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Jan 1, 1949

Jan 1, 1953

By A. J. Partos

Table 4, Page 311 Table 4 Experimental Parameters from Straight Line Interpretation of Mohr Failure Envelope [ ]

Jan 1, 1991

Jan 1, 1902

The following figures (Nos. 1-4) and tables (Nos. 1 and 2) were inadvertently omitted from the chapter entitled "Optimum Open-Pit Mine Production Scheduling" by Thys B. Johnson, Section 4, pages 539-562. [ ]

Jan 1, 1969

Jan 1, 1946

Most statistical and geostatistical methods of re-source and reserve estimation work extremely well for Normal (Gaussian) well-behaved information. However, most interesting mineral deposits exhibit far from this idealistic distribution of values. It seems that the more valuable the mineral, the less likely the data are to have a Normal distribution. Values tend to be "positively skewed" with a long tail into the high values. Using averaging techniques, such as inverse distance or ordinary kriging may result in consider-able overestimation of grades and tonnages unless such values are cut or otherwise modified. This paper presents some real case studies where distributions are (a) highly skewed and (b) the product of multiple phases of mineralisation. Practical methods are presented for solving such problems including combinations of indicator and log normal kriging methods. The emphasis of the presentation is on developing operational methods which can be used on project evaluation or grade control in producing mines.

Jan 1, 2000

Jan 1, 1955

The curve of Fig. 3 for pyrite requires modification. There 11% a range of pH values from 6.2 to 7.8 for which no cyanide is required to prevent contact. From 7.8 to 11.3 a small amount of cyanide is required, with a sharp maximum of 20 mg. per liter at pH=9.6. The mistake arose from considering too few solution compositions (17 were considered) in constructing the curve. Subsequent work with other collectors indicates that the corrected curve is typical for such systems; it illustrates the further development of the dip in the pyrite curve of Fig. 5.

Jan 1, 1938

By EDWARD B. DURHAH

The illustration Fig. 12, of the paper of Edward B. Durham, "Electrolytic Refining at the U. S. Mint, San Francisco, Cal.," printed in Bulletin No. 58, October, 1911, p. 830, contained an error. The corrected diagram is printed below.

Nov 1, 1911

By A T. Spathis

Corrections are given for “A three parameter rock fragmentation distribution” by A.T. Spathis (Measurement and Analysis of Blast Fragmentation, Workshop hosted by 10th International Symposium on Rock Fragmentation by Blasting, New Delhi, India, November 25-29, 2012 pp. 73-86). Typographical errors in two equations for the uniformity index in the two versions of the original and modified Kuz-Ram model are corrected. It is noted that the modified equation in the original published paper of Cunningham also had the same typographical error. A corrected plot of the dependence of the uniformity index versus the burden to diameter ratio is given. These corrections do not affect the rest of the original published paper.

Jan 1, 2013

By William C. Martin

"Consolidated Sudbury Basin Mines Limited owns two mines, 4 miles apart, near the southwest end of Sudbury Basin. The Errington mine lies close to the north boundary of Creighton Township and about 18 miles by car from Sudbury. The Vermilion Lake mine is in Fairbank Township, on the south side of Vermilion Lake.The first discovery of base-metal mineralization within Sudbury basin was made by James Stobie in 1897 at Stobie Falls on the Vermilion River, half a mile west of the Errington mine. A few years later Alphonse Oilier made a discovery of massive pyrite with base-metal sulphides near the west end of the present Errington workings.No work of consequence was done on these discoveries until 1924, when Joseph Errington acquired options on a large acreage of ground which was later taken over by the Treadwell Yukon Company Limited. After considerable diamond drilling this company sank No. 1 shaft in 1926. In the next three years, a large amount of underground development was done, a pilot mill was constructed and operated for two years. All operations ceased in 1931 with the fall in metal prices. While in operation the pilot mill treated 186,172 tons of ore the average grade of which was 1·07 per cent copper; 1 10 per cent lead; 4·60 per cent zinc; 0·03 ounces gold; and 1 70 ounces silver.Up to the time of closing in 193 l there had been completed at the mine 26, 790 feet of lateral work and 75,500 feet of surface and underground diamond drilling. The company records, however, showed no figures on ore reserves, except broken ore in stopes amounting to about 60,000 tons. A study of the plans, prior to reopening the mine in 1952, showed 1,450 lineal feet of ore (at 1952 prices) opened up by drifting on the 300-foot and 500- foot levels. It is clear that no comprehensive picture of the structure was had at this time. The orebodies were considered to be very irregular pods scattered through masses of quartzite-carbonate in a sheared zone about 800 feet wide. In some way the ore was believed to be related to the tuff-slate contact zone. With this picture it was impossible to make a reasonable ore estimate or even gauge the tonnage potentialities with any feeling of reliability."

