, the second equation should read: Ma-M1 = dlog_Pa M2-M1 d log P2

Jan 1, 1961

By Donald R. Williamson

Ore estimation at Cyprus Pima mine begins with estimating average copper grades for mine model blocks. Accuracy of the estimates has been checked four times in the last several years by comparing the estimates with arithmetic averages of corresponding blasthole (BH) assays. The data file now contains 3.567 estimated blockgrades and more than 10,000 blasthole assays. These data were compared by straight-line regression analysis at appropriate intervals as the body of data grew. Arithmetic averages of the estimates differed each time from arithmetic averages of the BH averages by nearly the same amount. More significantly, regression curves of the new total sets of data did not change position appreciably showing that copper grade estimates of blocks in all grade ranges from the lowest to the highest grade ores remained in error by nearly the same amount. Other studies revealed that core hole assays of the dominant ore type are biased and that that bias almost equals the error in estimation. Discovery of constancy in error indicated that regression analysis could be a means for correcting estimates of average block copper grades. A correction for estimated block grades was developed. It required determining which regression is appropriate for the particular problem, and a mechanism that is demonstrably sound. This paper describes the problem and the solution: No comparable study has been, found in the literature. Two other applications of regression analysis to metal grade comparisons are presented. Each adds a bit of support to a generalization which was suggested by correction of average block copper grades, namely, when performance of an estimate is to be evaluated, the variable taken to be the yardstick for the performance should be regressed on the estimate.

Jan 1, 1980

By Carmel Bofinger, Maureen E. Hassall, David Cliff, Eric Stemn

e Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

By Carmel Bofinger, Maureen E. Hassall, David Cliff, Eric Stemn

e Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

By E. Yiğit, M. N. Saridede

Correction to: Mining, Metallurgy & Exploration. https://doi.org/10.1007/BF03402385 The original online article has been revised to correct the author names. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The online version of the original article can be found at https://doi.org/ 10.1007/BF03402385 * M. N. Saridede theng@smenet.org 1 Metallurgical and Materials Engineering, Yildiz Technical University, Istanbul,

Dec 23, 2021

By Society of Mining Engineers of AIME

An error has been noticed in one of the figures in the 1974 Jackling Lecture "The Henderson Ore Body-Elements of Discovery, Reflections" by Stewart R. Wallace, published in SME Transactions, Vol. 256, No. 3, September 1974, pp. 216-227. The horizontal scale shown on Fig. 5 of this article is incorrect. The horizontal and vertical scales should be equal, i.e., the horizontal distance shown as 1000 ft should be 2000 ft.

Jan 1, 1976

By SME

"Comparison of Secondary Fe-Mn Oxides in Fracture Coating and Heavy-Mineral Concentrates as Sampling Media, Silver City Mining Region, New Mexico," a technical paper, appeared in the Aug. 1983 issue of the magazine. In Table 1, page 1182, the correct sequence of the first five metals in column 1 (Elements) should read: Zn, Pb, Cu, Fe, and Mn under "Central District" and Zn, Fe, Pb, Cu, and Mn under "Pinos Altos District." The published table shows the sequences transposed under the opposite districts. All other columns and sequences read correctly.

Jan 1, 1984

Jan 1, 1960

This investigation was supported by a grant from the American Iron and Steel Institute and a fellowship given by the Armco Steel Corp. We are indebted to J. F. Butler of the Jones & Laughlin Steel Corp. for a portion of the alloys used in this investigation and to R. L. LeRoy of the Armco Steel Corp. for chemical analyses.

Jan 1, 1962