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By C. B. Manula, R. Venkataramani

Application of computers to present-day open-pit mining with bucket wheel excavators (BWE) is discussed. The development of the wheel excavators and their use in mining are discussed along with the necessity for building a computer model of the bucket wheel and the mathematical formulation of the problem. The simulation procedure, testing the model, and test results are summarized. Even though the mining industry in 1966 produced more ore than ever before, current extraction rates are only a fraction of what is expected in the later years of the 20th century. Nearly 90% of all metals and mineral products consumed last year was recovered by open-pit mining. This has placed great pressure on this segment of the industry which has, consequently, resulted in some spectacular developments. With increasing size of projects, the need for increased sophistication of engineering, planning, management, and administration of modern mining installations has never become more apparent. The design of complete systems for the mine and plant that fit the mold of today's business and social environments is undergoing an evolutionary process. Traditional concepts in mine development and operations are being sidestepped in favor of new ideas and principles. As the overburden thickness increases, materials handling presents a major problem to mining companies, especially those concerned with the mass production of ore and waste from low-grade deposits. The profit margin here is likely to be significantly less as to take chances with capital investment. Constant efforts are needed to improve upon productivity if the ore is to be economically mined. The development of vast low-grade deposits and thick overburden deposits calls for better tools to handle the enormous amount of materials. A natural solution to this problem is the use of bucket wheel excavators (BWE), which employ a continuous cutting head to feed the materials handling system. High productivity, versatility, economy of operation, and adaptability to most types of haulage systems combine to make BWE's attractive for large earth-moving operations. "Operating costs are being pushed down by the impact of giant haulage units, by high-speed conveyors, and computerized railroads. Matching all these with the continuous output of BWEs, one can visualize increased production at much lower costs." Historical Background The wheel excavator, which was patented in Germany in 1913, made its first appearance in an open-pit lignite mine in 1920. From this early beginning, however, BWEs were slow coming into practice. Initial developments were dampened by many design problems. From 1936 onward, major developments in design improved the wheel's ability, capacity, and versatility. A literature survey shows that wheel excavators are being used in Australia, Zambia, South Africa, the Congo, India, Indonesia, Czechoslovakia, Russia, Great Britain, Guyana, Yugoslavia, Morocco, Germany, Canada, and the U.S. for mining and loading chalk, lignite, clay, sandstone, phosphate, broken ore of iron, coal, shale, loose and semi-loose rock overburden.' A recent LMG* BWE at work in a German lignite mine weighs 6790 tons with an hourly capacity of 11,000 cu m. Although the BWE has wide applicability, its application to new mining areas poses a problem. Because of the large capital investment involved in BWE application and the narrow profit margins in mining low-grade ores or coal at depth, little margin of error can be tolerated in the selection, design, and operation of these machines. The questions that need to be answered prior to installation of a BWE for a mineable deposit are: 1.) What are the anticipated BWE performance characteristics? 2) Which method of BWE operation is most efficient? Attempts to answer these questions require a thorough knowledge of the mining system and the BWE operation. One approach is the building of a computer-ori-ented simulation model to determine how information and policy create the character of the BWE system under consideration. BWE Operation Modern BWEs generally excavate in blocks. Fig. la shows a BWE working in an established cut. The wheel is positioned to travel on the pit floor in line with the top edge of the old highwall. As it advances, a new highwall is exposed in the direction of excavation. Digging is done by rotating the wheel, swinging it from side to side in long parallel arcs, and "crowding" into the bank, by advancing the entire machine, or by the travel of the digging boom if an automatic crowd is available (Fig. lb). A second way by which the wheel can be advanced into the bank is by the falling cut method. A brief description of each of these methods follows. Cut with a Crowding Machine: At the end of every swing, the digging boom can be extended by the thickness of cut desired and the boom swung back in the reverse direction. Obviously, the thickness of bank excavated does not vary with the boom position; therefore, the slewing motion of the boom is fairly constant for uniform output. The thickness through which the digging boom can be advanced into the bank is theoretically calculated from the formula'

