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By Nemick JA

Recent advances in computer simulation together with field measurements of caving and microseismic activity about longwall panels, has allowed a much better understanding of the caving process and the variability due to geology. The joint research between SCT Operations and CSIRO Division of Exploration and Mining has initiated new methods of computational modelling predicting various caving patterns and strata failure far ahead of the longwall face. This work was validated by field measurements of caving and microseismic activity at the longwall face.The rock fracture distribution and the caving characteristics of a range of strata sections have been simulated by computer methods. Validation studies of the method were addressed together with case studies. The interaction of caving with support convergence and face control is presented. The method allows the simulation of longwall support behaviour under various geological conditions. The system also allows a prediction of the monitoring data, which is best suited to give an early warning of weighting events or signal various key caving characteristics.

Jan 1, 1998

By R. H. Zeng

Many subsidence researchers in the U. S. have developed new empirical function methods to predict subsidence, or attempted to validate some empirical functions developed by foreign researchers for use in the U. S. An attempt is made in this paper to develop a new empirical function to predict a surface subsidence basin due to longwall mining. In the U. S., the emphasis of empirical methods for the subsidence prediction over longwall panels has been in large part on determining the profile functions for the major cross-sections of a subsidence basin. In this approach, the surface monuments are usually laid out along the major cross-sections and thus, the subsidence profiles developed are restricted for subsidence prediction along the major cross-sections. However in practice many monuments or surface structures for which subsidence prediction for damage evaluation is required, are not laid out along the major cross-section of the basin. In such cases, it is difficult to get either the empirical profile function for the cross-sections where these structures are located or to predict their subsidence based on the profile function developed for the major cross-sections. Therefore, there exists a strong need to develop a subsidence basin function with a reasonable degree of accuracy, which can be determined or confirmed by the data of scattered monuments on a surface basin. The same subsidence basin function can also be used for prediction of subsidence for any arbitrary points over a longwall panel. In 1982, He (1) proposed the clastic theory, for which he developed the basic differential equations describing the movement and deformation of overburden due to underground mining, and performed the selection of best solution for the equations. He found that the Weibull dietribution is the optimal solution. The Weibull distribution has three parameters that vary to fit the subsidence profile so well that the profile function derived from it provides a high degree of accuracy in subeidence prediction. However the results

Jan 1, 1986

By Shu DM, Moxon P

Subsidence profiles over longwalls at West Cliff Colliery (WCC) are predicted by three methods, NCB Emperical Method, Salustowicz's Profile Functions and Superposition of Two Critical Profiles. A method of deriving maximum subsidence using local data is also presented. For the first four longwalls, the results are compared with those from the subsidence surveys conducted during and after the extraction of each longwall. Longwall V has not yet been extracted, so the results are for the predicted subsidence profile. Superposition of two critical profiles is carried out in an attempt to check the principle of superposition. In addition, strata movement above the extracted longwalls is discussed.

Jan 1, 1986

This paper reviews the current techniques employed in the prediction of subsidence and how they have been applied to partial extraction systems. The method using influence functions to describe the shape of the profile is investigated in detail and has been adopted to model the subsidence over workings in Huntly. It is compared with the profile methods developed in the United Kingdom and found to predict the subsidence profile more accurately than these methods.

Jan 1, 1991

By Yi Luo

Subsurface strata movements and deformations associated with underground mining activities could cause problems to subsurface structures and water bodies. By incorporating the methods for surface subsidence prediction with the subsidence parameters derived from collected subsurface subsidence data, methods have been proposed for the prediction of final and dynamic subsurface movements and deformations induced by underground longwall mining. Two new' types of strata deformations in the field of subsidence research, vertical and total strains that could he useful in assessing subsidence influences to the subsurface structures and water bodies, have peen introduced.

Jan 1, 2000

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 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

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 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 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 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

By R. G. Reddy

Viscosities of alkaline metal oxide-silicate melts were estimated using our previously developed mathematical model. The structural based model considers depolymerization effects and related breakdown of the silicate network structure, on the addition of metal oxides to the melt. The developed expression: n =4.9x10 9 NO 0 T ½ exp(E/RT) poise was used to calculate the viscosities of various binary silicate systems such as, CaO-SiO,, MgO-SiO, and MnO-SiO,, as a function of temperature and composition. Excellent agreement was obtained between the calculated and experimentally determined viscosities.

Jan 1, 1991

By A H. Bagherieh, S Mesroghli, H Jorjani, S Chehreh Chelgani

The rare earth elements (REEs) assay and recovery in leaching processes is being determined using expensive analytical methods, ICP-AES, and ICP-MS. This paper presents a neural network model to predict the effects of operational variables on the lanthanum, cerium, yttrium, neodymium recovery in the leaching of apatite concentrate. The effects of leaching times: ten to 40 min, pulp densities: 30 to 50 per cent, acid concentrations: 20 to 60 per cent and agitation rates: 100 to 200 rpm were investigated and optimised on REEs recovery in laboratory at a leaching temperature of 60¦C. The obtained data, on the laboratory optimisation process were used for training and testing of a neural network. A feed-forward artificial neural network with 4-5-5-1 arrangement, was capable of estimating the REEs leaching recovery. Neural network predicted values were in good agreement with the experimental results. The correlations of R = 1 in training stages, and R =  0.971, 0.952, 0.985 and 0.98 in testing stages were results for Ce, Nd, La and Y recovery predictions respectively and that theses values are considerably acceptable. It was shown that the proposed neural network model accurately reproduces all the effects of operation variables, and can be used in the simulation of a REEs leaching plant.

Jan 1, 2008