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By H. H. Wober, P. J. Morgan

"The proposed classification of ore reserves based on geostatistical and economic parameters is designed to be used as a communication tool to identify for investors, bankers, and financial analysts the degree of risk associated with a specific class of ore reserves. The class designation identifies the degree of accuracy of the estimates for tonnage and grade of annual production volumes of ore. It thus enables decision makers to quantify the risks and uncertainties inherent to the ore reserve in achieving over-all economic goals and annual financial objectives.IntroductionDuring the evaluation, in 1980, of the Creede silver deposit in Colorado, U.S.A., as a potential bulk mining project, the authors were faced with the problem of conveying to senior management the risks and uncertainties that are implied by using common or conventional classification systems to categorize ""ore reserves"". The estimated numbers for tonnages and grade of the projected ore reserves represented the only entry for future revenue from the project. It was, therefore, necessary to convey to management the level of risk associated with those numbers when implementing further stages of development, and to convey what impact significant differences would have on the profitability of the project. The desire to quantify these risks led to a revised definition of ""ore"" and to a re-examination of the classification system of ""ore reserves"". Traditional methods for estimating tonnage and grade of reserves do not include a quantification of the errors of these estimates. The advent of geostatistical methods made this quantification possible. The proper application of geostatistical methods, with adequate geological input and a reliable set of sample data, is essential to its validity.Others have proposed a classification of ore reserves based on geostatistical parameters. Diehl and David (1982) recognized ""that concepts of proven, probable and possible have to be linked to the production rate or time horizon"". Royle (1977) had also addressed this problem. Forsyth (1987) pointed out that bankers are concerned with ""the probability that the variability will fall within a certain degree of accuracy"" especially for grade estimates, and would like to see grades expressed together with the error of the estimate at the 950Jo confidence level."

Jan 1, 1993

By F. W. Wellmer

"I would like to comment on the classification scheme of ore reserves based on geestatistical and economic parameters as proposed by Wober and Morgan in the CIM Bulletin, January 1993.In principle, a classification system that combines quantitative data for levels of confidence and errors with economic expectations must be welcomed. I would like to point out, however, two aspects of the proposed classification system which might create ambiguities, and therefore problems, by working with two levels of confidence and two kinds of block sizes. A normal classification scheme reflects the information density, the highest reserve category such as class A, or proven, or measured reserves having the highest density. This is not necessarily the case in the scheme of Wober and Morgan (1993), unless the definitions are amended.I. Assuming, as the authors do, that the estimation errors show an approximately normal clistribution, at the 95% confidence level, the standard deviation has to be multiplied by about 2 for the error and, at the 84% confidence level by about 104. Reserves (or resources) with the same or even higher information density than class A reserves, which at the 95% confidence level would not fulfill the required economic criteria at the threshold level, i.e. not qualify as ""ore"" at all, could be retained ""on paper"" to class B reserves by just multiplying the standard deviation by the factor 104 instead of 2, thereby narrowing the error.This statistical ambiguity was partly inherent in the old German classification scheme which used varying confidence levels and errors (GDMB 1956; Jahns 1959). In the new German scheme, which was later adopted in Austria in a moclified version, the level of confidence was therefore fixed at 90070 for all reserve categories (Wellmer 1983; ONORM 1989)."

Jan 1, 1993

By P. Diehl, M. David

"As shortage in the supply of mineral commodities become more probable, there is an increasing need for reliable and comparable reserve/resource data. It is therefore necessary to standardize the traditional classification procedure by quantifying the three basic criteria in resource evaluation: economic feasibility, geologic assurance and recovery. For the portion of fairly well documented deposits and as far as their geologic parameters are concerned, geostatistical methods can considerably improve the quality of ore classification. In respect of economic feasibility and recovery, approved geostatistical methods can be introduced directly into the classification procedure. The concept of geologic assurance, however, requires a new interpretation in terms of geostatistical error spread. As the ""risk"" in estimating an ore parameter is also a function of the respective block size, it is reasonable to define not only upper limits for the risk intervals for each class of uncertainty, but also block dimensions which reflect the typical stage of planning associated with the different ore categories. An algorithm has been developed to block out areas inside a mine or a large panel which satisfies the constraints of an ore category in respect of the risk intervals of a parameter and in respect of their size. The method has been computerized and is demonstrated on a massive copper-nickel deposit.IntroductionFacing an uncertain future with respect to the demand and supply of mineral commodities, both governmental authorities and the mining industries show an increasing interest in a useful framework for the assessment and classification of the remaining mineral resources"". This is reflected by the recently published appraisal and classification scheme of the United Nations as well as by the new or revised classification systems of the USA; Canada or Australia. Moreover there are various classification schemes which are currently discussed in major mining companies."