Jan 1, 1971

By T. J. Neil, C. B. Manula, O&apos

This paper describes a simulation model developed to analyze truck haulage problems as they are found in the open pit mining and construction industries. The model, using a computer, cycles trucks between their assigned loading and discharge points over a measured haulage route. Required parameters for model operation include the number and type of operating trucks and loading units, maximum truck acceleration and velocity, a realistic vehicular deceleration rate, and the ratio of ore to total material loaded at each origin. Equipment performance characteristics, system profile characteristics, and service time distributions are also included. Records are kept of all waiting times at the loading and discharge points and of any truck interference on the haulroads (moving queues), and a current journal of ore and waste production is maintained. In the pursuit of reduced truck haulage costs, management is confronted with three particular problems in truck and shovel mines. 1) What is the best assignment of trucks to shovels in the system? 2) If new equipment is needed, which new shovels and trucks will operate most effectively in the system? 3) Are any physical changes in the haulage layout warranted to increase productivity? For small open-pit mines the answers to these questions may be obvious. However, for larger operations complex problems are encountered with production and system configuration relationships usually being unknown. The mining simulator discussed here was developed to aid management in evaluating alternative pit-haulage schemes to reduce the risk involved in the selection and assignment of equipment in open-pit truck and shovel mining systems. There are two fundamental differences between this model and other truck haulage simulators: one is a difference of degree, the other a difference of kind. This model can simulate more complex systems than previous models by allowing transportation of material from multiple mine faces to multiple mine destinations and by being readily adaptable to a wide variety of mine layouts. The second difference that characterizes the model is the standard simulation of truck movements. This technique enables each vehicle to perform according to its mechanical capabilities and the physical profile of the haul road. Among the similarities between other truck haulage simulators and this model are the recognition of the probabilistic nature of certain service times in the system and the use of a digital computer to operate the model. The computer inherits the staggering computational and bookkeeping work loads and provides a means for compressing the time scale by allowing several days' operations to be simulated in a matter of minutes. DISTRIBUTION SYSTEM Basic aspects of the system analyzed here may be brought into focus by referring to Fig. 1, a schematic of the typical open pit truck-shovel operation programmed into the simulation model. A single stage materials handling scheme is employed to transport both ore and waste. Loaded trucks are directed to a crusher or stockpile if ore is being mined, while overburden is conveyed to a waste dump. Each shovel has a given number of assigned trucks and has two associated haulage routes, one for waste and one for ore. Up to 5 shovels and as many as 10 trucks per shovel (or a total of 50 trucks) can be operated in the model. Essentially the model, using a computer, cycles trucks between their assigned shovels and the proper discharge points over measured haulage routes. Required input parameters for model operation include the number of shifts to be simulated, operating time per shift, number and type of operating trucks and shovels, maximum truck acceleration and velocity, a realistic vehicular deceleration rate, and the ratio of ore to total material loaded at each shovel. Equipment performance characteristics, system profile characteristics, and service time distributions are

