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By Hector Carreon

Due to elevated temperatures, excessive stresses and severed corrosion conditions, turbine engine components are subject to creep processes that limit the components life such as a turbine bucket. The failure mechanism of a turbine bucket is related primarily to creep and corrosion and secondarily to thermal fatigue. As a result, it is desirable to assess the current condition of such turbine component. By using eddy current images, it was shown that the eddy current technique could be used to monitor creep damage in a nickel base super-alloy. The eddy current results also show a significative variation in the electrical conductivity on the creep damage zone with respect to the background zone in nickel-base super-alloy samples. In addition, the absolute thermoelectric power coefficient show also a strong dependence associated with creep damage in nickel base super-alloy samples using a copper hot tip reference. The eddy current and thermoelectric power techniques seems to show promise as nondestructive evaluation tools for monitoring the level of creep damage in metallic alloys.

Jan 1, 2010

By Duk-Won Park

Since geologic anomalies are one of the governing factors of instability of underground openings, it is extremely important to know in advance the locations and extent of these anomalies such as faults and weak zones. At an underground coal mine in the Black Warrior Basin, core drilling was conducted to find a fault in an area where they were planning to locate longwall panels and possibly a shaft. Core logging did not provide sufficient information to delineate the exact location, strike, and dip of the fault. However, a high resolution reflection seismic study successfully solved this problem. This experience proved the seismic exploration technique could be used for premining coal exploration by reducing drilling costs and providing more accurate and detailed information about geologic anomalies.

Jan 1, 1988

By Ravi Sahu, Ankit Jha, Sudarshan Rajagopal, Purushotham Tukkaraja

Geologic structures are one of the crucial parameters in blast design. Structural geology and rock properties influence drilling patterns, blast layout, and initiation systems. A comprehensive understanding of structural characteristics is essential to achieve the desired blasting objectives. This paper aims to identify geologic features in a bench using images obtained from the drone.

Feb 1, 2020

By Hector Carreon

This paper presents experimental data for the magnetic field produced by thermoelectric currents around uncracked hard-alpha inclusions in a Ti?6Al?4V specimen under external thermal excitation for different nitrogen content ranging from 1.6% to 5.9%. According to our preliminary results, the magnetic flux density measurements were found to be rather sensitive to changes in nitrogen content with the exception of the intrinsic material background magnetic signal that affected deeply the detectability of inclusions and imperfections in non-contacting thermoelectric measurements. Hopefully, these preliminary results can help identify a non-destructive test method that can detect material inclusions with a level of nitrogen that could be detrimental to Ti?6Al?4V alloy components.

Jan 1, 2009

By Joseph L. Condon

The U.S. Department of the Interior, Bureau of Mines, is doing research on methods to obtain premining information on problems such as detection of old mine workings, abandoned oil wells, channel sands, faults, and other geologic features that are undesirable from a production or safety aspect. Techniques being developed to acquire the necessary information include seismic and electromagnetic. The seismic methods include high-resolution seismic and acoustic methods applied from the surface and inseam channel waves applied under- ground. The electromagnetic, or radar, methods are applied from the surface, from boreholes, and from the working face underground. All of these methods are useful under certain site-specific conditions, but none of these methods is universally applicable for all mining conditions.