Jan 1, 1982

Paper written on project work carried out in partial fulfilment of M.Sc. (Chemical Engineering)degree Identification of non-linear systems can be a daunting task and in the process industries the problem is complicated considerably by the presence of noise from various sources, non-stationarity of the data and intermittence, such as observed in particulate flows. Traditional methods, such as frequency analysis and linear model-ling do not handle these systems well. Similar problems arise with the application of non-linear theory developed over the last few decades, where much of the analysis depends on embedding of the data in a phase space or pseudophase space, since these methods were not originally designed to deal with noisy systems. In contrast, singular spectrum analysis is a method which can be used to identify structure and distinguish noise from important information in multivariate data, such as the trajectory matrices of time series measurements. It provides an orthogonal basis onto which the data can be transformed, so that components of the time series can be investigated individually. With Monte Carlo methods, the components can be compared with those from linear surrogate data in order to identify possibly non-linearities in the data. In this paper, this versatile technique is briefly explained and illustrated by means of case studies.

Jan 1, 2003

By S. J. Haynes

"This paper documents the characteristics of the different quartz veins, and their relation to gold, in the turbiditic Meguma domain of eastern Nova Scotia. Eleven different types have been distinguished in the greenschist facies on the basis of form, internal structure, mineralogy, wallrock alteration and crosscutting relationships to each other and to regional structures. Gold is present as rare visible disseminations in host rock slates and in ore-grade, early-formed veins, that pre-date the formation of upright Acadian folds and intrusions of Devonian granitoids. Four vein types pre-date Acadian folding. Five gold-deficient vein types are associated with Acadian folding (syndeformation); whereas two gold-deficient vein types clearly post-date this deformational event: Previously unreported, wall rock alteration is associated with early crosscutting and semiconcordant auriferous veins. In contrast, early stratiform veins are devoid of wallrock alteration. It is suggested that the auriferous vein arrays formed as submarine exhalitive systems over synsedimentary tensional faults and that the Meguma domain may represent the opening of a basin similar to the modernGuaymas rift .IntroductionThe term ""turbidite-hosted gold deposits"" is introduced to distinguish gold deposits contained within a turbiditic sequence of wackes and shales (or their metamorphosed equivalents) from gold deposits in other types of sediments. Usage and justification for the term is similar to ""carbonate-hosted lead-zinc deposits"" or ""volcanogenic massive sulphides"". A range of different characteristics can be present in individual deposits, some of which may be indicative of specific deposit types in the same manner that volcanogenic massive sulphides can be subdivided into ""Cyprus type"", ""Kuroko type"", ""Besshi type"", etc. Historically, gold deposits hosted by turbiditic flysch were an important class of quartz-vein gold mines in the 19th century. They included the goldfields of Victoria, Australia, Otago, New Zealand and Nova Scotia, Canada. Production from most of these fields had ceased by World War II and interest in this type of deposit waned. However, the recent exploration interest in gold has resulted in both re-assessment of the classical mining camps and the discovery that ""turbidite-hosted"" gold deposits are present in many parts of the world.Mapping of the regional geology and gold deposits of the Megumadomain of Nova Scotia was carried out by E.R. Faribault of the Geological Survey of Canada between 1886 and 1939. Faribault produced a comprehensive series of maps and reports for each gold district, a compendium of which was published by Malcolm (1929)."

Jan 1, 1987

By L. A. Readdy

Defining classes of ore using geostatistical methods that match historical classifications has presented problems in the past. A simplistic and practical approach to this problem is presented. An example of the approach is presented using a porphyry type system as a case study. The technique described uses area of drill hole influence and known conditions from similar deposits, or the production history of the deposit being studied. It provides a practical bridging mechanism for defining the class of ore on a block-by-block basis using .the error of estimation value developed during kreiging. The approach provides a filtering method that refocuses attention on the adequacy of drill hole spacing.