Jan 1, 1968

By Daniel T. Brian, O&apos

Exploration targets and results in the mining industry are commonly summarized in terms of tonnage and grade. Business appraisals require, as additional information, the expected profit or loss implied by the physical summay. The early incorporation of knowledge gained through financial analysis computations can provide a means for economically resolving many common exploration and development problems. Examples of such applications from the activities of exploration planning, reconnaissance, land acquisition, and development planning are presented. While prospecting for mineral deposits, geologists are frequently asked what they hope to find or have found. The response is usually descriptive, such as "50 million tons of 1% copper," and the economic implications are deferred until a capital investment decision is required. At that time, a financial analysis integrating the four analysis components is completed. These components are: 1) Tonnage and Average grade above cutoff grade 2) Capital and operating costs 3) Product value 4) Appropriate profit or loss accounting Close examination of these components reveals that there is no intrinsic reason for avoiding financial analyses at earlier stages in exploration, as long as the uncertainty of the estimate is realized. Tonnage and average grades are commonly estimated at early stages in exploration. For this component, the appropriate cutoff is the major uncertainty. Capital and operating costs are difficult to estimate before detailed quantitative information is available, but estimates useful for the types of applications pertinent to earlier exploration stages are possible. Product value estimates may or may not be difficult, depending on the pricing structure of the particular commodity. A simplified accounting procedure usually may be adopted without introducing any significant loss of validity. This paper describe some of the financial analysis applications which are pertinent during mineral exploration and deposit development. These applications lend additional insight during exploration planning, land acquisition, physical exploration, and development. The examples are oriented toward a hypothetical management investment policy which is based on a single criterion, a minimum 15% discounted cash flow rate of return. In reality, managements use multiple criteria. This simplification is convenient for purposes of presentation and does not invalidate the examples. Computers are useful, but not a requirement, for the applications described. The use of computers provides the results on a more timely basis at costs lower than equivalent manual computations. UNDERLYING PRINCIPLES AND ANALYSIS METHODS Data estimates are subject to uncertainty. The analyst may choose to assess this uncertainty by using one of two principles. Each principle has some advantages. The principle most commonly employed is "assumed certainty." Each financial analysis is based on one set of selected data, which may represent the average estimates, minimum expected conditions, etc. The critical data is identified by making successive calculations, varying the values used. Such studies, called sensitivity analyses, isolate the economic effect of various uncertainties, and guide the allocation of future efforts toward improving the overall accuracy of the analysis. A second principle, "assumed risk," examines the interactive effect of the current uncertainties by qualitatively assigning probability distributions to each data element. These probability distributions indicate the currently appraised chance that a given numerical value will prove to be true. The corresponding procedure, risk analysis, consists of processing many individual financial analyses, each analysis being based on randomly selected numerical values from the probabilistic distributions. The result is a numerical presentation of the uncertainty, but care must be exercised in the interpretation of results. The advantage of this procedure is the direct measurement of economic uncertainty caused by the interaction of uncertain estimates. Kennecott Copper Corp. has developed computer systems for accomplishing sensitivity and risk analyses.

Jan 1, 1970

By D. P. Harris

This paper surveys a few of the quantitative exploration models that might be of interest to an ex-plorationist seeking to apply methods of operations research to mineral exploration. A general development of each model is presented, and the model is evaluated relative to the exploration activity. Finally, a philosophy of exploration and mineral resources is presented, and a possible exploration strategy incorporating concepts of some of these models consistent with this philosophy is proposed. The high cost of conducting an effective exploration program has generated an increasing interest in methods of optimizing the allocation of the resources of a firm engaged in mineral exploration. As a step towards this, mathematical models have been formulated which are designed to quantify those variables of mineral exploration in such a manner that they can be integrated with all phases of the firm's operations so as to optimize its profit objective. QUANTITATIVE MODELS General: Exploration models express at least two concepts: mineral occurrence and effectiveness of search. In simplest terms, an exploration model might be formulated mathematically as follows: P(X) = F(X) . G(X), where P(X) = the unconditional probability of discovery of X deposits, F(X) = the probability of occurrence of X deposits, and G(X) = the probability of discovery of X deposits, conditional upon their existence in the search area. With these general models as a basis of reference, some of the specific exploration models are examined below. The Allais Model: DEVELOPMENT - M. Allaisl performed the first study in which a philosophy of exploration was formulated into a probability framework. His objective was to estimate the economic profitability of the exploration of the Algerian Sahara. Allais divided the Algerian Sahara into blocks of land (called cells) 10 km square and defined the random variable, X, as the number of mining districts per cell. Thus, F(X) would describe the probability of 1, 2, or n mining districts occurring within any cell of the search area. Allais reasoned that his best information as to the form of the distribution, F(X), was the number of mining districts per cell that had been found in well explored areas in the world, such as the Western States of the U.S. He found that the Poisson function produced a reasonably good fit to the distribution of districts in these explored areas: where? is a parameter, such as the average number of districts per cell. Thus, by determining the value

