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By J. M. Mutmansky

The usual method employed to determine the direction of flow in a connecting airway in advance is to conduct a survey between the two existing nodes being connected by means of the new airway. It is then assumed that airflow will take place from the node with higher pressure to the node with lower pressure. The purpose of this paper is to examine this direction-of-flow problem --using properties of the system characteristic curve for a two-terminal network --and specify some conditions that ensure that flow will take from the higher-pressure node to the lower-pressure node. It is concluded that the only requirement is that all characteristic curves of fans in the network should be strictly decreasing. Five numerical examples illustrate some important properties of the direction-of-flow problem.

Jan 1, 1999

By Matthew R. Ward

This paper aims to describe the reasons and methods to extend enterprise networks into underground mines. Currently mines use a number of communication networks for telephones, PLCs, two-way VHF radio and computer networking. Each system can have different wiring, protocols, converters and interfaces all which require expansion and maintenance. The convergence of voice, video and data using Ethernet and TCP/IP has the very real benefit of one ?wire? for underground networks. Now that the majority of underground equipment can communicate using TCP/IP, network convergence can offer reduced network expansion and maintenance costs. Cost savings are good, but the truly exciting aspect of network convergence using Ethernet is the low-cost, high speed wireless networking now available . These wireless networks offer an opportunity to facilitate mining automation even on a small budget. How would mine operators change they way they operate if they could gather vehicle location and data in real time? What about controlling equipment remotely? The list of applications is long and includes traffic control, dispatching, real-time ore blending, tele-operation, automated production reporting, vehicle health monitoring, elimination of paper forms and immediate network access for surveyors and geologists. An ideal converged network for underground mining would be robust, inexpensive, easy to expand, enable fast and secure connections while keeping voice communication performance equivalent or better than what is now provided by VHF radio

Apr 1, 2005

By D H. Lee, J F, J H. Rubinstein

This paper examines the problem of optimising the life-of-mine costs of an underground mine, focussing particularly on the costs associated with the underground development needed to provide access to and haulage from the ore zones. This mine optimisation problem is modelled as a network and a solution technique is outlined. The effectiveness of this solution technique is then demonstrated by applying it to a prototype test example and some actual case studies. Finally, extensions of this technique to other underground mining problems are discussed.

Jan 1, 2000

By Gordon W. Hines

This paper, dealing with network planning in an industrial complex, discusses the type and size of project that can be handled on a manual basis. After a review of the fundamentals of network planning, and specifically of the Critical Path Method, the author analyses several different types of projects, each with its own characteristics. Some can be handled in conjunction with a computer, but others can be dealt with equally well without one. Among the projects discussed are a blast furnace reline, the installation of a billet mill flying shear, and routine maintenance-type operations.

Jan 1, 1967

By H. Hamza, M. R. MacIver, M. Pawlik, L. Malin

"Size and shape are important properties of aggregates formed in engineering processes and natural systems. Measurement of aggregate size or shape is a challenge when the solids concentration is beyond the gel point, ?? > ??g, when the aggregates are in contact and it is difficult to distinguish or segment individual particles in the network. This paper describes the use of a micro-computed tomography (micro-CT) method for the calculation of the aggregate volume fraction, ??, and a characteristic aggregate length, ??a, based on a 3D chord length measurement algorithm. These parameters can be used to quantify changes in aggregate and pore size during a compressive yield stress test to help understand the impact of flocculation and network structure on dewatering. INTRODUCTION In a thickener, flocculation is often used as a pre-treatment step and the final solids content of the underflow depends on the network strength and permeability of the bed formed by settled aggregates (Usher, Spehar, and Scales 2009). This paper describes the measurement and analysis of micro-computed tomography (micro-CT) of flocculated mineral sediments to obtain a characteristic length, ??a, of the aggregate and pore phases during a staged compressive yield stress experiment. This parameter may be used as an indicator of changes in aggregate and pore size to quantify material behaviour in solid-liquid separation processes that depend on self-weight compression or applied pressure. This analysis was carried out to help understand the effect of flocculation on aggregate structure and the thickening process as part of a broader research on the improved solid liquid separation of tailings produced in the oil sands industry.Aggregate properties The solids phase of an aggregated or flocculated suspension will eventually settle – if left undisturbed – under the influence of gravity. As settling of a flocculated suspension continues, the solids volume concentration, ??, may increase to the gel point or gelation threshold (Landman and White 1994), ??g, above which the aggregates form a network of touching particles, or space-filling structure (Kranenburg 1994). In the sediment bed, the aggregates are surrounded by fluid and occupy a partial volume fraction, ??, according to"