Jan 1, 1985

By Nick Barton

The ability to estimate that support is unnecessary for various dimensions of opening in certain rock mass environments is of particular relevance in the mining industry, where most excavations are not expected to remain open indefinitely. As Hoek1 observes: "the basic aim of any underground excavation design should be to utilize the rock itself as the principal structural material, creating as little disturbance as possible during the excavation process and adding as little as possible in the way of concrete or steel support". Approximately two hundred case records have been analysed for the purpose of determining the amount of permanent support normally used when a given span is excavated in known rock mass conditions Some thirty cases concerned unsupported openings. Excavations that can stand unsupported appear to have certain common characteristics as regards rock mass quality. However, the mechanical properties of the rock mass are not the only factors controlling the choice of design. The dimension of the opening (span or height) and the type of excavation are two important variables.

Jan 1, 1975

By Antoni Kidybinski

Mechanical properties of the nether roof rocks play a substantial role in maintenance of mine openings. On longwall faces too low strength is a cause of rock- falls which bring about delays in face advance, and hazard to the miners, while too high strength may result in over hanging of roof rocks in gobs, sometimes over a large area, initiating dynamic periodical pressures. Hence, efforts have been undertaken to develop methods of caveability evaluation for proper selection of roof control system and of longwall-face supports. DESCRIPTIVE CLASSIFICATIONS One of the basic qualitative criteria for classification of roof types with different caveability is the criterion of geological structure. The 26-stages scale worked out for conditions of Donetz Coalfield. USSR, by E.T. Proyavkin et al.(l) may be an example of that type of classification. This scale is presented graphically in Fig. 1. The first types of roofs according to this scale are easily caving immediately after exposure of a small surface, while the last be- long to the strongest roofs which over-hang in waste and require artificial fracturing. Observations and measurements carried out also in Donetz mines lead W.T. Davidyanc (2) to following classification: Class I - weak and densely bedded rocks very easily caving after each advance of supports. The roof subsidence over a waste edge is: [ ] where: h is roof subsidence, m M is thickness of the seam being mined, m S is width of an opening, m Class II - medium caveability and small main roof movements [ ] Class III - hardly breakable rocks, thicker beds with higher strength: caving by large blocks. After exposure of larger roof surfaces the movements of the main roof strata may affect an opening. [ ] Class IV - very hardly breakable rocks - thick, monolithic sandstones: they cause considerable difficulties in working with caving. [ ] Class V - thin-bedded rocks featured by the capacity of a smooth subsidence without disintegration into loose blocks with keeping contact between the particular blocks in a bed. In this classification mining with full roof caving is recommended for class I-III and only exceptionally for class IV. In many coal fields, qualitative and quantitative observations of roof rocks behavior have been carried out: as a result relations and characteristics found in certain mines are given. As an example, Kizielov Coalfield data in the USSR given by B.S. Vinokur (3) may be mentioned. In working thin pitching seams man- less system of undercutting the seam by means of rope saw is used there. The author separated four classes of roofs and gave, for every class, the empirical relation between the extracted area and the time of roof stability. The size of the stable area of exposed roof for particular classes is as follows: 120 to 150 m2 (1291 to 1614 ft2 ), 170 to 200 m2 (1829 to 2152 ft2), 250 to 280 m2 (2690 to 3013 ft2 ) and 350 to 400 m2 (3766 to 4300 ft ). For Kuznetz coalfield in the USSR the following classification of roofs was developed at the WIMI Institute in Leningrad: Class I - completely unstable: impact strength in- dex of rock f = up to 1.5 - the strip of roof 1.4 to 2.0 m (4.59 to 6.56 it) wide, exposed after shearer's cut holds stable during the time not longer than 5 min. Class II - low stability: f = 1.5 to 2.5 - the strip of roof 1.4 to 2.0 m (4.59 to 6.56 ft) wide and up to 4.0 m (13 it) long, exposed after shearer's cut holds stable during 5 to 20 min. Class III - medium stability: f = 2.51 to 3.5 - the strip of roof 1.4 to 2.0 m (4.59 to 6.56 it) wide and 4.0 to 25 m (13 to 82 ft) long, exposed after shearer's cut holds stable from 20 min. to 2 hours. Class IV - stable: f = 3.51 to 4.5 - the strip of roof 1.4 to 2.0 m (4.59 to 6.56 ft) wide and 25 m (82 it) long exposed after shearer's cut is stable during the time longer than 2 hours. Class V - entirely stable: f = over 4.5

Jan 1, 1982

By N. B. Aughenbaugh, W. C. Patrick

The availability of a thorough, simple-to-use roof fall classification system is desirable for several reasons. First, such a system would expedite the reporting of falls for the purposes of estimating material removal costs, analyzing damage levels, and complying with federal regulations to report falls in certain areas of the mine. Second, the classification system would allow the mining engineer to record falls routinely with little time and effort expended. This would encourage him to make observations concerning what type of roof support would work best in a given situation. Third, and probably most important, an