Jan 1, 1949

By Raphaël Mermillod-Blondin, Claude Bazin

"Sampling is an important step for ore body evaluation and daily plant material balances. The Gy’s equation is regularly used to assess the size of the samples to be collected and to estimate the reproducibility of the collected samples. The equation works well for iron and base metal ores but seems to produce pessimistic estimates of the gold content reproducibility for gold ores. A model is proposed to simulate the division of samples using a standard splitter and discrepancies are observed between the Gy’s equation predictions and simulated samples reproducibility for crushed gold ore. Results of the simulation show that sampling a pile of ore crushed to -2 mm should lead to more reproducible gold content than it is observed in practice and predicted by the Gy’s equation.INTRODUCTION With current gold prices above $1400 per troy ounce operators seek for new economical ore bodies. The metallurgical evaluation of an ore body is one of the numerous steps required to justify the investment for the development of a gold mine. Metallurgical testing requires to sample the ore body and to prepare the ore sample for the grinding and separation tests. The preparation implies crushing the ore usually to less than 2mm and its division into batches of a given weight for subsequent processing. For gold ore testing a batch of crushed ore is ground in a laboratory mill and leached using cyanide. Gold recovered in the solution is used as an indicator of the ore processing amenability. One difficulty associated with ore testing is due to the reproducibility of gold content measurements of the feed sample. According to the Gy’s equation (Gy, 1979; Morisson, 2009) the division of a 20 kg batch of -2 mm ore containing 5 g/t of gold into batches of 1 kg may lead to batches containing 5 ± 5 g/t of gold. For this reason the gold content of a feed sample is usually back calculated from the assays of the solution and solids residues of the leaching process. However, these two assays may contain measurement errors that can propagate through the calculation of the gold recovery. The use of the feed assays as a redundant information could add robustness in the evaluation of ore amenability to processing. This paper discusses the problem of sampling reproducibility for a gold ore. The analysis is carried out with reference to the Gy’s equation for the sampling of particular materials."

Jan 1, 2012

By J. P. Harrison, R. Roostaei

"In this paper we investigate the consequences of using conventional triaxial peak strength criteria in the polyaxial stress conditions that occur around the end of a long circular underground opening. A series of 3d boundary element analyses were performed using Examine3D to determine the stresses induced around the tunnel in different in-situ stress conditions. To discuss the applicability of a triaxial strength criterion in failure prediction, strength factors were computed for both the Ottosen polyaxial strength criterion and the triaxial Hoek-Brown criterion. Assuming that the Ottosen criterion is an accurate indicator of strength factor, the discrepancy from this value due to the Hoek-Brown criterion is the error in prediction.In order to simultaneously show the error in prediction and actual strength factor, a quantitative bivariate colour scheme is used to visualize the results. This allows rapid identification of regions with large error, and where either unsafe or overly conservative conditions occur. We show how the inappropriate use of triaxial criteria rather than polyaxial criteria leads to an under- or over-estimation of strength factor, and can result in uneconomic or unsafe design. Extending the analysis to different in situ stress regimes shows how the in situ stress conditions affect the assessment."

Jan 1, 2015

Abstract - A method to extract certain types of error variance information from paired data is presented, which constitutes an important tool for the calibration of Gy’s formula for variance predictions in the sampling of broken ores. The research summarized here complements publications by others on geochemical precision. The model and method proposed for treating duplicate data provide a practical means of deriving separate estimates of the purely analytical and aliquot sampling variances, from a unique set of duplicate assays, and, hence, proposes a new avenue for estimating effective mineral grain size.

