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By A. Y. Senchenko, Y. V. Kulikov

"Different methods aiming at specific energy calculation and design of AG/SAG circuits are used in mining. They include JK DWT/SMC test, SAGDesign test and CWi/RWi/BWi tests. The simplest methods with no secret equations and giving energy required for grinding in kWh/t were proposed by F.?. Bond and J.H. Starkey. D.J. Barratt proposed to use results of CWi/RWi/BWi tests for AG/SAG mill sizing. The present paper gives an alternative for forecast of specific AG/SAG energy. The principle is similar to the theory proposed earlier and consists in cumulative AG/SAG energy calculation using indices of rod and ball grinding (RWi+BWi) without impact crushing index (CWi). Then total energy is divided between two grinding stages using special formulas based on extensive statistical information. The forecast indices of AG/SAG and ball grinding are similar to the SAGDesign testing results. Hereinafter the predicted indices are used for mill sizing of the whole grinding circuit. The method considered in the paper enables prediction of AG/SAG energy based on the test results according to Bond method. The proposed method version was not widely published earlier. The aim of the work was to elaborate cost-effective, reliable and accessible way to determine specific AG/SAG energy. At this stage of method development, it is recommended to use it only for works at preliminary Geometallurgy stage (Scoping, Pre-Feasibility). At design stage the obtained data should be confirmed by appropriate testing using alternative JK DWT/SMC and SAGDesign methods.INTRODUCTIONAppropriate crushing and grinding testing is required to design efficient comminution circuits. It is obviously important to obtain accurate information about ore hardness properties since the main energy consumption at the plant is associated with comminution processes. Moreover, it is required to apply the processes that make it possible to minimize operating costs and efficiently use capacity of the installed equipment installed.When studying ore hardness properties, it is required to take into account that breakage of the material is performed using different equipment. That is why accurate selection of the equipment and the circuit calculation without relevant testworks is impossible. Ideally, the testworks should provide answers to the following questions: determination of ore hardness properties, determination of specific energy consumption for comminution as well as obtaining of sufficient amount of statistically significant results from the tests. The results from crushability and grindability testworks in the first place provide assessment of ore hardness properties and in addition correlate with throughput of the respective equipment. The data obtained can be used for modeling of comminution circuits at the stage of the technology development"

Jan 1, 2016

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 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

Prediction of the performance of explosives In bench mining by C. M. LOWNDS, B.Se. Hons (Rhodes), Ph.D (Cape) (Visitor) Some of the problems encountered in the calculation of the relative weight strength of explosives are discussed, and a realistic model of the behaviour of explosion products in a borehole is proposed. The heat flow from condensed to gaseous products is calculated as a function of time, and the relationship between the pressure and the volume of the product gases is found by a consideration of the compression of the rock between radial cracks. Work done on the burden is assumed to cease when the cracks first appear at the face. Reasonable values of burden and crack velocities are obtained from the model. Calculations of the relative weight strengths of aluminized mixtures of ammonium nitrate and fuel oil show that the increased density resulting from the addition of aluminium is the main advantage to be gained.

Jan 2, 1975

By M. Fooladi Mahani, M. R. Izadpanah

The knowledge of the thermal shock resistance of refractory materials is of outmost importance due to their wide range of applications in various industries, namely; ceramics, oil refinery, thermal insulation, nuclear power, chemical and petrochemical industries. Thermal shock resistance is measured in terms of the number of cycles that a refractory material can withstand when subjected to sudden temperature changes. This property is measured experimentally by heating a sample of the material and cooling it in a known environment then the number of cycles it withstands this treatment without spalling is a measure of its thermal shock resistance. Although this method of experimentation has long been the practice, but it is both expensive and time consuming. Various studies have been carried out to predict this vital property, however, the rang of applications are limited. The present study employs a test rig by which temperature distributions in magnesite samples are accurately measured. This information plus other results obtained from relevant tests on related properties are used to develop a model which predicts the number of quench cycles withstand by different materials when subjected to sudden temperature changes. The comparison of the theoretical values using the solution of the presented model with experimental data for different materials having different thermal resistance shows a closed agreement.