Jan 3, 1979

By W. C. Patrick

Introduction The availability of a thorough, simple-to-use roof fall classification system is desirable for several reasons. First, such a system would expedite the reporting of falls for the purposes of estimating material removal costs, analyzing damage levels, and complying with federal regulations to report falls in certain areas of the mine. Second, the classification system would allow the mining engineer to record falls routinely with little time and effort expended. This would encourage him to make observations concerning what type of roof support would work best in a given situation. Third, and probably most important, an adequate roof fall classification system is the first step toward developing a means of predicting the occurrence and extent of future falls. It was with these three reasons in mind that the following classification system was developed. The type of classification chosen reflects the background of the developer and the intended purposes of the system. Hylbert has proposed a geologic-based classification which groups falls as to structural and compositional origin. The need for similar systems is noted by Cox and by Ferm and Melton. Each of these researchers is confronted with a situation requiring a relationship between local geology and roof fall occurrence. Such a relationship would allow the prediction of the relative frequency of falls from a knowledge of local geologic features. The current research is aimed at fulfilling somewhat different requirements, as noted above. Fall geometry has been chosen as the basis of the classification system so fall type can be reported independent of local geological, environmental, and tectonic conditions. This approach parallels that used in the common slope failure classification with groups failures relatively independent of

Jan 1, 1977

By N. B. Aughenbaugh, W. C. Patrick

The availability of a thorough, simple-to-use roof fall classification system is desirable for several reasons. First, such a system would expedite the reporting of falls for the purposes of estimating material removal costs, analyzing damage levels, and complying with federal regulations to report falls in certain areas of the mine. Second, the classification system would allow the mining engineer to record falls routinely with little time and effort expended. This would encourage him to make observations concerning what type of roof support would work best in a given situation. Third, and probably most important, an

Jan 1, 1980

By G. Song

"SynopsisAccording to the overlying rock strength and thickness of 10 shallow coal seams in China, the strata are classified into four types: weak and laminated strata (hereinafter referred to as WL), weak and massive strata (WM), strong and laminated strata (SL), and strong and massive strata (SM). The four types of strata, together with longwall panel height (M), panel length (L), and seam depth (H) are factors that influence the loads on hydraulic power supports. Principal component analysis was used to study the seven influencing factors. It turns out that seam depth (H) is multicollinear with WL, WM, SL, and SM. Thus the enumerated seven factors are divided into two models. In Model 1, six factors; panel height, panel length, and layer thickness of the four type strata are involved. In Model 2, three factors; panel height, panel length, and seam depth are included. Using multivariate regression analysis, two equations to calculate loads on supports are obtained for each model. The equations were applied to shallow coal seams. The equation of Model 1 is more accurate than that of Model 2, but Model 2 could be further used in deep coal seams. The equations were coded through VB programming to simplify the calculation.IntroductionTo ensure coal production in the long term, the focus of coal mining has been gradually shifted to the western areas of China in recent years due to rich coal resources in this region. Most of the coal seams in this area are shallow but the support loads are abnormally high. In these circumstances, the chock loads estimating methods used in the past do not work. If the rated working resistance of a support is larger than needed, the shield is unable to fully perform, while a lower working resistance would lead to accidents like shield crushing or even casualties. This problem is frequently found in shallow coal seams such as Shenfu-Dongsheng, Yulin, Tuha, Lingwu etc. (Zhang, 2011). Thus, to ensure safety and efficiency during mining, the estimation of support loads and selection of supports are of great significance."

Jan 1, 2015

By A. J. Rergis

During the past ten years a number of papers have appeared describing zeolite occurrences in the western United States in Mesozoic sedimentary rocks, altered bedded Tertiary pyroclastics, playa lakes, hot springs, and amygdaloidal basalts. With the emergence of natural zeolites as economic industrial minerals it appears, useful to develop a system of classification to delineate those zeolite deposits of economic interest which were not discovered until the middle 1950's. The first attempt at a classification of naturally occurring zeolites was made in 1914 by Lindgren l when he classified the zeolites into eleven groups based on their mode of occurrence. The zoned zeolites in the Mesozoic rocks of New Zealand and elsewhere were classified according to the mineral assemblages by Fyfe, et al.2 in 1958 as a zeolite facies corresponding to low grade regional metamorphism. In 1966, Hay suggested as an alternative to the preceding classification one which would "classify as diagenetic all mineral assemblages produced in sedimentary rocks under conditions in which zeolites are formed." In this paper a classification is proposed with the objective of providing an overall perspective of the zeolite occurrences in the western United States.

