Search Documents

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

By Darryl Amick, Keith A. Prisbrey, Lee E. Plansky

The problem addressed was to develop automated image analysis of metal welds to replace unreliable and costly 100% visual inspection. The objective was to evaluate neural networks combined with features from gray level surfaces. Photomicrographs of the welded seam in Zircaloy-4 channels used in nuclear fuel rod assemblies were examined. The methodology used was to select a set of morphological feature coefficients from Bessel Fourier morphological feature coefficients extracted from digitized photomicrographs. The selected morphological feature coefficients were then used to train a neural network. The approach was verified initially by successfully training a neural network using the known, secondary chemistry of the alloy welds. The results were good. The Bessel Fourier coefficients captured sufficient information from the metallographic images to distinguish weld quality. This approach has the combined advantages of compressing the images through Bessel Fourier coefficients and give the ability to rapidly process large amounts of data using neural networks.

Jan 1, 1993

By S. L. Jung, T. L. Nichols, K. Prisbrey

The problem was to evaluate the on-line detection of rock properties through acoustic emission sensors. Acoustic emission sensors, when attached to crushers, grinders, and roof bolters, are robust, economical, and relatively maintenance free. Our objective was to relate acoustic emissions to rock fracture mechanisms, including micropore closures, linear elastic deformations, and crack propagation. We evaluated acoustic emissions from steel jacketed rock cores during indentation tests. In addition we attached sensors to the rotating shaft of a rock bolter drill. A back propagation neural network "learned" rock fracture characteristics and rock type with 99% classification accuracy. A neural network's calibration and computational abilities suggest better acoustic emission sensor capabilities.

Jan 1, 1993

By J. T. Gepford, K. A. Prisbrey, T. Scott, M. Spangler

Using neural networks for plant control is challenging. The objective was to show how to make a neural network effective, even with noise, lag times, and unmeasured disturbances in the plant. The procedure was to evaluate the neural network in a laboratory flotation vision system and in a plant refractory gold autoclave system. Although the resulting neural network easily conquered simple laboratory data, the noisy plant data was much more difficult, and adaptive linear models were equally effective. We concluded that the neural network needs support by combining it with other tools found in the best control packages.

Jan 1, 2000

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 Ross Corben

This article presents the results of a study conducted using Prospect Explorer, a neural software tool from Neural Mining Solutions. This exploration tool takes an alternative approach to data analysis and visualisation, automating the detection and prioritisation of anomalies and the relationships between their components. Results from the project area around the Selwyn Cu-Au Mine in central Queensland are given which clearly demonstrate the ability of neural networks to quickly, effectively and consistently locate mineral deposits.

Aug 25, 2002

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 Lynn B. Hales, Randy A. Ynchausti

Introduction There continues to be a large economic opportunity associated with improving process performance through advanced process control systems within the minerals industry. The basis for this opportunity lies in the inherently low efficiencies of many of the unit operations found in mineral processing plants as well as the continual variation that is present in the ores being processed. The ageless target of such control systems is improved recoveries and concentrate grades, higher processing rates and lower unit costs. Many plants are enjoying the benefits of modem, well functioning process control systems but few if any have achieved plant-wide optimization in an economical sense. As new process modeling and control technologies, such as neural networks, continue to evolve and develop, the potential for plant- wide optimization is becoming a closer reality. Neural network technology appears to have a significant potential in the general field of process control and control of mineral processing operations. Neural networks are an emerging technology that have been evolving out of the study of biological systems since the early 1940's. The first software simulation of neural computing networks was reported in 1956 by Nathanial Rochester of IBM Research during the first conference on artificial intelligence which was sponsored by the Rockefeller Foundation. Neural Networks Neural networks can be considered to be a class of computer program that perform their problem solving functions based on a rudimentary model of the human brain and how it solves problems. Traditional computer programs and even computers themselves solve problems or process data sequentially and in a deterministic manner. The human brain, on the other hand, processes data and solves problems in a massively parallel fashion. We think of computers normally in terms of having a single processing unit, sometimes a co-processor to assist in certain activities such as mathematical functions or communications, bytes of memory, processing speed and program/data storage size.

Jan 1, 1992

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 Jorge Cangahuala

"El nuevo concepto que se establece es la Neuro prevención derivada como una disciplina de las neurociencias que busca conocer cómo trabaja nuestro cerebro, sus potencialidades y limitaciones al momento de enfrentar situaciones con potencial de algún incidente o accidente laboral. Identificado estos indicadores y conexiones neuro centrales podemos tener mapeado cómo se comporta nuestro cerebro, como lo refleja en el comportamiento (acto seguro e inseguro), su análisis y que técnicas vamos a implementar en cada proceso."

