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By Manuel Gomez

This, report presents correlative data on ash and sulfur forms for Pittsburgh seam coal from Greene County, Pa. Prediction equations were developed for sulfur form distributions and washability characteristics. The equations developed were evaluated with an independent set of exploration core samples. It was demonstrated-that both sulfur distributions and washability characteristic's can be predicted from exploration core data ahead of mining. This information can improve the efficiency of selective mining techniques for the production of a uniform metallurgical coal product.

Jan 1, 1967

By Vladimir Adamek

This paper describes the Bureau of Mines development of a novel subsidence prediction methodology suitable to the mining and geologic conditions in the Northern Appalachian Coal Region. It describes the computation of vertical and horizontal movements, inclination, curvature, and horizontal strains. The substance of this method is the separation of the effects of lithology by introducing a correlation between hypothetically homogeneous overburden and existing lithologic conditions, while providing for different mining conditions, i.e., underground geometry and overburden thickness. The effects of lithology have been expressed in the form of a variable subsidence coefficient within the subsidence trough. The subsidence coefficient is considered a constant for other predictive methods. Field data from 16 test sites at 11 Bureau longwall panel studies were used in the analysis. For each panel, the characteristics of the variability of the subsidence coefficient were defined. Regression analysis of the subsidence coefficients from all test sites on their locations relative to the edges of their respective panels yielded a third-degree polynomial equation. The results from additional longwall panel studies, not included in the regression analysis, were used to prove the validity of this method. To facilitate the use of this precalculation methodology, a computer program was written in BASIC for use on a personal computer.

Jan 1, 1987

By Mao Bai

In predicting the integrity of under-mined structures, surface horizontal displacements and curvatures are frequently of greater importance than the more homogeneous vertical displacements. Accurate evaluation of mining induced subsidence and horizontal displacement further requires that the predictive model adequately represents the subsurface geology, mining geometry and extraction sequencing. The following documents an extension of the Subsidence Prediction and System Identification method (SPASID) to the evaluation of horizontal strains. Comparisons are made between predictions from SPASID and two other empirical methods against measured data from a case study. The system identification method is illustrated to predict horizontal displacements and strains most consistently with the in situ data.

Jan 1, 1989

This paper presents a two-dimensional numerical procedure which incorporates concepts related to the mechanics of strata deformation' as well as' empirical indices associated with subsidence engineering. Regional deformation data were employed in order to establish overburden deformation zones, while material properties were kept constant. Scaling of final displacements was based on empirically predicted values for subsidence and strain for the Appalachian coalfield. The model was validated with a number of regional case studies and predicted subsidence and strain curves were compared to those obtained using the semi-empirical influence function formulation developed by VPI&SU.

Jan 1, 1988

By W. L. Zhong

Probability function integration method is one of the influence function method. It is a widely accepted method in many mining districts in China and Poland mainly because its theory and formulae can well-represent the surface subsidence basins due to longwall mining of flat or near-flat seams. This paper introduces the basic theory of the probability function integration method and determines its 4 basic parameters by analyzing the measured subsidence profiles in eight longwall panels in Northern Appalachian Coal field. It also discusses the two methods for determining the 4 parameters. Finally the acquired parameters are used to predict the subsidence and deformation of the houses for damage assessment. Comparisons are made between the measured and the predicted subsidence. Probability Function Integration Method The probability function integration method for predicting surface movements and deformations is widely used in China and Poland. The governing equation of the method contains five parameters: subsidence factor, a, offset of inflection point, d, tangent of the major influence angle, tan fl, horizontal movement constant, b, and major dispersion angle e (e = 90. for flat seams). Its applicable range iis much larger including seams less than 30' from the horizontal, critical and noncritical mining and soft to hard overburden.