Jan 1, 1967

By P. A. Rossouw, G. A. Fourie

Several attempts have been made over the latter half of the twentieth century to devise a system for the classification of underground mining methods. None of these are found to be totally successful. A revised classification is suggested with three main categories being identified by reference to fill requirements, steep stopes and stratified geological environments. Methods always needing lasting mass or structure fill include methods with timber-structured stopes and those with mass-filled (or cut-and-fill) stopes. Methods associated with steep stopes are subdivided into steep-stope-specific caving and steep-stope-specific non-caving (or self-supporting) methods. Methods not in the first two categories, often used in stratified geological situations, are not necessarily restricted to stratified environments and include caving as well as non-caving varieties. This category is further divided into room-and-pillar mining, longwall and shortwall coal mining and the type of open stoping found in gold or platinum reefs comprising overhand, underhand and breast-stoping variations

Dec 1, 1996

By P. A. Rossouw, G. A. Fourie

The correction to the paper published in Trans. IMM A, vol.105, 1996, p.A162-A165 stresses that figs. 4 and 5 had been interchanged, reproduces them with their correct captions for clarity and encourages readers to comment on the Delft proposal

Jun 19, 1905

By Bennett Bates

NOWHERE in the world has cone classification in closed-circuit grinding with tube mills reached as high a state of development as on the Witwatersrand. In the development of the Far East section the necessity of fine grinding to obtain. satisfactory residues gave rise to a situation with which the cone could cope only at a great sacrifice in efficiency of tube-mill grinding. H., A. White1 recognized the limitation of the cone in his study of the theory of tube milling as early as 1915, but it was not until Ewing, Davis and. Willey2 carried out their tests at Springs Mines during 1920 that the advantages of mechanical classifiers' were demonstrated. During the fall of 1924 the writer supervised a series of tests with Dorr classifiers undertaken by Mr. White, Consulting Metallurgist, at the mill of the Geduld Proprietary Mines, Ltd., Dersley, Transvaal (Far East Rand). The importance of classification to obtain efficient tube-mill grinding was thoroughly demonstrated. The installation of classifiers of the above type that followed the test, confirmed the results of the experimental work.

Jan 2, 1926

By R E. McIvor, K M. Bartholomew

Classification system efficiency (CSE) is a simple, powerful metric that provides a comprehensive measure of classification system performance. CSE directly quantifies the amount of coarse solids in the mill, and as a result, also directly quantifies the percentage of mill power being used effectively. In this manner, it is an effective, practical grinding energy efficiency metric.Three independent variables drive CSE: circulating load ratio, sharpness of classification, and classifier bypass. Due to practical downstream constrains on per cent solids, improvements realised by increasing circulating load reach diminishing returns as sharpness of classification and bypass deteriorate in the most commonly used classification device, the hydrocyclone. This combination of effects leads to the use of CSE, and its relationship to operational economics, as an objective circuit optimisation criterion.Its simplicity in measurement makes CSE a useful tool for the plant metallurgist, and it is not subject to a great amount of experimental error. CSE provides the plant operator a fast, simple, and effective means to evaluate the performance of the plant classification system. Improvements in CSE relate directly to circuit throughput and energy efficiency. This paper, through description and plant case studies demonstrates the power and utility of this emerging grinding system optimisation metric.CITATION:Bartholomew, K M and McIvor, R E, 2013. Classification system efficiency - a comprehensive closed-circuit grinding optimisation metric, in Proceedings World Gold 2013 , pp 389-394 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 26, 2013