Jan 1, 1980

By Arnaud Faucher, Claude Bazin, Marilène Renaud, Mohamed Mai Manga

"Grinding circuits are operated in the presence of disturbances due to ore changes and equipment malfunctions. One of the challenges faced by the operators is the detection of a disturbance, the identification of its origin and of the action needed to minimize its impact on the process. Hydrocyclones overloading, worn or partly plugged apexes of the units of a nest, surging and internal liner detachment may cause a loss of classification capacity and quality. Recovery losses in downstream leaching or flotation circuits justify the effort in developing techniques for the detection of hydrocyclone malfunction. Two approaches for the detection of hydrocyclone fault are presented in this paper. The first proposed method uses the conventional sensors available in a grinding circuit allowing real time detection of hydrocyclone abnormal operation. The second technique is based on the use of vibration sensors installed on the hydrocyclone of a nest. This last technique enables the classification of the hydrocyclone malfunction as surging, overloading and plugged apex. The two techniques can be combined to increase the robustness of the problem detection.INTRODUCTIONHydrocyclones are used for many purposes ranging from water separation, fines removal and obviously for the preparation of the feed for subsequent concentration circuits in a mineral processing plant. The strategic role of the hydrocyclones for the preparation of the feed to the concentration circuit is often underestimated because of their simplicity of operation and robustness (Napier-Munn et al., 1996; McIvor, 1988). It remains that despite their simplicity, hydrocyclones have been studied extensively from 1970 to 1990 with the objective of developing mathematical models of their operation to be incorporated into grinding circuit simulators (Napier-Munn et al., 1996; Lynch, 1977; Flintoff et al., 1987). Such a development effort is already an acknowledgement of the importance of these units in a mineral processing plant. Several authors (Del Villar et al., 1996; Plitt and Kawatra, 1979; Casali et al., 1998) proposed soft sensors to estimate the classification efficiency of hydrocyclones using sensor measurements. However, few of the proposed sensors are used in the industry. More recently several papers were published to discuss the application of 3-D modelling of the hydrocyclones (Narasimha et al., 2005; Cullivan et al., 2004) and sensors (Bond et al., 1999; Neesse et al., 2003; Hou et al., 1998; Olson and Waterman, 2004; Williams et al., 1995; Willams et al., 1997) to help in the detection of hydrocyclone malfunction."

Jan 1, 2010

By Xie Qiang

It is known that the stress state inside rock discs under Brazilian tests is neither simple compression nor tension, but a complex combination of both stress types, as well as for a beam subjected to bending, where compressive and tensile stresses are separated by the neutral axis. In this study, AE generation was obtained on pure white marbles from south Portugal that were submitted to both Brazilian and bending tests. Main questions to be answered were: (1) Does Kaiser effect exist in tests which create complex distributions of tension and compression? (2) Are there any meaningful differences in AE behavior of marble under Brazilian and bending tests?

May 1, 2009

By J. L. Shultz

The investigation of organic compounds in respiratory coal dust by high-resolution mass spectrometry has shown that the organic components differ (1) with particle size, (2) with rank of coal, (3) in coals of same rank from different coal seams, and (4) in respirable mine dust and respirable coal dust (prepared) from the same mine. The eight respirable mine dusts studied are from coal seams in the Appalachian region where the incidence of coal workers' pnumoconiosis is relatively high. The molecular weights of the major organic components vary greatly among these respirable mine dusts; three show strong concentrations of polynuclear aromatic hydrocarbons.

Jan 1, 1972

By N. S. Dalal, B. Jafari, F. Y. H. Green, V. Vallyathan

"Using electron spin resonance (ESR) spectroscopy we have carried out a systematic study of the measurement of concentrations of organic free radicals in freeze-dried lung tissue. Sixty-six tissue samples were obtained from the National Coal Workers' Autopsy Study (NCWAS). The samples were characterized in terms of the concentration per gram of the free radicals, and examined in relationship to the histopathological parameters such as age, numbers of years in the mining atmosphere, the degree of disease, and smoking history. Preliminary results indicate a positive correlation between the free radicals in the lung tissue and the severity of pneumoconiosis.IntroductionThis paper summarizes our electron spin resonance (ESR) measurements of the concentration of organic free radicals in the freeze-dried lung tissue of autopsied coal miners. This work was undertaken because it is well known that most coals contain carbon-centered organic free radi¬cals,(1,2) and recent studies from our laboratories indicated that reactive free radicals might play a role in the biochemi¬cal mechanism of coal workers' pneumoconiosis (CWP) (3) Thus we obtained 144 lung-tissue samples from the Nation¬al Institute for Occupational Safety and Health (NIOSH) in Morgantown. The samples came from the data bank of the National Coal Workers' Autopsy Study (NCWAS). This selection was unique because fairly detailed histopathologi¬cal information was available for each sample.Other recent studies in this field are those of Ohta et al (4) who conducted ESR measurements on black dust deposited in autopsied human lungs, but no coal miner was included therein. Earlier Retcofsky (2) had examined one lung-tissue sample via ESR spectroscopy and had concluded that the free radicals in the lung tissue reflected simply the fusain (narrow component) ESR signals. The present study was thus undertaken to complement the earlier work (2-4) and the results indicate a positive correlation with the disease severity fairly strongly."