Jan 1, 1986

By Liguang Wang

The froth phase in flotation has received much less attention than the pulp phase despite the fact that froth stability plays an important role in determining flotation performance. Froth stability is largely determined by the stability of thin foam films, which in turn has a strong correlation with the critical rupture thickness (Hcr) of the films. In the present work, a new approach to predicting the Hcr values of thin foam films in flotation froth phase was proposed and examined. The Hcr values were calculated using an advanced capillary wave model with input parameters being surface tension, film radius, capillary suction pressure, and intersurface force. The intersurface force was estimated by using the Stefan-Reynolds lubrication approximation to fit the measured thinning rates of single foam films formed in a Scheludko ring cell, where the film radii were less than 50 microns and were kept constant during the film-thinning process. A comparison between the predicted and experimental Hcr values was made for the films with the use of sodium chloride as background electrolyte and in the presence of various frothing reagents such as methyl isobutyl carbinol (MIBC), polypropylene glycol with an average molecular weight of 400 (PPG400), and sodium dodecyl sulphate (SDS), at different concentrations and film radii. It was found that the model correctly predicted the trends of Hcr-versus-concentration and Hcr-versus-film radius curves. Moreover, the predicted Hcr values matched well with the experimental ones of the foam films containing either SDS or PPG400. For the foam films containing MIBC, however, the model overpredicted the Hcr values. The reason for this discrepancy was discussed.

Jan 1, 2016

By P. Myllykoski, J. Nylander, A. S. Korhonen, J. Larkiola

The coefficient of friction and the deformation resistance have been determined from the measured rolling parameters by applying the Bland-Ford-Ellis rolling force model and the artificial neural network model (ANN). The dependence of friction on the parameters such as reduction, rolling speed, front and back tensions, contact pressure and deformation resistance have been studied by the ANN model. Measured data of over 10 000 coils have been used in the training stage. Various dependencies have been examined and the results show that the friction coefficient does not depend much on the rolling speed. The reduction and deformation resistance seem to have the strongest effect on the friction coefficient. A friction model based on neural computing has been included into the existing rolling force model.

Jan 1, 1994

By K. Aoki, Y. Mito, S. Shirasagi

The authors developed the TBM Excavation Control System (the TBM Navigator), in order to realize the advanced observational construction. During the excavation using this system, the rock strength value is immediately calculated from the TBM driving data as an index of rock properties, and the drill energy coefficient is also calculated from the drill logging data that is carried out using the probe hole ahead of the tunnel face. In this study, a new, geostatistical simulation-based method is proposed for the automatic prediction of the rock properties ahead of the tunnel face with good precision, using both the drill logging data and the TBM driving data. The applicability of this method is validated through the prediction at the actual tunnel excavation that passes through the faults. Les auteurs ont mis au point un Système de Contrôle de forage avec la machine à pleine section (Navigateur de la machine à pleine section), en vue de réaliser un système de surveillance plus efficace. Avec ce système, la résistance de la roche est immédiatement calculée, au cours du forage, à partir des données de conduite de la machine, comme indice des propriétés de la roche, et le coefficient d'énergie de la foreuse est également calculé à partir des données de log de forage collectées dans un trou de sonde foré dans le front de taille. Dans cette étude, on propose une nouvelle méthode basée sur la simulation géostatistique qui permet de prévoir d’une manière automatique, des propriétés de roches se trouvant derrière le front de taille avec une précision satisfaisante, à l’aide des données de log de forage et celles de conduite de la machine à pleine section. Cette méthode s’est avérée suffisamment précise au cours du forage d’un tunnel réel qui traversait une zone de failles. Die Autoren entwickelten das TBM Ausschachtungssteuerungssystem (den TBM Navigator), um noch besser überwachte Bauarbeiten zu ermöglichen. Unter Einsatz dieses Systems wird die Gesteinsfestigkeit während der Ausschachtungsarbeiten unmittelbar aus den TBM Antriebsdaten als ein Index für die Gesteinseigenschaften berechnet. Gleichzeitig wird auch der Bohrenergiekoeffizient aus den Bohrlogdaten berechnet, die an Hand der Probebohrung am Stirnende des Tunnels erhalten wurden. In der vorliegenden Studie wurde ein neuartiges, auf geostatistischen Methoden basierendes Verfahren für die automatische und angemessen genaue Vorhersage der Gesteinseigenschaften am Stirnende des Tunnels vorgeschlagen, für das sowohl die Bohrlogdaten als auch die TBM Antriebsdaten verwendet werden. Die Anwendbarkeit dieser Methode wird durch die Vorhersage bei einer tatsächlichen, durch Verwerfungen verlaufenden Tunnelausschachtung bewiesen.