Jan 1, 1968

By Y. C. Kim, C. B. Manula

This paper is concerned with the details of the derivation of an operations research model, specifically linear programming, to solve production scheduling problems. While some results are presented for an actual study, the calculation of cases of a more general nature have not yet been completed. Production managers from those segments of the mining industry that experience a seasonal or highly variable sales demand find it rather difficult to develop overall producing plans for their organizations. In an attempt to solve this technical problem, demands are usually met by keeping production corresponding exactly to sales. This results in a fluctuating production schedule which is costly to maintain because of overtime premiums in periods of high requirements and because of costs associated with an idle mine plant during slack periods. An alternate solution may be to produce part of the desired amount and make up any deficiencies by using overproduction in previous time periods. This tends to smooth the production pattern through the use of a stockpile. However, because of associated storage costs, the solution may again be undesirable if it yields comparatively large surpluses. In general, this type of scheduling problem has an infinite number of solutions which satisfy the requirements. These are largely dependent on the extent to which an operation is geared to changes in production and on the size of its stockpiling facility. The determination of an efficient schedule is implied, therefore, as one lying between two extreme solutions, i.e., one that minimizes surpluses and one that minimizes output fluctuations. These conflicting objectives result in an economic balance problem between the costs of carrying surpluses forward from slack periods versus the cost of high production levels during peak demands. Various methods of operations research have been developed to handle problems whose genesis is explained above. It is very seldom, however, that these quantitative means are used in practice by the mining industry. Those who are familiar with mining operations know that most managers have not progressed satisfactorily in this area. Production planning systems which most mines employ today are often no more than rough records of management's mental planning. For small mines these means are normally sufficient; but as operations grow larger and become more complex, this type of planning ability is no longer adequate. Managers may find themselves losing control of cost relationships, taking longer to outline production sequences, and becoming forgetful of certain resource availabilities. ORIGIN AND SCOPE OF A SCHEDULING PROBLEM The more fundamental aspects of the production scheduling problem can be brought into focus by demonstrating how it exists in a bituminous coal mine* located in southwestern Pennsylvania. This mine, operating in the Pittsburgh seam, uses a definite room-and-pillar block system of mining, which is planned and carried out with only slight variations to meet local conditions. The coal seam, which is firm and varying in thickness from 8 to 9 ft with the bottom 6% ft being extracted, is under 500 ft of cover and opened by a shaft. The bulk of the coal (60%) is obtained from the pillaring activity with the mains and sections, which are considered primarily as development activities, contributing the balance of 12% and 28%, respectively. All coal is won by a ripper-type continuous miner in conjunction with shuttle-cars directly behind a loading machine. Track haulage is employed to move coal from these machine centers to the shaft bottom. The coal, thus mined, is shipped to one destination as part of a mixture of coals to be used in the making of steel. A material flow diagram of this distribution scheme is illustrated in Fig. 1. PROBLEM STATEMENT From the above types of shipments and mining limitations, the basic problem is to determine an economic means of producing and stockpiling a single coal product produced in three different machine centers (main, section, and pillar). Fig. 2 shows total demand for coal as a function of time. Because of the extremely high peak demands, slack period production must be stored and then distributed