Mar 1, 1989

By A. H. Rhoades, D. T. King, R. J. Gray

Tests and methods of detecting oxidized coal are studied. Particular emphasis is placed on the microscopically discernible changes that accompany the chemical and physical changes affecting the technological properties of coal during oxidation. Coal samples were used from the low-volatile Pocahontas No. 3 Seam, McDowell County, W.V., and from the high-volatile Cedar Grove Seam, Mingo County, W.V. Tests conducted include: carbonization, free-swelling index, phase inversion, froth flotation, Gieseler plasticity, oxygen by difference and by neutron activation, volatile matter, pH of coal slurries, alkali solubility, and electrostatic charge.

Jan 1, 1977

By R. J. Gray

Oxidation of coal can best be described as a process of deterioration, since most of the uses of coal, such as for fuel and by-product chemical and coke production, are adversely affected by coal oxidation, and the inclusion of oxidized coal in most coal products detracts from their value. There are probably many mechanisms of coal oxidation, and the ways oxygen molecules combine as complexes with coal macromolecules to form new substances have been dealt with and speculated upon by many workers1)* but are not dealt with in the present work. This study deals with tests and methods of detecting oxidized coal, with particular emphasis on the microscopically discernible changes that accompany the chemical and physical changes that affect the technological properties of coal during oxidation.

Jan 1, 1974

By N. S. Dalal, B. Jafari, F. H. Y. Green, M. M. Suryan, V. Vallyathan

"Freshly ground and aged anthracite and bituminous coal samples were investigated by electron spin resonance (ESR) spectroscopy to detect the presence, concentration and reactivity of free radicals. Freshly ground anthracite coal produced greater concentration of free radicals than the bituminous coal, and the radical reactivity was also greater for the anthracite. The reactivity of the newly produced free radicals in the anthracite dust correlated with the dust's toxicity. Furthermore similar coal-based free radicals were detected in the lung tissue of autopsied coal miners, suggestive of persistent reactivity by the embedded coal dust leading to the progressive disease process. Results of the studies on the severity of coal workers' pneumoconiosis (CWP) and free radical concentration m lung tissue support this hypothesis.INTRODUCTIONCOAL WORKERS' PNEUMOCONIOSIS (CWP) has been an important topic of concern to miners, mining engineers, epidemiologists and medical researchers for decades. It is well known that the prevalence and severity of CWP differ markedly between different regions and mines despite comparable exposures to respirable dust (REISNER and RoBOCK, 1977; HURLEY et al., 1979). These variations in pathogenicity of coal mine dusts can only partially be explained by differences in the mineral composition and by rank of coal (CHRISTIAN and NELSON, 1978; CHRISTIAN et al., 1979; HATCH et al., 1980). Cytotoxity studies on mammalian cells do not correspond well with the human disease pattern (CHRISTIAN and NELSON, 1978; CHRISTIAN et al., 1979; HATCH et al.,1980; VALL YA THAN et al., 1988). For example, while epidemiological studies of coal miners (REISNER and ROBECK, 1977; HURLEY et al., 1979) indicate that the prevalence of CWP correlates roughly with the rank of coal, such correlation has not yet been established in biological studies, despite considerable effort (CHRISTIAN and NELSON, 1978; CHRISTIAN et al., 1979; HATCH et al., 1980). In order to help resolve this apparent paradox we have undertaken a systematic study to evaluate whether any correlation exists between the concentration of free radicals present in fresh or aged coal dusts from two well-characterized U.S. coals. This study was also prompted in part by observations reported in the literature that organic free radicals can be formed on the surface of freshly fractured coals (LEBEDEV et al., 1978; DULIBA and CLARKSON, 1984). In particular, ARTEMOV and REZNIK (1980) noted that the decay lifetimes and, hence, the reactivity of the organic free radicals formed on crushing Russian coals, .depended"