Jan 1, 2004

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 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

By Ashish Kumar Vishwakarma, Vivek Kumar Himanshu, Kaushik Dey

The accurate prediction of blast-induced ground vibration due to underground ring blasting is a prominent need for ensuring the safety of structures. Different site-specific empirical equations are available for the prediction of ground vibration. These empirical equations are best suited when the monitoring and blasting locations are present in the same medium. The change in the medium alters the behavior of wave propagation. Hence, existing empirical equations have limitations in peak particle velocity (PPV) prediction when the blasting location is an underground hard rock mine and the monitoring location is ground surface. This is because the underground metal mine comprises different levels having void in the form of excavated stope or paste-filled stope. It is very difficult to predict the magnitude of PPV on the surface in such instances. Therefore, this study has been carried out to predict the PPV at surface due to underground blasting. In this paper, PPV data was recorded at surface for 207-ring blasts. Furthermore, the PPV has also been measured at different underground locations for 47-ring blasts. Different empirical equations along with k-nearest neighbor (KNN) and random forest (RF) model of machine learning technique were developed for the prediction of PPV. Most of the empirical models have higher accuracy in the prediction of PPV at an underground location. This shows that scaled distance-based empirical predictors are best suited when the monitoring and blasting media are the same. However, the empirical models do not predict PPV accurately when the monitoring location is ground surface and the blast is conducted underground. The machine learning models are better suited for PPV prediction in such cases. Based on the analysis performed for the case study site, RF model predicts PPV at surface with the highest accuracy. The coefficient of determination and root mean square error for RF model used for predicting PPV at ground surface are 0.94 and 0.438 mm/s respectively. The RF-based model is also the best suited among all the models for predicting PPV at underground locations as well.

Apr 20, 2024

By D. Dunbar

The flooding of open pits is now receiving considerable attention from the mining industry as an option that may minimize environmental impacts, particularly when anoxia in pit-lake deep waters can effectively eliminate oxidation of pit-wall sulfides. This paper describes a state-of-the-art, coupled physical-geochemical pit-lake model. The model is used to verify that a controlled, two-stage infilling of the open pit at the Kori Kollo gold mine, southeast of La Paz, Bolivia, would result in a cost-effective, environmentally responsible closure strategy. By filling the pit initially with highly saline surface waters and naturally saline deep groundwater and then capping with fresh river water, a very stable and strongly stratified pit lake will be developed. In addition, the stratification will result in the isolation of metal-rich saline bottom waters from surface waters of good quality. Results of this model provide defensible predictions of the physical stability and evolution of water quality in the Kori Kollo pit lake over time. The model results indicate that anoxic conditions will develop below the fresh-saline water boundary within one to two years, followed by the rapid onset of sulfate-reducing conditions in the bottom waters. Hydrogen sulfide produced by sulfate reduction will readily react with dissolved metals to form highly insoluble metal sulfides. The process results in the gradual removal of metals from the water column and the concomitant improvement of water quality over time. A notable benefit of this strategy is that it can be fully integrated with other mine site closure options as well as future environmental-management strategies. The proposed plan allows for the safe isolation of saline waters, the potential for passive treatment in anoxic bottom waters of ARD seepage from other mine site operations, should they arise, and a repository for future reactive wastes that could be effectively isolated in a physically and chemically stable environment.