Jan 1, 1969

By J. F. Abel

It can be concluded that rock mechanics instrumentation, geologic mapping, and operations research in combination will produce an accurate estimate of tunnel support requirements for establishing a steel-set load prediction model. This estimate can be used to increase safety and to reduce tunneling costs. Unnecessary supports can be eliminated; overloaded or insufficient supports can be replaced on a more scientific basis than previously possible. Only a limited number of measurements are required to develop a steel set load prediction model. The results of this study can be applied generally; but, every tunnel must be considered as a separate problem. The construction and geologic variables which were found to be significant in the prediction of rock loads in this tunnel were: steel section modulus, set blocking points, percentage of alteration, relative degree of faulting, relative water condition, distance to the nearest fault, and average joint spacing. INTRODUCTION A combined engineering geology and rock mechanics program was performed throughout the Straight Creek tunnel pilot bore in Colorado in order to develop a technique for estimation of steel tunnel support loads. The technique involves the construction of a statistical model from measured steel set loads, geologic and construction factors. The aim of the investigation was to produce a set of charts, based on these factors, which could be used by a trained man equipped with a hand lens and measuring tape, to specify a safe and economical steel size and set spacing. In order to be of practical value the measured parameters had to be rapidly measurable and capable of quantification. It was apparent from the beginning that quantitative values for many of the geologic factors would have to be arbitrary. It was impossible to ascertain in advance which of the readily determi-nable geologic and construction factors significantly affect steel set loading. As the investigation progressed the number of observations was systematically reduced in order to determine the minimum amount of data which would be necessary for a significant mathematical model. This paper presents an analysis of all the variables measured at each instrumented steel set. The statistical significance of the measured variables was determined. On the basis of this determination, a mathematical model was constructed and charts were prepared to select and space the steel tunnel supports at any given point in the tunnel. Once the effect of geologic and construction factors on set loading is sufficiently established, it is possible to exert close control over the selection of steel tunnel support size and spacing. The magnitude of this rock mechanics program can be appreciated when it is known that there were 44 instrumentation stations for which steel set loads were measured. For each of these sets there were three construction variables recorded, and nine geologic parameters determined (Table I). A rough approximation of the cost for both the collection and reduction of the data would be $50,000. This is in addition to the cost of geologic mapping.

Jan 1, 1967

By Sukumar Bandopadhyay

The escalation in micro-computer power during the past ten years has been instrumental in the realization of operations research and other mine planning tools as a practical mine design tool. Present day utilization of computer techniques varies across a wide spectrum of activities: from operations research to aid mine management through payroll processing to mine design, monitoring and control. With the advances in computers, increasing automation and robotization in mining, operations research techniques are being used in modeling and evaluation. The trend discernable in mine planning is increasing use of operations research models. In the next ten to fifteen years, nanotechnology will enter in our lives in a big way. There are several methods being pursued for information processing at the nanoscale. The mineral industry management in the 21st century will be faced with several new challenges and opportunities from these advances in the mineral industry applications, data and information acquisition and transmission technology

Aug 25, 2002

By Thomas V. Falkie

The use of operation research and computer techniques in the mineral industries continued to gain acceptance during 1966. There are generally at least five sources of information for researchers and practitioners in the field: (1) Technical periodicals like MINING ENGINEER- ING, Mining Congress Journal, etc. (2) Proceedings of the Symposium and Short Course on Computers and Operations Re- search in Mineral Industries, held last year at Penn. State University and jointly sponsored by the University of Arizona, Stanford University, Colorado School of Mines, Penn. State University and the Society of Mining Engineers of AIME. (3) Various local conferences, such as the 1966 Rocky Mountain Minerals Conference held in Denver. (4) United States Bureau of Mines publications. (5) The AIME annual meeting in New York and the SME Fall Meeting in Tampa.

Jan 2, 1967

By V. Srajer

Operations research includes the application of systems analysis to mining operations, the acquisition of field data and the creation and verification of computer models of these operations. These activities result in the generation of computerized mine planning tools which have a firm base-in observational facts. The Surface Mining Laboratory of the Canada Centre for Mineral and Energy Technology (CANMET), in Devon, Alberta has been engaged in operations research activities since 1982. The work includes computer simulation of truck/shovel, continuous mining equipment and dragline operations. Other examples include cooperative programs with industry (such as the demonstration and evaluation of an integrated truck management system), the development of a device to measure off-highway truck rolling resistance, and an automated remote data transmission network. The presentation outlines existing and proposed research projects of the Operations Research group.