Jan 1, 2003

By A. Karadogan, G. Tuncer, A. Kahriman, S. Gorgun

This paper presents the results of ground vibration measurements induced by blasting operations at five different sites located close to the residential areas. Within the scope of this study, ground vibration components were measured for all blast events over a long period of time in order to predict peak particle velocity and determine the slope of the attenuation curve for five sites. During the study, while the parameters of scaled distance were being recorded carefully, suitable monitors for all blast events of five blasting sites measured the ground vibration components. At the end of evaluation of the data pairs, an empirical relation, which gives an average line at 95% confidence level, and upper bound 95% prediction line with a good correlation coefficient was established and suggested for each site

Jan 1, 2003

By K. Oraee

Rate of penetration of Tunnel Boring Machines (TBM) has a significant role in the planning, measurement of productivity and performance of any tunneling project. In this paper, the application of Adaptive Neuro Fuzzy Inference System (ANFIS) in prediction of TBM penetration rate is evaluated. For this purpose, a database including Rock Quality Designation (RQD), Uni-axial Compressive Strength (UCS) of the rock, the Distance between Planes of Weakness (DPW) in the rock mass, and empirical data regarding rate of penetration of TBM from several tunneling projects are collected. The Rate of Penetration is then estimated by using ANFIS. These results are then compared with measured TBM penetration rates (actual data). It is concluded that ANFIS can be applied successfully for such purpose and result in high accuracy for prediction for the rate of penetration of TBM. The method provided in this paper can assist the mining engineer to estimate the performance of tunneling accurately.

Jan 1, 2012

By R. M. Morris, G. A. Matthesius, M. J. Desai

Slow pyrolysis experiments were caried out on samples of coal from north em Natal having a volatile mater content ranging between 10 and 25 per cent by mass and a constant particle-size distribution. The tests were conducted at atmospheric pressure in a helium atmosphere, and ata heating rate of 10 KImin and a final temperature of 1220 K. The pyrolysis products were taken as coke and volatile mater (tar and eight other species), and tMir respective yields were measured. The experimental results were used in the modification of an existing mathematical model and in the derivation of new corelations for some of the parameters of the model, so that the volatile products of a coal can be predicted from ultimate and proximate analyses of the coal. The pyrolysis tests showed good agreement between the model predictions and the experimental results.

Jan 1, 1987

By A. McLean, W. Q. Chen, W. Yan, M. Barati, Y. D. Yang

In order to maximally remove the harmful impurities phosphorus and reduce the loss of valuable elements from Si-based alloy for production of solar-grade silicon, thermodynamic properties of the Si-based alloy were studied. In this work a new model, the Molecular Interaction Volume Model (MIVM) derived from statistical thermodynamics, was successfully used in the Si-based and Si-Fe-based alloy. The calculation started from binary Si-Fe, Si-P and Fe-P systems, then the thermodynamic parameters of ternary systems could be derived based sub-binary systems. Using the activity coefficients of the elements in the Si-based alloys and the saturated vapor pressure of the pure substance of the components, mass fraction of P during vapor-liquid equilibrium can be calculated. The calculated thermodynamic parameters were compared with experimental data available in different publications, and it is found that the thermodynamic parameters calculated using MIVM model are in good agreement with the measured results. The calculated result shows that the mass fraction of phosphorus during vapor-liquid equilibrium of Si-Fe-P melt increases with the increasing phosphorus content and decreasing temperature. This is an important direction for experimental practice in vacuum or low-pressure EML refining of ferrosilicon for potential production of solar-grade silicon without significant vaporization loss of valuable element.

Jan 1, 2015

By Xiaodong Yu, Jinxia Zhang, Fusheng Niu, Hongmei Zhang

The three-dimensional (3D) fractal dimension is an important parameter to analyze the 3D structure and flocculation effect of the hematite flocs. In this work, the 3D fractal dimension of hematite flocs was predicted by establishing the particle swarm optimization (PSO) algorithm optimized with the back propagation (BP) neural network (PSO-BP). The proposed model considers four factors, namely, the flocculant quantity, flocculation time, stirring speed, and temperature, as the input parameters. We normalize the input data during the preprocessing stage. The BP neural network was optimized by using the PSO algorithm for realizing the 3D fractal dimension. The prediction accuracy and effectiveness of the model were evaluated. The experimental results showed that the predictions performed by the proposed PSO-BP neural network were better than that of BP neural network. In addition, the root mean square error (RMSE), mean squared relative error (MSRE), mean absolute error (MAE), and mean absolute relative error (MARE) of the proposed model are lower, compared with the errors of the BP network. Similarly, the measurement coefficient R2 of the proposed model is higher, compared with the BP network. The maximum absolute error of the model is 0.0772, the maximum relative error is 0.02405, and the regression coefficient r is 0.98592. These results showed that the proposed model has a good performance and high prediction accuracy. When verifying the practicability and the effectiveness of the proposed PSO-BP model, the prediction results of 10 groups of hematite flocculation experimental data under different conditions showed that the prediction value of the proposed PSO-BP model was closer to the ground truth as compared to the BP model. Therefore, the proposed model is suitable for predicting the 3D fractal dimension of hematite flocculation.