Jan 1, 1989

By A. Fahr, T. Byczkowski, M. Genest, C. Mandache

"Layered aluminum structures assembled with rivets are often found in aircraft fuselage. These structures are exposed to various mechanical loads and a range of environmental conditions that contribute to their ""aging"". The rivet holes are the source of stress concentrations and the location of crack nucleation and growth. Detection and sizing of second and third layer cracks under fasteners represent a major challenge for non-destructive evaluation (NDE) technicians. Electromagnetic methods are currently the most feasible NDE techniques that are capable of successfully inspecting such structures. However, different probe lift-offs, due to protruding rivets and paint coatings, or interlayer separations, due to sealant or air gaps, pose difficulties in detecting and sizing cracks. In addition, the use of conventional eddy current technique is limited due to the inverse relationship between the probe spatial resolution and its field penetrability. In this paper, we present an alternative approach that overcomes the previously mentioned shortcomings by using pulsed eddy current excitation and magnetoresistive sensor pick-up. Various signal manipulations are employed for defect detection and quantification."

Jan 1, 2006

By J. ?H. Chen

Remote sensing technologies have been applied to numerous fields and already benefited those in improving efficiency and recognition accuracy. Change of streamflow can be devastating to residents living along riversides. Still it is difficult to detect such deathly change in large area scale by traditional field surveys. The research aims at provision of detecting streamflow change using a mathematic method based on remote sensing technologies. The scope is on remote sensing stream images taken during the invasion by Typhoon Morakot, bringing one of the major catastrophes to Taiwan. An algorithm was adopted to deal with streamflow recognition on RGB images. The algorithm is capable of yielding change measured by meters and illustrates how main streams shift. An practical example for the Baolai area surrounded by Laonong stream shows that the streamflow moved 88 meters (36 pixels) toward the downtown area during the invasion by Typhoon Morakot. In addition, the results highlight all dangerous zones (scouring zones) for residents to prevent further potential damage.

Aug 1, 2013

By Rambabu Pothina, Rajive Ganguli

The economics of a mineral processing circuit is dependent on the numerous sensors that are critical to optimization and control systems. The sheer volume of sensors results in mines not being able to keep the sensors calibrated. When a sensor goes off calibration, it results in errors which can severely impact plant economics. Normally, these errors are not detected until the error grows and becomes a large (“gross”) error. Undetected errors are not remedied until the next calibration, which on average are a year apart Beamex (2019) [1] across all industries. Classical statistical methods that depend on linear relationships between input and response variables are typically used to find gross errors, but are not effective for highly non-linear and non-stationary operations in mining and mineral processing industries. Calibration of sensors is time-consuming and generally requires physical intervention that results in equipment downtimes causing production losses. Therefore, in situ detection methods are warranted that do not require physical removal of sensors to compare with standard measurements or well-calibrated sensors. Data-mining based techniques and algorithms have high success rate in tackling such problems. This paper presents results from a groundbreaking multi-year big data research project whose goal was to develop data-mining-based original techniques in a multi-sensor environment to identify when sensors start to stray, rather than wait for errors to grow. The carbon stripping (gold) circuit in the Pogo Mine of Alaska was selected for this project. Maintaining strip vessel temperatures at optimum levels is crucial for maximizing gold recoveries at Pogo, and hence the focus was on the temperature sensors of the two strip vessels. An automated algorithm to detect errors in strip vessel temperature sensors was developed that exploited data from multiple sensors in the strip circuit. This algorithm was able to detect with a high success rate when bias errors of magnitude as low as ± 2% were artificially induced into strip vessel temperature data streams. The detection times of about 1 month were much less than the industry average calibration frequency of 1 year. Thus, the algorithm helps detect errors early, without stopping the circuit. The algorithm alarms can serve as triggers when calibrations are done. Experimental methodology along with results, limitations of algorithms, and future research are also presented in this paper.