Jan 1, 1989

By J. S. Aronofsky

The areas are identified where the techniques of operations research have been applied in the petroleum industry. Particular emphasis is given to the linear program models that have been used for solving management problems. Of the entire gamut of applications for linear programming, attention is focused on the models that represent underground reservoirs. Such models could be used to help schedule drilling rig operations and to schedule oil production. A framework is established for placing the model for underground reservoirs in the proper perspective along with the models which represent other facets of the oil business. One of the remarkable phenomena of this century is the rapid growth in the size and' complexity of human organizations. The mere size of modern business means that administrative decisions can affect vast quantities of capital and large number of people. Mistakes can be tremendously costly, and a single wrong decision can require years for rectification. Moreover, the pace of modern business is such that decisions are required more rapidly than ever before; the mere postponement of action can give a decided advantage to a competitor. It is not surprising that the increase in difficulty of decision making has been followed by efforts to put this activity on a more objective basis. The growing science of operations research, which relies heavily on the methods that have proved so successful in the physical sciences, is one aspect of these efforts.

Jan 1, 1962

By D. Gatzemeier

The Jewett mine is a surface mine in central Texas, producing about 8,000,000 tons/year of lignite. The mine covers 46,000 acres of land. Approximately 430 people are employed by Northwestern Resources Company and through their efforts, the Jewett Mine is one of the most productive mining operations in the country. The Jewett Mine utilizes a unique structure of staff to convert Operations Research into plans which can be implemented at the "front line" level of the Operations Team. This planning not only makes the mine cost effective and efficient, but promotes creativity in mining, which promotes ongoing research to lower the cost of mining.

Jan 1, 1993

By S. W. Hazen

The use of operations research and computer techniques continued to expand during 1967. A number of mining companies either obtained computers or were experimenting with their use through service bureaus. This trend was also evident in the Canadian mining industry. Although the annual Symposium and Short Course on Computers and Operations Research in Mineral Industries was not held in 1967, technical periodicals like MINING ENGINEERING, Mining Congress Journal and Engineering and Mining Journal stepped up their coverage on the subject. The annual AIME meeting had eight papers in two sessions and one session on Operations Research was held at the SME Fall Meeting in Las Vegas.

Jan 2, 1968

By Guy G. Jr. Granger

INTRODUCTION Due to the current depressed state of the mining industry, many mining companies have been forced to operate at less than maximum capacity. This paper will describe how the AMAX Henderson Molybdenum Mine and Mill near Empire and Parshall, Colorado respectively have utilized operations scheduling and budgeting to achieve production targets at minimum operating costs The AMAX Henderson Molybdenum Mine and Mill have been operating most recently at approximately sixty percent of maximum capacity. OPERATIONS SCHEDULING Production Targets. The production targets are tons, operating days, tons per opera- ting day, head grade, recovery, and pounds of molybdenum. One specific objective of operations scheduling is to meet the production target for pounds of molybdenum. Present market conditions dictate that production be adjusted to match sales of molybdenum. Over-production and creation of molybdenum inventories is unacceptable. In summary, the product ion target for pounds of molybdenum must be met, but not exceeded. Capacity. The AMAX Henderson Mill single stage open crushing circuit capacity is 48,000 dry short tons per day. The AMAX Henderson Mill single stage semi-autogenous primary grinding mill circuit capacity is 38,000 dry short tons per day. All other AMAX Henderson Mi 11 circuits such as rougher flotation, regrind, cleaner flotation, lead leach, filtering, drying and concentrate handling are sized to accommodate the 38,000 dry short tons per day and pro- ducts from same. The tailing delivery system is designed to accommodate tailing from a 38,000 dry short tons per day operation. Constraints. One mill production scheduling constraint is that the mill operating schedule must be the same as mine production, ore haulage and crushing. There is a total crushed ore stockpi1e storage capacity of 240,000 dry short tons. This breaks down to 60,000 dry short tons of crushed ore over the three reclaim pan feeders for each of the four primary grinding mill circuits. However, there is only approximately 10,000 dry short tons of live crushed ore available over the three reclaim pan feeders for each of the four primary grinding mill circuits. Dozers must be utilized to re- claim and push the other crushed ore. Primary grinding mill throughput is