Jan 1, 2003

By D. Dunbar

Flooding of open pits now receives considerable attention from the mining industry as an option that may minimize environmental impacts, particularly when anoxia in pit lake deep waters can effectively eliminate oxidation of pit-wall sulphides. A state-of-the-art, coupled physical-geochemical pit lake model is used to verify that a cost-effective, environmentally responsible closure strategy for the open pit at the Kori Kollo gold mine, southeast of La Paz, Bolivia is to adopt a controlled, two-stage infilling of the open pit upon cessation of operations. By filling the pit initially with highly saline waters from evaporation/infiltration basins and naturally saline deep groundwater, followed by capping with fresh river water, a very stable and strongly stratified pit lake will be developed. In addition, the stratification will result in the isolation of metal-rich saline bottom waters from surface waters of good quality. Results of this model provide defensible predictions of the physical stability and evolution of water quality in the Kori Kollo pit lake over time. The model results indicate that anoxic conditions will develop below the fresh-saline water boundary within 1 to 2 years, followed by the rapid onset of sulphate reducing conditions in the bottom waters. Hydrogen sulphide produced by sulphate reduction will readily react with dissolved metals to form highly insoluble metal sulphides. The process results in the gradual removal of metals from the water column and the concomitant improvement of water quality over time. A notable benefit of this strategy is that it can be fully integrated with other mine site closure options as well as future environmental management strategies. The proposed plan allows for: 1) safe storage of saline evaporation basin waters; 2) the potential for passive treatment in anoxic bottom waters of ARD seepages from other mine site operations should they arise; and 3) a repository for future reactive wastes that could be effectively isolated in a physically and chemically stable environment.

Jan 1, 2001

By R Marchant, D Willingham

"Predictive maintenance (also known as PHM or equipment ‘health monitoring’) refers to the intelligent monitoring of equipment to avoid future equipment failures. In contrast to conventional preventive maintenance, the maintenance schedule is not determined by a prescribed timeline; instead, it is determined by analytic algorithms, such as machine learning, using data collected from equipment sensors and/or via simulation.Predictive maintenance offers the following benefits for operators and equipment manufacturers:reduction of equipment downtime by identifying issues before failure, thereby enabling convenient scheduling of equipment service and extending equipment lifetimeautomatic determination of the root cause of the failure, enabling appropriate service to be performed without utilising resources to determine a diagnosis avoidance of costs of unnecessary maintenance and unexpected failures improved safety and product quality.Algorithms and data are critical to predictive maintenance success. Sensor data preprocessing is performed using advanced statistical and signal processing techniques. In the case that data isn’t available for the full range of operating modes, operating scenarios can be simulated. Machine learning techniques are then used to estimate equipment health. Once tested, predictive maintenance algorithms may be operationalised in an IT environment such as a server or cloud. Alternatively, algorithms may be implemented in an embedded system directly on the equipment, allowing for faster response times and significantly reducing the amount of data sent over the network.In this paper, we use both historical and simulated data of the plant under various modes of operation including run to failure, and we then leverage machine learning techniques in MATLAB to estimate degradation and remaining useful life. This technique, of combining both simulation models and machine learning, in the areas of predictive maintenance and equipment health monitoring has been adopted by Safran, makers of aircraft jet engines, since 2007. They now have over 160 modules developed (Lacaille and Aurelie, 2015).Citation:Willingham, D and Marchant, R, 2016. Predictive Maintenance Using Simulation and Machine Learning, in Proceedings 13th AusIMM Mill Operators’ Conference 2016, pp 279–284 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Oct 10, 2016

By Gwenaële Petit, Ulan Massimkhanov, Gilles Joubert

For its Zuuvch Ovoo uranium deposit in Mongolia, Badrakh Energy (partly owned by Orano Mining) is working on the prefeasibility study for exploitation by ISR (in situ recovery). As part of this project, Orano built a 2-cell (each cell is constituted of one producer well located in the center and surrounded by six injection wells in an hexagonal pattern) and a processing pilot plant that will be used to demonstrate the operability and economic viability of an ISR process. Another main objective of this pilot trial is to demonstrate the ability of Orano’s modeling tools to predict the ore behavior under given leaching conditions. To model uranium production by ISR, Orano uses HYTEC, a reactive transport computational software, which was adapted for its existing operation in Kazakhstan (Katco) to predict uranium recovery and reagent usage based on operating conditions. In order to perform similar simulations for this new deposit, Orano engaged in a comprehensive program including ore sampling, detailed characterization, and metallurgical testwork. The mineralogical conclusions were used to prepare a simplified geochemistry for the ore, in particular carbonate and reactive clay grades, uranium mineralization and penalizing elements such as pyrite. Then, bottle roll leach tests were used to characterize dissolution kinetics for smectite clays. Eventually, column leach test results such as pH, free acid, and uranium recovery were modeled on HYTEC to improve the fit of some of the dissolution kinetic parameters. Parameters fitted using metallurgical test work results will now be used as inputs for predictive model at the pilot scale, in order to anticipate operational results and help with operating decisions for production ramp up.