Oct 3, 2022

By Murray G

This paper outlines the processes involved in acid generation from sulphides and the key factors in identifying and predicting time dependent changes in the nature of acid mine drainage (AMD). The initial characteristics of mine water; whether drainage from exposed mining faces, runoff and seepage from waste dumps or seepage from process tailings; do not necessarily reflect the long term potential for AMD. The ultimate quality of mine drainage is a function of the interaction of many chemical, physical, biological and engineering (operational) factors. Techniques are available that can predict the qualitative nature of acid generation and mine drainage quality but accurate quantitative predictions are essentially impossible at the present time. As more and more mining operations exploit sulphide ore, the AMD and acid waste issue will become a. far greater concern. The significance of time dependent factors in the production of acid mine drainage-.is not just restricted to particuL.ar mine sites but needs to be recognised by the mining industry as a whole.

Jan 1, 1988

By Christian Ludwig, Virginie Silberstein, Rizlan Bernier-Latmani, Simona Regenspurg

"Residues from waste incineration (bottom ashes) form large amounts of heavy metal-rich waste. Theoretically, heating and the addition of chloride to the ashes could remove these metals by formation of volatile metal chlorides. The remaining heavy metal-poor ashes could be used, for example, as cement additives. In the proposed process of our study we used bypass dust from the cement industry as chloride additives to the bottom ashes. We aim to determine experimentally the optimal conditions to remove heavy metals from ashes and to simulate this process by thermo- chemical modeling. In our experiment we investigate heavy metal flows by heating of differently composed mixtures of bottoms ash and bypass dust in dependence of different furnace temperatures (500-1200 °C). Furthermore, we investigate the reactivity (leaching characteristic) of the resulting product in order to predict if secondary residues can be landfilled in agreement to Swiss regulations for inert materials.IntroductionThe incineration of garbage is generally considered as the most environmentally friendly method for the reduction of municipal solid waste. This is because, compared to traditional storage of waste on landfills, incineration produces less greenhouse gases and destroys toxic organic compounds. The released exothermic energy can be recuperated and the residues from the combustion chamber (bottom ash) are strongly reduced in volume. However, they still form about 600 kt of waste per year in Switzerland, which needs to be landfilled. However, these residues could be potentially used as construction materials after mechanically recovering ferrous and non-ferrous metals. However, the resulting ashes (discharged dry or wet) still contain increased amount of heavy metals (exceeding state regulation) requiring them to be finally deposed on a hazardous waste site. A thermal treatment of these ashes could enable both, to separate and recuperate the remaining heavy metals and an inorganic heavy-metal poor material. The evaporation of metals depends on temperature and can be lowered by addition of chlorides, which would lower the boiling point of most metals by formation of metal chlorides (table 1)."

Jan 1, 2008

By Christian Ludwig, Virginie Silberstein, Rizlan Bernier-Latmani, Simona Regenspurg