Jan 1, 1986

By B Clements, G Lane

The operations and project environments are very different, but overlap in many areas. The development of mineral processing projects is goal oriented, is schedule focused and is a contractual environment. The operating environment for a mineral processing plant is oriented around longer-term commitments, focused on delivery against budget expectations, and an environment heavily influenced by long-term personal relationships, community relationships, and people issues.Further contrast is evident in the way expenditure is managed. Projects commit large amounts of money quickly to meet schedule-based objectives. Operations manage expenditure to minimise operating costs and are able to, or are forced to delay decisions on large expenditures. However, these two environments need to come together in many ways, ranging from greenfields plant handover to a new operations and maintenance crew, to day to day interaction and management of brownfields projects, often in close proximity.This paper considers the different perspectives people bring to projects and early operations, including the advantages and disadvantages of æoperationsÆ input to a project and models for managing value adding during project development, implementation and debottlenecking.CITATION:Lane, G and Clements, B, 2012. Operations versus projects - How do people think and what are the implications?, in Proceedings 11th AusIMM Mill OperatorsÆ Conference , pp 11-16 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Oct 29, 2012

In this paper the system of an operative expert estimation of innovative policy of mining enterprise technological development on sustainable development principles, created in the laboratory of economic analysis, planning and management of Mining Institute after D A Kunayev is represented. It is based on application of technology of satellite navigation and global positioning system (GPS). It allows to estimate the geotechnological complex functioning efficiency in the automated mode and on the basis of the obtained results to develop a complex of management measures on the competitiveness increase, realised by means of the automated corporative management system of geotechnological complex. The estimation of complex competitiveness is carried out on such basic aspects, as: technical, technological, organisational, ecological and economic as all of it give certain expenses and influence on complex overall performance. The major difference of the given approach is its realisation within the limits of the automated corporative management system of geotechnological complexes. It is based the fact that data of monitoring of complex's work will provide a sustainable development of geotechnological complex of mining enterprise.

Dec 6, 2010

By Sylvain Castonguay, Sylvain Pilon

"Operator competency is an important factor in the safe and successful operation of today’s complex processes. Competent process unit operators deliver superior performance and results. In greenfield projects, operator competency is particularly critical, as the lack of specific operations experience must be offset with proven practical knowledge and skills to avoid mistakes that can cost millions in terms of start-up delays, production losses and equipment damage. It is, therefore, important to know precisely what competencies are required for a project or site, and what any gaps in operator competency need to be addressed and improved. The success of plant commissioning, start-up and sustainable operations is directly tied to operator competency. The significant investments required for new projects, the exploitation of more challenging ore bodies, and the increasing complexity of plant operations have placed renewed emphasis on the need for competent operators. Furthermore, many large capital projects are being developed in remote regions, often with ties to the delivery of social benefits for the local population in terms of jobs, training and other development opportunities. While automation and processing technologies have helped do more with less, indicators now point to a bottleneck in human performance limiting operational results.Automation industry development in managing abnormal situations, including solutions for simulation and operator training, has produced applications to provide customized and flexible training methods. This paper will discuss tools to automate the operator competency process in order to guide trainers to effectively teach, measure, control, provide feedback, and monitor operator skills. Today’s competency management solutions can be effectively applied throughout the course of an operator’s professional life, as a guide to hiring new operators, for developing competencies to support operations, and also to develop and refine best practices and share lessons learned."

Jan 1, 2016

By R. Zunich, E. Rybinski, M. Grondin, B. Flintoff

It is uncommon these days to read a business magazine or newspaper without finding a reference to Knowledge Management (KM), and its critical importance to corporate success. One definition of KM is: “getting the right knowledge to the right people at the right time and helping them to put information into action in such ways that strive to improve organizational performance.” In the minerals processing industry we could assert that we have been practicing this for years through effective operator training. In most cases we would probably be wrong. Training has certainly filled the need of addressing safety and basic operational issues, but we must collectively focus on education (learning), and the difference, as measured in outcomes, is not at all subtle. This paper briefly examines the history of training and education in the mining industry, and looks in more detail at the impact of one company’s approach to empower its workforce, while keeping the ultimate goal clearly in mind: “The great end of knowledge is not knowledge, but action.” (T.H. Huxley)