Jan 1, 2020

By Kim Henley, Chris Batten

This paper discusses the use of the Orica WildFireTM flyrock model to help mining operations in Australia and Asia minimise blast exclusion zones for equipment, increasing utilisation and productivity and delivering downstream value

Feb 1, 2020

By Yucheng Li, De Huang, Yonghong Liu

Mine frictional resistance is one of the essential parameters of mine ventilation systems, and it is of considerable significance for the prevention and control of mine risks and accident hazards. Frictional resistance requires a lot of resources and time and has errors. To reduce the errors of frictional resistance, a regression model of frictional resistance is proposed based on the correlation analysis of the affecting factors. Through the comparative test analysis of seven regression methods, it is found that the classification and regression tree method for solving the proposed regression model has better performance than other methods. The least square error criterion is used to calculate and rank the importance of the factors affecting frictional resistance. The results show that the cross-sectional area of the tunnel, the support method, and the presence or absence of devices or pedestrian ladders have the most considerable influence on the prediction accuracy of frictional resistance. The support methods and cross-sectional areas are essential input variables. Through the combination test analysis of the influencing factors, the results show that the regression model does not have predictive ability when the number of input variables is less than 3 and the best predictive performance cannot be obtained using all the influencing factors. The best prediction model of resistance is to combine the shape of the tunnel, the support method, the presence or absence of devices or pedestrian ladders, and the cross-sectional area. The method proposed in this paper does not require a lot of resources and time investment, and the frictional resistance error is small. At the same time, it solves the problem of resistance prediction with incomplete data of influencing factors and provides a compelling new method for obtaining frictional resistance parameters.

Jan 28, 2022

By C. Orf. Chukabarah

Erratic behavior in convergency during iterative numerical-method evaluation of mine ventilation fan characteristic curves is one of the common problems encountered in mine ventilation network analysis. Five different sets of mine fan data points, that had resulted in unsatisfactory fan characteristic curves, were re-analyzed in the present study, using an embedded mine network optimization computer program. Current results revealed that all five fan data sets not only can be satisfactorily fitted as Yang (1992) did with MFIRE subprogram, but can further be optimized with unique solutions as implemented in the present study. It was concluded, inter alia, that the improved MFIRE-based mine fan curve-fitting computer subprogram (SPLINE) produces satisfactorily similar fan curves as SPSS-based curve-fitting algorithms, including when the latter method uses either standardized or unstandardized fan curve data sets.

Jan 1, 1993

By A Fitzpatrick

An integrated and dynamic water balance and water quality model for a proposed base metal mine has been created using the GoldSimÖ software package. The model was initially developed to address the water quality of the Tailings Management Facility pond and how pond discharge would potentially affect downstream water quality. However, due to changing project objectives, the modelling has expanded over time through an iterative process to encompass the full mine site and all receiving environment water quality. The model results have been used as part of the mine planning/design process but are also intended to be used for the environmental impact assessment and permitting process. The model has been built to allow for changes to input terms to be readily made as additional data becomes available. The model results serve as a prediction of water quality and the potential project impacts to the receiving environment water quality. This paper presents the process by which data from a wide variety of sources was used to construct the model and how the model results were used to guide development of a conceptual water management plan. In addition, the role of the model in establishing proposed discharge criteria will be highlighted. The model has been delivered as a fully functional application with the intent that the end-user can be other than the modeller, the development of this aspect shall also be covered. CITATION:Fitzpatrick, A, 2012. Predictive site-wide water quality modelling as a tool for mine planning and permitting - A recent Canadian example, in Proceedings Life-of-Mine 2012 , pp 71-78 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 10, 2012