Jan 1, 2018

By J. F. Archibald

This paper reviews the formation of the Canadian Mining Education Council (CMEC) and its terms of reference. CMEC considers that Canada?s mining schools represent a distinct competitive advantage to the Canadian Mining Industry. The core objective of CMEC is to reinforce the links between the constituent schools and their individual Industry Advisory Committees. The latter traditionally provide the schools with a great deal of guidance on a range of planning issues, including recruitment, curriculum, research and infrastructure. Beyond those bounds CMEC intends to improve its ability to network with the industry and government. It is concerned overall with the objective to raise the quality and level of its educational and research contributions to meet the needs of industry and society.

May 1, 2002

By F. B. Foley

A description of tests made by a committee of the Division of Engineering of the National Research Council to determine whether velocity of impact affects the formation of Neumann bands. FOREWORD Not infrequently, in the case of the failure of a metal structure, such as a bridge, tank, airplane, gun carriage, etc., a doubt arises whether the failure was due, among other causes, to the effect of an overburden or of an explosive maliciously or accidentally exploded in contact with it. As few means of discriminating between the effects of these causes have been known and these means were of a rough qualitative character, it was surprising to find in the record of an inquiry into the failure of a structure, the statement that, by observations on the Neumann bands in the structure, it was possible to determine the speed of breakage in the metal and to distinguish between a fracture caused by an explosion and one caused by an ordinary mechanical strain. Such a criterion, if well proved, was of value to the explosives experts of the Bureau of Mines, the Ordnance Department of the United States Army and the Bureau of Ordnance of the United States Navy, but search showed no record of it in the literature nor did any of the metallurgists and metallographers consulted know of any data on which such a criterion could be based having been assembled and critically studied. However, it was universally held that there was promise in the idea and that there existed unique facilities at the Bureau of Mines Experiment Station at Pittsburgh for the preparation, under definitely known conditions, of material for study by metal-lographers, and that material of value might also be obtained from the proving grounds of the Army and Navy. Therefore, with the approval of the Director of the Bureau of Mines and the Chiefs of Ordnance of the War and Navy Departments, the Chairman of the Division of Engineering of the National Research Council, on April 12, 1919, appointed a Committee on the Investigation of Neumann Bands, which has since been engaged in this study and which, after some slight change in its personnel, consists now of the following: Dr. William Campbell, Advisory Metallurgist, Bureau of Ordnance, Navy Department. F. B. Foley, Metallurgist, Bureau of Mines. S P. Howell, Explosives Engineer, Bureau of Mines. Comdr. O. M. Hustvedt, U. S. N., Bureau of Ordnance, Navy Department. Dr. Charles E. Munroe, Chief Explosives Chemist, Bureau of Mines.

Jan 9, 1922

By KeBler H-W, Seeliger A

Vibration analysis for diagnosis of machines is a powerful instrument for condition monitoring. Especially for diagnosis of antifriction bearings various equipment and techniques have been developed and are available. The envelope analysis is the most reliable method for bearing diagnosis. Different bearing failures lead to different patterns of the envelope spectrum. Checking the amplitudes of characteristic bearing frequencies is mostly not sufficient for a reliable diagnosis. Therefore human experts compare the pattern of the spectrum with typical patterns of bearing defects. In this paper a diagnosis system is presented which imitates this action of human experts. Using a neural net the system not only distinguishes typical bearing defects but also detects unbalance, beating of disengaged machine parts and other kinds of machine failures.