"Residues from waste incineration (bottom ashes) form large amounts of heavy metal-rich waste. Theoretically, heating and the addition of chloride to the ashes could remove these metals by formation of volatile metal chlorides. The remaining heavy metal-poor ashes could be used, for example, as cement additives. In the proposed process of our study we used bypass dust from the cement industry as chloride additives to the bottom ashes. We aim to determine experimentally the optimal conditions to remove heavy metals from ashes and to simulate this process by thermo- chemical modeling. In our experiment we investigate heavy metal flows by heating of differently composed mixtures of bottoms ash and bypass dust in dependence of different furnace temperatures (500-1200 °C). Furthermore, we investigate the reactivity (leaching characteristic) of the resulting product in order to predict if secondary residues can be landfilled in agreement to Swiss regulations for inert materials.IntroductionThe incineration of garbage is generally considered as the most environmentally friendly method for the reduction of municipal solid waste. This is because, compared to traditional storage of waste on landfills, incineration produces less greenhouse gases and destroys toxic organic compounds. The released exothermic energy can be recuperated and the residues from the combustion chamber (bottom ash) are strongly reduced in volume. However, they still form about 600 kt of waste per year in Switzerland, which needs to be landfilled. However, these residues could be potentially used as construction materials after mechanically recovering ferrous and non-ferrous metals. However, the resulting ashes (discharged dry or wet) still contain increased amount of heavy metals (exceeding state regulation) requiring them to be finally deposed on a hazardous waste site. A thermal treatment of these ashes could enable both, to separate and recuperate the remaining heavy metals and an inorganic heavy-metal poor material. The evaporation of metals depends on temperature and can be lowered by addition of chlorides, which would lower the boiling point of most metals by formation of metal chlorides (table 1). 745"

Jan 1, 2008

By R. G. Shen

The relation curve of strain and acceleration is presented in this paper. The steel pipe pile of 609.6 mm in external diameter driven by IDH-45 diesel motor hammer on the esturial shoal of Yangtze River. The ultimate bearing capacity of pile is analyzed by one dimensional wave equation and the effect of computational parameters of the equation is discussed.

Jan 1, 2010

By Vahid Tajdar

The behavior similarity between pile and cone penetration test, has guided the researchers to use the CPT results in prediction of piles bearing capacity. In this article, artificial neural networks have been used to study about relation between cone tip and sleeve friction strength obtained from cone penetration test and ultimate pile bearing capacity, obtained from static pile load test. Artificial neural networks "ANNS" is a form of artificial intelligence. ANN tries to dramatize the human nerves & brain system biology structure in self-structure. In this article, the results of relevant studies about the prediction of ultimate pile bearing capacity in especial area of soils are presented by use of back - propagation neural networks. The results of static pile load test up to failure on 63 piles with closed end and CPT test results in the place of construction these piles use in order to confirm and creation ANN model. Comparing predicted ultimate pile bearing capacity by utilizing ANN with the numbers which are predicted by 5 common traditional methods shows that using of artificial neural networks is a suitable method for predicted ultimate pile bearing capacity in identified area of texture of soil moreover it has a better operation in comparison with common traditional methods.

Jan 1, 2006

By Sufi Md Gulzar, L B. Roy

Rock engineering tasks like tunnelling, dam and building construction, and rock slope stability rely heavily on properly estimating the rock’s uniaxial compressive strength (UCS), a crucial rock geomechanical characteristic. As high-quality specimen are not always possible, scientists often estimate UCS indirectly. The primary objective of this paper is to assess the efficacy of long short-term memory (LSTM), K-nearest neighbour (KNN), a combination of particle swarm optimisation (PSO) with an artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) to estimate the UCS of sandstones from Jharia, Dhanbad, India. Point load index (PLI), porosity (n), P-wave velocity (Vp), density (ρ), and moisture content (%) are the parameters used for the present study. Finally, a comparison was made between the various prediction algorithms outputs. The findings of the study validated the effectiveness of computational intelligence methods in forecasting UCS compared to other models used in this paper. The KNN achieves overall the best results, with an R2 of 0.95 for training, 0.94 for testing, and an RMSE of 0.03 for training and 0.05 for testing.

Nov 23, 2023

By M. E. Grimes

A method of predicting uranium extraction rate in underground bacterial leaching of as-blasted ore has been developed. The method is based on the hypothesis that extraction is directly proportional to the volume of a piece of ore that is penetrated by the leaching agent, and that each piece of ore, regardless of size, has been penetrated radially from the external surface to the same depth at a given leaching time. Consequently, given an initial size and assay distribution, extraction should be predictable for a given penetration. Two laboratory bacterial leaching tests under simulated stope leaching conditions were carried out for a periods of 89 and 96 weeks. On completion, the residual ore was screened and analyzed. The information so obtained was employed to provide an elapsed time base for the penetration model, from which predictions of future extraction were made. The finding of solubilized uranium trapped within apparently sol id ore pieces was unexpected. However, it is not recoverable except after crushing, and hence does not affect calculations of underground leaching .

Jan 1, 1974