By Rob Sloan, Simon Yu, Rob Robison, Mark Schmidt

"In 2000-2009, Highland Valley Copper (HVC) tested new generation 660 mm cyclones supplied by various manufacturers in an aim to replace existing first generation 762 mm cyclones without success. Due to plant infrastructure limitations, new generation larger (838 and 800 mm) cyclones were not feasible. In 2010, an approach of testing hybrid 800 mm diameter cyclones was initiated by HVC. The hybrid cyclone consists of an 800 mm cyclone upper head section and the use of existing 762 mm cyclone conical sections. Three hybrid 800 mm cyclones were engineered and fabricated, targeting a throughput of 850-950 mtph and were installed in the E line cyclone cluster. Since starting operation on September 20, 2011, three hybrid 800 mm cyclones have been running at combined throughput ranges of 750 to 1050 mtph. Based on plant Daily Met Reports, the test hybrid cyclones demonstrated a decrease in misplacement of coarse copper bearing material to the cyclone overflow and better cyclone efficiency when compared to the existing 762 mm cyclones at the same throughput. The hybrid 800 mm cyclones also demonstrated significantly higher capacity per unit when compared to the existing cyclones. Based on the improvement demonstrated in the full plant trials, HVC decided to install the new hybrid 800 mm cyclones in all five grinding lines starting in July, 2012. A more detailed discussion of the full plant trials and initial results of the first complete retrofit of E line will be presented. INTRODUCTIONHighland Valley Copper Partnership (HVC) is Canada’s largest base metals mine and one of the world’s largest producers of Cu-in-concentrate. The mine, located in the central interior of British Columbia, is owned (97.5% interest) and operated by Teck Resources Ltd. In 2011, HVC produced about 210 million lbs. of Cu and 8 million lbs. of Mo. HVC’s Mill Modernization Project (MOP), which was budgeted for $475 M, is currently under intensive construction and is scheduled for commissioning in Q1 2014. The following description was valid at the time of writing this paper.The operation has been expanded a number of times since the startup of the original Lornex mill in 1972. The plant started up with two 9.75 m (32 foot) diameter primary SAG mills. It was expanded in 1981 with the addition of a 10.36 m (34 foot) diameter SAG mill. In 1988, the Highmont concentrator grinding mills and flotation section were moved and attached to the Lornex mill site. This added two10.36 m (34 foot) diameter AG primary mills to the Highland mill. This brought the total number of primary mills to five referred to as Line A, B, C, D and E: three SAG mills (A, B and C) each with two ball mills in parallel and two AG mills (D and E) each with one ball mill. Each AG mill line is equipped with one short head pebble crusher. Ball mill circuits are classified using 8 clusters of ten Linatex 762 mm cyclones to reach a target P80 330 micron (equivalent 43% +100 mesh), before the cyclone overflow product advances to the bulk flotation circuit."

Jan 1, 2013

By B. Fitch, E. J. Roberts

Definitions and Terminology Classification is defined within this section as the separation of solid particles into size or weight fractions or classes by differential settling through a fluid. It is usually thought of as a size separation, but is actually made on the basis of particle settling rate, and so is affected by any variations in particle density and shape. The fluid may be liquid (wet classification) or gas (dry or air classification). This section will treat only wet classification. There are two basic types of classification; pool and hydraulic. In pool classification a suspension of feed particles is fed into and out of a pool of some kind at such a rate that only part of them¬ the coarser and faster-settling part-has time to settle out. The remain¬der overflows as fines. In hydraulic classification the feed suspension is introduced into or above one or more columns or pockets through which water is rising at a controlled velocity. Coarse particles subside through the pockets and are removed through spigots. Those which settle slower than the rising velocity in any column or pocket are (at least ideally) prevented from subsiding and so must overflow. Hydraulic classifiers or sizers are capable of making far sharper sepa¬rations than pool classifiers. The force causing settling may be gravity, or alternatively may be from centrifugal acceleration as developed in a centrifuge or hydra cyclone. Two things are needed to characterize the separation produced by classification: first, the size, D„ or equivalently the screen mesh at which it is made; second the sharpness of separation. Separation size may be defined in various ways. In closed-circuit grinding it is commonly specified by some point in the overflow screen analysis: For example, 5% on 65 mesh. In other applications, notably

Jan 1, 1985

By Radford B. Langston, John A. Marjerison, Graham C. Howell, Hendrik A. D. Kirsten

Probabilistic design is gaining wider acceptance in the rock engineering community since it allows more rigorous determination of risk relating to ground fall or excavation instability. Risk analysis can be conducted by various means, but the basis is formed by either objectively or subjectively determining probability of occurrence of an event. In the case of rock engineering, this event is either instability or excavation failure. Rock mass classification systems provide objective analysis of data collected on a typically subjective basis that also relate closely to excavation stability. A probabilistic analysis technique is presented that uses statistical distributions of rock mass and material properties, ground support fixture specifications, stress conditions, opening geometry, and ground support installation quality to more rigorously determine probability of failure for an underground opening and the subsequent risk to personnel.

Jan 5, 2007