Jan 1, 1995

By D. H. Rubisov

The neural networks modelling approach is one of the most elegant black-box type methods to model industrial processes. In this paper, this approach is applied to model the performance of the CEZ zinc electrowinning plant. The model that was built was found to exhibit an excellent predictive ability for voltage and current density. The model was also used to investigate the sensitivity of the process to various key variables such as temperature, current density, zinc and sulphuric acid concentration, etc. Optimal regimes for zinc production were identified from the power consumption standpoint.

Jan 1, 1996

By G. F. Friel, J. C. Edwards

A series of seven underground-coal-mine fire experiments was conducted in the Safety Re-search Coal Mine at the National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory. Coal and styrene-butadiene-rubber conveyor belting were burned upwind of two sensor stations, 18 m and 148 m from the fire source. Exhaust from a diesel locomotive flowed over the fire sources in six of the tests. Metal-oxide-semiconductor (MOS), CO, and optical-path-smoke sensors were positioned at both stations and found to be an optimum set of sensors for the fire discriminations. A representative set of 7,679 samples of CO data and data from the smoke and diesel-exhaust MOS sensors were used as inputs to train a neural network (NN). By testing 42,538 data samples from the seven experiments, all fires were detected by the NN within 9.67 min from the onset of significant changes in the MOS voltages without any false alarms.

By S. Katz, V. Stanek, K. L. Moore, E. D. Larsen, D. E. Clark, P. E. King

"Cupola melting is a complex physical and chemical process having a number of nonlinearities which make conventional process control difficult. In addition, sensors for both inputs and outputs of interest to the foundry operator are often difficult or impractical to apply in a production situation. The present work is an effort to control the process using both intelligent and conventional control techniques as appropriate. Neural networks are used as an inverse process model in a cascade, feedforward controller in conjunction with a finite difference model of the cupola. A separate implementation of the neural net serves as a stand-alone visualization tool; it can operate on a laptop computer and is a useful tool for the cupola operator. IntroductionThe goal of the present work is to develop a controller to improve the efficiency of cupola melting. Although cupola melting as a process has a long history, automatic control has been elusive because the process has been poorly understood. The purpose of this four-year program, which has been underway for about six months, is to develop a controller for the cupola process using intelligent and conventional control methods.The Cupola ProcessThe cupola furnace [1,2] is one of the oldest processes for making cast iron, and is still the dominant method. It takes the form of a water-cooled vertical cylinder, as large as 4 m in diameter and 20 m tall. The charge consists of scrap metal, alloying ingredients, limestone, and coke, which serves as fuel; it is composed by weight and fed through doors near the top of the cupola. Air, usually heated and often enriched with oxygen, is blown in near the bottom through tuyeres extending a short distance into the interior of the cupola. As the coke is consumed, the charge drops and melts. A continuous flow of iron emerges from the bottom of the furnace, at rates as high as 100 tons per hour, and. is transferred to casting lines in the foundry. The exhaust gases are drawn from the top of the cupola, where they are processed to meet pollution standards."