Jan 1, 1998

By Rudolf E. Greuer, Linneas W. Laage, Xinton Chang

The usual run time errors occur when illegal mathematical operations are attempted by MFIRE For exam¬ple, if the user enters a negative value for the input variable HEAT to model a heat sink or cooling station, a run tame error will occur because an exponential function is employed to better model the changing temperature dis¬tribution in the airways. DATA FILE ERRORS Two types of data file errors can occur. 1 An error may occur because the input data file itself is invalid This may be caused by: The data file cannot be found due to incorrect spelling, drive, or path specification Check the spelling and path at the prompt for location of the data file. The data file contains non-ASCII symbols Check op¬eration of the text processor to insure that only ASCII characters are produced Delete all non-ASCII and/or all out of place characters. 2 Data are accepted by the program, but are incorrect for the data types or ranges or the combination causes a data inconsistency This may be due to. An invalid character is located where a numerical value is required The input data value is outside the reasonable range The input data is insufficient to build the model for simulation. The input data exceeds the capacity of the program. PROGRAM ERROR DETECTION When the program is running, tracing messages are given on the screen indicating the group and lines of data that have been read and the section of the program that is executing Program termination indicates that problems are caused by the subsequent (not read) data file section. For each detected error, descriptive message starting with * ERROR * is issued and the program terminates. When a possibly wrong value and/or its calculated intermediate result is detected, a descriptive message starting with * WARNING * is issued and the program continues. If a modification of input value or calculated intermediate result is automatically made by the program, descrip¬tive message starting with * ATTENTION * is issued and the program continues. LOCATING ERRORS If a message indicates that the error condition occurred due to the data input file, locate and correct the data item. If no error message is issued, but the simulation results are not as expected, carefully check the list of input data shown in the output file WARNING messages and ATTENTION messages may reveal an error in the input data even though the program continues to operate Insure that the default values or modifications adopted by the program run are acceptable. Anomalistic data, though not incorrect or impossible, may lead to an unusual simulation For example, making a fan unusually powerful may prevent a normal strength fire from affecting the airflow distribution in the system Much care must be employed before considering an ex¬treme case. If a message indicates that the accuracy criterion in the temperature section of the program was not satisfied, a table detailing the current parameters of airways which have large fluctuation in the average air temperature from iteration to iteration is produced If the pattern of the parameters is clearly cyclical, while the variation of natural ventilation pressure is stable or cyclical from iteration to iteration, then, most likely there is no single equilibrium stage for the event If the variation of natural ventilation pressure decreases steadily though slowly, increasing the value of data item variables MADJ or ITN (on control line number 1) may allow convergence.

Jan 1, 1990

By Simon C. Dominy, Alwyn E. Annels, Mark A. Noppé

Abstract - Mineral Resources and their subsequent conversion to Ore Reserves are of key importance to mining companies. Their reliable estimation is critical to both the confidence in a feasibility study, and also to the day-to-day operation of a mine. Together with sampling, assaying, geological and other errors introduced during interpretation and estimation, additional errors are likely to be introduced during the application of technical and economic parameters used for conversion of resources to reserves. There is thus a requirement for high-quality interpretation and estimation to be supported by high-quality data. Any company expecting to make sound investment or operational decisions must base this on both relevant and reliable information. An Ore Reserve statement generally contains a single set of grade and tonnage figures without a discussion of the potential inherent errors in these estimates. Some sensitivity studies may be run, but confidence limits are rarely quoted and, if they are, often do not take into account many of the factors that cause uncertainty in the grade and tonnage estimates. Mineral Resource and Ore Reserve estimates thus carry certain errors leading to uncertainty and risk; some of these are unquantifiable for various reasons, for which the operator should be aware. This paper presents a review of the potential sources of error that might occur during a resource estimation program, which are carried through into the reserve estimate. A number of methods are discussed that allow the estimator to be more transparent about the inherent risks in his/her estimate. Emphasis is placed on data quality, and the requirement for strong quality management to be linked to continuous improvement.

Jan 1, 2002

By A E. Annels, M NoppT

Ore Reserves are key mining company assets and their reliable estimation is crucial for both feasibility studies and the day to day operation of a mine. The reserve is based on the Mineral Resource after the application of certain technical and economic parameters. Engineering aspects of reserve estimation can be accurately determined to ¦10 per cent, however the majority of project risk will revolve around the resource. A final Ore Reserve generally contains a set of figures quoted without reference to its potential errors. Rarely are overall confidence limits quoted and, if they are, they often do not take into account many of the factors that cause uncertainty in the grade and tonnage estimates. There is thus an unquantifiable risk, which the operator should be aware of. This paper presents a review of the possible sources of error that might occur during the various phases of an exploration and estimation programme which are carried through into the Ore Reserve and hence mine design.

Jan 1, 2002