Jan 1, 2001

By Tobias Hansson

The quality of Lime Cement (L/C) columns is to a great extent dependent on the installation procedure. At a site in the south of Sweden, the client reported problems with the installed columns. To evaluate the operator and equipment dependent part of the installation, Hercules Grundläggning AB was appointed as a competitor to the site contractor. The purpose was to install a number of L/C columns with equal installation parameters for both contractors. A total of 65 test columns were installed in June 1999. The geology is a typical Swedish organic soil profile with a top layer of peat overlying gyttja and clay. Two different binders were used in the comparison; blast furnace slag with cement and lime with cement. After installation, the quality of the columns was monitored by penetration tests and a column sampler for extracting whole columns. The column penetration test were performed 28 and 134 days after installation and extraction 134 days after installation. Inspection and tests of the extracted columns showed that by using a fully computer controlled procedure, the contractor produces columns of superior quality and homogeneity.

Jan 1, 2000

By Richard L. Seal, Allan L. Turner

INTRODUCTION The chapter discusses the procedures and operations starting with the actual training of operations personnel with respect to a grinding mill circuit and a typical check-out and start-up of a grinding circuit before turning over the operating circuit to production personnel. For the purposes of this paper, it will be assumed that the mill circuit installation is at a new location or a new operation at an existing operation. Expansion of an existing grinding facility would probably require less time-consuming and less costly procedures than those that will be outlined here. There are numerous arrangements and types of equipment that can be used for grinding circuits. For example, the grinding could be wet or dry. The grinding mill could be a rod or ball mill or some combination. The circuit could be open or closed. The closed circuit classification could be accomplished with rake classifiers, spiral classifiers, cyclones, stationary screens, or vibrating screens. To limit the discussion, the grinding circuit shown on Figure I will be discussed. The circuit includes a dry ore feed by a belt conveyor (feeder) to a wet ball mill. The ball mill discharges to a cyclone feed pump box and pump. The pump feeds a cyclone type classifier with the cyclone underflow returning to the ball mill feed and cyclone overflow being the required grinding circuit product. For the purposes of this paper, it will also be assumed that the extensive overall preparation necessary for every start-up has been accomplished. These preparations are only outlined here as they have been adequately dealt with in recent papers. (1), (2), (3) The items in these preparations would include. 1. Appointment and indoctrination of the start-up organization. 2. Integration of the start-up into the overall project schedule. 3. Development of the specific start-up schedule. 4. Budget determination and monies appropriation.

Jan 1, 1982

By R. T. Rometti, C. A. Bossard

INTRODUCTION Whenever a new mining property is developed or a new concentrator is built, considerable time, money, and planning are invested for the exploration, engineering, and construction of the required facilities. Yet, when these facilities are ready for startup, all too often insufficient time, money, and planning are invested in this critical phase of the project. Failure to plan for this phase results in untimely personnel mobilization, insufficient operator training, and inadequately prepared budget estimates, which combine to create excessive costs, lack of communication, unclear responsibility divisions, unnecessary delays, and poor production levels. Insufficient or excessive leadership, due to unclear assignment of responsibility, results in irrevocable problems within the startup team. A carefully planned and budgeted startup phase can avoid many of these problems. SCHEDULING A key element in properly planning the startup phase lies within the area of scheduling. Once the schedule has been established, planning can be greatly simplified through the expansion of each specified item. The overall project schedule should specify the date when the startup and initial operating management team will be brought onto the project. This team should be mobilized at least two years prior to the anticipated startup date, to allow sufficient time to begin to plan, assemble the startup groups for the various areas of the plant, initiate a workable communications system, and implement the development of the training program. This team is responsible for the preparation of a startup schedule, which would detail the dates for all key items associated with this phase of the project. These key items would include training program development, personnel hiring dates, prestartup plant checkout, etc.

Jan 1, 1982