Jan 1, 1994

By L. A. Puchkov, Mesentsev. V. K., I. O. Temkin

The problem of increasing underground mine ventilation efficiency is still of top priority. The complication of geological conditions with the depth of underground mining processes and reinforcement of requirements to technological and ecological safety are extorting the necessity of solving this complex problem with using of new information technologies. There exist different approaches to the ventilation problem. One of them, which the authors adhere to, is based on the concept of monitoring (under special mining and geological conditions-automatic control) of mining atmosphere and operative re-distribution of underground mining air among consumer , in accordance with the criterion of optimality [ 1.3 1. ] The set of tasks which should be decided in the frame of con- crete ventilation system depends on the peculiarities of deposit, technological schemes and so on. Nevertheless, some ventilation tasks such as: defining of ventilation network parameters; finding of dependence between these parametrs; operative redistribution of air streems, could be interpreted as universal ones. Main aspects of practical solving of different ventilation problems and their classification were investigated and considered in detail before [I ,2 ]. Some years ago we have began our investigations to develope quite simple and effective methods for decision of ventilation tasks on the base of neural network technology. It is well known that ventilation net, formed by various mine workings, is the main component of underground mining ventilation system. In general, network air distribution may be described by some functional F(Q,R,H), where Q- is a vector of air consumptions; H- is a vector of depressions; R- is a vector of airdynamic resistences. (Q,R,H) - are the informative entities , characterising every branch of the net. There exist some fundamental tasks which should be decided during the process of control of mining ventilation: calculating of natural air distribution Q = Q,{R, H,}, or Q = Q2(R, Q?}, where H, - is the maximal depression of the ventilation net, Q, - is the air consumption in the ventilation shaft; identifying of airdynamic resistance for some branches on the base either of air-depression removal or operative monitoring: [ ]

Jan 1, 1996

By C. Cho

One problem with the application of neural networks to the multivariable control of mineral and extractive processing is deciding whether and how best to use them. The objective was to compare neural network control to more conventional strategies and find any advantages in terms of set point tracking, rise time, settling time, disturbance rejection, and other criteria. The procedure was to develop neural network controllers using both historic plant data and simulation models. We tried various neural network control patterns including both inverse and direct neural network plant models. These were compared to state space controllers that were, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic Gaussian controller. We also learned the importance of incorporating state space into neural networks by making them recurrent--feeding certain output state variables into input nodes in the neural network. We concluded that neural network controllers can have better disturbance rejection, set point tracking, rise time, settling time, lower set point overshoot, and can be more foolproof and easy to implement in complex, multivariable plants.

Jan 1, 1996

The relationship between neural network (NN) performance and the parameters that form the architecture of the neural network is complicated. This paper studies an issue that is still controversial—the effect of the number of neurons and number of hidden layers on NN performance. This paper tests if the above parameters affect network performance when the NN is trained according to the quick stop strategy. It was determined that the number of neurons did not have any effect on the NN performance for a single layer or a double layer network. It was also found that the training error and calibration error could be poor indicators of the prediction correlation coefficient.

Jan 1, 2003

By Walter K. Utt

The process of drilling and bolting the roof is currently one of the most dangerous jobs in underground mining, resulting in about 1,000 accidents with injuries each year in the United States. To increase the safety of underground miners, researchers froth the Spokane Research Laboratory of- the National Institute for Occupational Safety and Health are applying neural network technology to rite classification of mine roof strata in terms of relative strength. In this project, the feasibility of using a monitoring system on a roof drill to assess the integrity of a mine roof and warn a roof drill operator when a weak layer is encountered is being studied. Using measurements taken while a layer is being drilled, one can convert the data to suitably scaled features and classify the strength of the layer with a neural network. The feasibility of using a drill monitoring system to estimate the strength of successive layers of rock was demonstrated in the laboratory.

By R. Vance, C. Cho, N. Mardi, Z. Qian, K. Prisbrey

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

Jan 1, 1998

By R. H. Estrada-Ruiz

This work analyses a new approach to estimates bubble size distribution of froth surfaces using artificial neural networks (ANN).Also, the robustness of ANN to interpret images with illumination perturbations, produced by light problems or dirt attached to the window of the video camera is evaluated. The experimental work was carried out in a laboratory flotation column, instrumented with an image acquisition system. The images were processed making use of a perceptron model with a hidden layer, sigmoidal transfer function and unitary bias, and the ANN trained with a back propagation algorithm. The results of validation show that ANN are reliable for learning and producing generalized predictions of the froth mean bubble diameter and bubble size distribution, when the model is trained using a database that contains information on the illumination intensity. Keywords: image analysis, neural networks, bubble diameter, bubble size distribution.