Sep 1, 2017

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

By B, G Strobos, J P. Maree, N Smit

In mining and processing operations where minerals, high in pyrite and low in calcite/dolomite are processed, acid is generated, which needs to be neutralised. Ticor at Empangeni in South Africa (SA) produces rutile (TiO2), leucoxene (TiO2 plus iron compounds), titanium (Ti), zircon (Zi) and ilmenite (TiO2 plus iron compounds). Neutralisation is required in the processing plant where acid is leached into the wash water. This water needs to be treated to a quality suitable for re-use in the metallurgical process or to a higher quality to make it suitable for discharge into the Empangeni sewage system (SA). For re-use the water needs to be neutral and under-saturated with respect to gypsum while for discharge into the sewage system the sulfate concentration needs to be reduced to less than 500 mg/L (as SO4). Acid mine water is generally neutralised with lime. Disadvantages associated with lime are the costs and maintenance of the slaking equipment as well as hazards, associated with handling of the alkali. The cost of powdered limestone (CaCO3) in South Africa, a by-product, is 50 - 60 per cent cheaper than lime. A technology has been developed where limestone is used for neutralisation of acid mine water instead of lime. This development includes: Limestone handling and dosing system. A novel, robust system has been developed where waste CaCO3 from the paper industry is slurried to a constant density. A full-scale plant was constructed to evaluate its performance (Figure 4). The dosing system consists of an inclined slab onto which the limestone is stored and from where it is slurried into a make-up tank. The density of the slurry in the make-up tank is controlled via a loadcell that measures the weight of the tank (operated at constant volume) and activates/deactivates the spray of the recycle slurry onto the slab. Integrated limestone neutralisation/lime treatment process. In this process powdered CaCO3 is used for neutralisation and partial sulfate removal of acid leached from the coal washing plant. The sulfate concentration can be further reduced to below the saturation level of gypsum (ie 1200 mg/L) through gypsum crystallisation by means of lime treatment, resulting in metal precipitation (eg Fe3+ and Al3+). CO2- treatment for CaCO3 precipitation. The pH of the lime treated water can be adjusted to pH levels of 8.5 and lower using CO2. The CO2, generated during limestone neutralisation (Equation 2), can be captured and used in this stage of the process. The first two stages (limestone- and lime neutralisation) of this technology have been implemented on full-scale at Ticor to treat the effluent of the process. The constructed plant is 80 m3/hr and differs from coal processing plants by way of not containing Fe(II) and Fe(III) in the water. The CO2-treatment stage (Stage 3) will soon be implemented at IHM as part of a program to optimise the technology for maximum benefits.

Jan 1, 2003

By Evan Jamieson

The conditions under which various iron(III) oxy-hydroxides phases precipitate from iron(III) chloride and sulfate solutions at ambient temperatures (<60°C) are reviewed. Many of these precipitates are slow settling and gelatinous but ultimately transform into other more stable or crystalline phases. The phase that forms depends upon the rate of precipitation and pH. A comparison has been made of the iron(III) oxides formed upon the neutralisation of various iron(III) solutions with caustic, slaked lime or slaked magnesia which provide different rates of neutralisation and slowly dissolving solids with localised high pH. Mössbauer spectra of ferrihydrites precipitated under these different conditions are compared and interpreted. Particular attention is paid to the settling rate and settled volume which is likely to relate to industrial thickener and filter performance. It is shown that in pure chloride solutions, poorly settling ferrihydrite is usually produced at 20oC regardless of the choice of base. However in sulfate solutions fast settling precipitates are produced using lime and magnesia but not with caustic. In mixed chloride/sulfate solutions, optimum settling rates occur when the molar ratio of [SO4]2to Fe3+ is about 1:1 and when the sulfate concentration is <0.20 M. It is proposed that ?dense amorphous basic iron sulfate? or well-ordered ferrihydrite is formed on slowly dissolving lime or magnesia surfaces. Implications for industrial liquors are considered.

Jan 1, 2003

By Wayne A. Ericson, Kirk S. Davis, Cornelis Winkler

BCI has developed a chemical process known as Ecophos® for the multi-industrial Pine Level Project in DeSoto County, Florida. Approximately 85 percent of the phosphogypsum at Pine Level will be converted to a very clean gypsum. The remaining 15 percent, including impurities plus some quartz tailings sand, constitutes what is termed redigestion solids. BCI has developed an alternative to the practice of disposing phosphogypsum in large above-ground impoundments. The proposed disposal method consists of mixing the redigestion solids with clays and reacting the mixture (leached tailings) with lime prior to disposal into the mine excavations as a reclamation soil material. Leachate water quality testing using lysimeter equipment indicates that the pore water associated with neutralized leached tailings meets maximum contaminant levels set for the drinking water standards of radium and selected primary metals.

Jan 1, 1992

By J. K. Horbaczewski

The reclamation of an open dragline pit (Pond A3P-2) at the Gibbons Creek lignite mine in east-central Texas required neutralization of acid mine pit water. At the start of treatment with hydrated lime (Ca(OH)2) on March 28, 2000, the pond had partially filled with 913,000 m3 (740 acre ft) of pH 4.9 water. Another 666,600 m3 (540 acre ft) of pH 3.3 water was pumped in from an adjacent pit (PitA3P- 1). By the end of the project (July 5, 2000), a combined volume of 1,579,000m3(1,280ac-ft or 417milliongal) in PondA3P-2 had been successfully treated to pH 8.2 with 431 t (475 st) of lime. This was two to three times more neutralizer than laboratory trials and detailed chemical titration had predicted. The main reasons for the difference are thought to be the inadequate representation in a laboratory setting of some of the chemical reactions that occurred in the field, including incomplete mixing of reactants, reactions with clay minerals in the pit walls, the kinetics of biologically-mediated reactions and the kinetics of aluminum oxyhydroxide polymerization (aging).

Jan 1, 2002

By B. M. Nyombolo

Mintek has been involved in the development of bioleach technology for many years, initially for refractory gold extraction, and more recently also for base metals extraction. Mintek, in collaboration with its Australian technology partner (BacTech), is the technology provider for the Beaconsfield refractory gold bioleach plant. At present Mintek, BacTech and Peñoles are constructing a demonstration bioleach plant for copper-zinc concentrate treatment. Bioleach liquors produced in all applications have to be neutralized. In the copper-zinc case, neutralization is required for bleeding iron from the circuit and for adjusting the pH value ahead of solvent extraction. In the refractory gold bioleach circuit, neutralization is required for producing a stable iron-arsenic residue. Some of the challenges encountered in each of these neutralization applications are discussed, and examples of results obtained are presented.

Jan 1, 2000