By Rambabu Pothina, Rajive Ganguli

The economics of a mineral processing circuit is dependent on the numerous sensors that are critical to optimization and control systems. The sheer volume of sensors results in mines not being able to keep the sensors calibrated. When a sensor goes off calibration, it results in errors which can severely impact plant economics. Normally, these errors are not detected until the error grows and becomes a large (“gross”) error. Undetected errors are not remedied until the next calibration, which on average are a year apart Beamex (2019) [1] across all industries. Classical statistical methods that depend on linear relationships between input and response variables are typically used to find gross errors, but are not effective for highly non-linear and non-stationary operations in mining and mineral processing industries. Calibration of sensors is time-consuming and generally requires physical intervention that results in equipment downtimes causing production losses. Therefore, in situ detection methods are warranted that do not require physical removal of sensors to compare with standard measurements or well-calibrated sensors. Data-mining based techniques and algorithms have high success rate in tackling such problems. This paper presents results from a groundbreaking multi-year big data research project whose goal was to develop data-mining-based original techniques in a multi-sensor environment to identify when sensors start to stray, rather than wait for errors to grow. The carbon stripping (gold) circuit in the Pogo Mine of Alaska was selected for this project. Maintaining strip vessel temperatures at optimum levels is crucial for maximizing gold recoveries at Pogo, and hence the focus was on the temperature sensors of the two strip vessels. An automated algorithm to detect errors in strip vessel temperature sensors was developed that exploited data from multiple sensors in the strip circuit. This algorithm was able to detect with a high success rate when bias errors of magnitude as low as ± 2% were artificially induced into strip vessel temperature data streams. The detection times of about 1 month were much less than the industry average calibration frequency of 1 year. Thus, the algorithm helps detect errors early, without stopping the circuit. The algorithm alarms can serve as triggers when calibrations are done. Experimental methodology along with results, limitations of algorithms, and future research are also presented in this paper.

By Rambabu Pothina, Rajive Ganguli

The economics of a mineral-processing circuit is dependent on the numerous sensors that are critical to optimization and control systems. When a sensor goes off calibration, it results in errors that can have a severe impact on plant economics. Classical statistical approaches fail to detect when errors are “subtle,” unless they grow and become large enough to be “gross,” whereas methods based on signal processing and artificial intelligence fail in dealing with the complexity of process fluctuations. Due to the sheer volume of sensors, undetected errors are not remedied until the next calibration, which on average are a year apart across all industries [1]. This research aims to detect such subtle errors (2 percent bias) in shorter time spans of about a month — rather than wait for errors to grow — using innovative data-mining techniques and algorithms.

By M. Z. Naghadehi, F. F. Nasategay, J. Sattarvand, R. Battulwar, E. E. Gohari

After the recent increase in catastrophic failures of tailings dams such as the Burmadinho, Brazil failure in 2019, the mining industry has actively engaged in the stabilisation of tailings dams. Most of the tailings storage facility (TSF) guidelines suggest a laborious action that can be facilitated through imaging by unmanned aerial vehicles (UAVs). Although UAVs are not unknown to most mining operations, automated rill detection through aerial image processing is a new concept. This study utilises images from sites in the U.S. and employs semantic segmentation with a UNet architecture to detect location and size of these rills. The developed model can be used as a tool for monitoring surface erosion of TSFs.

Sep 1, 2021

By S. Banisi, M. R. Yarahmadi, S. R. Mortazavi

Nowadays the development of automatic on-line sampling systems has enabled large amounts of data to be available to plant managers. These data are inevitably prone to random and sometimes systematic errors. Data reconciliation methods are used to deal with random errors. In these methods, the measured data are adjusted in accordance to their error, so that the mass conservation law is satisfied at the same time. Measured data may also contain systematic errors, using these data in mass balancing software will result in inaccurate or even irrational results. In this paper three methods for detecting systematic errors (global test, node constraint test and maximum power constraint test) were presented. A step by step method to locate the systematic errors was demonstrated. Then, the new version of Movazen©, a mass balancing software, was introduced and used to locate systematic errors in the measurements taken from the Sarcheshmeh pilot plant copper flotation circuit. Keywords: Mass balancing, data reconciliation, random error, systematic error

Jan 1, 2020

By N Winkelmann

Underground coal heating is a significant hazard in some coal mines. The broad principles of how underground coal heatings occur is reasonably known, however the technique for locating the heatings in often inaccessible goaf has been proved to be difficult. Since 2002, CSIRO, with the support of the Australian Coal Association Research Program (ACARP) has been undertaking investigations to apply and develop innovative surface-based radon technique for locating the underground heatings in Australia. This paper presents the progress of the investigation.

Jan 1, 2005