Jan 1, 2009

By Wankun Wang, Fuchun Wang

Based on the study of artificial neural network, the neural model was established for the prediction of germanium extraction from zinc oxide dust by microwave alkaline roasting-water leaching. Alkali-material mass ratio, microwave heating temperature, liquid-solid ratio, aging time, leaching time and leaching temperature were the significant factors for the process. The results indicated that the neural network prediction model was reliable, and the forecast values fitted well with the actual experimental values. The model could be used to predict the regeneration experiments with high credibility and practical significance. The accuracy of convergence of the model reached 10−5.

Mar 1, 2018

By Lu Li, Sara E. Wilson

Occupational exposure to whole body vibration has long been associated with increased incidence of low back pain and low back injuries1. A number of studies have investigated transmissibility of seat pan vibration 5, 6. While transmissibility has been well researched, the mechanism by which vibration may induce injury has not been thoroughly studied. Winter et al. identified increased reflex response delay after vibration exposure and speculated that muscular fatigue may be the cause of this increase9. However, a mechanism has yet to be demonstrated completely. A potential mechanism that may explain the increased risk is neuromotor habituation. Muscle spindle organs have been shown in the extremities to be sensitive to muscle and tendon vibration. Rapid length changes in muscle have been shown to result in kinesthetic illusions as the regular firing of the muscle spindles is interpreted as muscle lengthening4, 7. These illusions have also been demonstrated in the paraspinal musculature2. With removal of vibration, research in the extremities has demonstrated increased positioning errors, probably due to neuromotor habituation8. In this research, it has been hypothesized that neuromotor habituation after exposure to occupational vibration will increase positioning errors. It is further hypothesized that these errors can be shown to be linked to increased reflex response time. Such increased reflex response time could, in turn, decrease spinal stability and increase low back injury risk. Methods Both positioning error and sudden load response were measured before and after exposure to 20 minutes of 5 Hz, 0.223 m/s2 RMS seat pan vibration. Subjects were asked to sit on an unpadded seat without a backrest. Throughout the whole body vibration period, subjects were instructed to put their hands on a stable hand rest and feet on an adjustable stable footrest. The subjects were instructed to assume a comfortable and relax sitting posture for the duration of the exposure. Positioning error was measured using an active-active reposition sense protocol. Electromagnetic markers (Motionstar, Ascension Tech, Burlington, VT) were used to track trunk motion. With markers attached to the skin at the T10 vertebra, the S1 vertebra and manubrium, trunk flexion (the angle from vertical of the line connecting T10 and S1) and lumbar curvature (the difference in inclination of the T10 and S1 markers) were tracked. In the reposition sense protocol, subjects were asked to maintain an upright trunk flexion and to rotate their pelvis and lumbar curvature to assume a target lumbar curvature. In the protocol subjects completed training trials, where they were asked to match their lumbar curvature using a visual display, and assessment trials, where they were asked to reproduce the lumbar curvature from memory. After two initial training trials, training trials and assessment trials were alternated for a total of 3 assessment trials. Reposition error was defined as the absolute difference between the target lumbar curvature and the lumbar curvature the subject assumed during the assessment trials.

Jan 6, 2006

By Jorge Cangahuala

La neurociencia ha cobrado un papel muy importante en las organizaciones en las últimas décadas. En ese contexto, la neuroprevención busca determinar las potencialidades y limitaciones del cerebro al momento de enfrentar situaciones con potencial de algún incidente o accidente laboral. Identificando estos indicadores y conexiones neuro-centrales podemos tener mapeado cómo se comporta nuestro cerebro, cómo lo refleja en el comportamiento (acto seguro o inseguro) y cuáles técnicas implementar en cada proceso, para asegurar una cultura de seguridad preventiva y sostenible en el tiempo que se orienta primero al factor humano como generador de conductas que pueden ser remodeladas hacia acciones seguras.

Apr 30, 2018