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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

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 Pratoomkhuan N. Lertsgoon

A large underground cavern with dimensions of 25m × 175m × 49m (W×L×H) was excavated in sandstone and siltstone to house 4 units of hydropower generator and was completed in 2000. In feasibility study stage, the property of the siltstone which governs the ceiling of the cavern was questionable due to slaking phenomenon. Parameters on rock properties for design were back calculated from actual ground response measured from a trial excavation. For safety of construction, comparison between prediction and actual ground responses during excavation had proceeded. The prediction was provided by means of PHASES and PHASE2 software and the actual rock response was detected by the provided monitoring system.

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 Quin Dennis

Process Optimization ?What is it really? It is NOT ?a larger fan to increase draft through a process ?a larger pump on a wet circuit ?a wider conveyor belt for solid materials ?a larger feed elevator into or out of a process These relieve individual constraints ??bottlenecks??and allow operation to the next constraint in the process ?It is also NOT about automating the operator ?which captures consistency but is not continuous optimization

Nov 1, 2011

By D T. Feinstein, M B. Gotkowitz

In Wisconsin, state laws and regulations governing metallic mine permitting stipulate that protection of groundwater quality be assured indefinitely through all phases of the mining project, including development, active mining, reclamation, and long-term closure. By rule, the Wisconsin Department of Natural Resources (WDNR) requires that permit applicants use a numerical model to assist in the assessment of compliance with this requirement for all proposed mine-waste facilities. As a part of the compliance assessment, model calculations are used to predict groundwater solute concentrations through time at designated boundaries for comparison to current groundwater quality standards. In practice, this modelling requirement has involved the application of numerical solute-transport models. Our review of a solute-transport model submitted by the developers of a proposed mine near Crandon, Wisconsin, provides us with an opportunity to assess the usefulness of transport modelling as a permitting requirement. Recent evaluations of solute-transport models by other workers suggest limitations in predictive accuracy. Our review of the model submitted for this project also identified important limitations due primarily to uncertainty in calibration of model parameters at the undeveloped site. The utility of any model to predict project compliance or non-compliance with numerical standards is limited by the range of uncertainty of key model parameters, such as dispersivity and porosity. Uncertainty in the future behaviour of the solute source(s) also reduces the predictive power. Moreover, the site characterisation provided by the model may have limited power to describe the site condition post-development. Although these inherent shortcomings accompany the use of solute transport models at undeveloped sites, the requirement for model development and the ensuing technical review has substantially assisted in our evaluation of site conditions and project design. Through this process we have identified elements of the waste facility design that are crucial to the environmental viability of the project, resulting in modifications to original project proposals. While results from this model review will be used to evaluate compliance, additional benefits come from the identification of critical design features that enhance groundwater protection.

Jan 1, 2003

By Y Xie, D Willingham

Predictive maintenance (also known as ‘condition/health monitoring’) refers to the intelligent monitoring of equipment to avoid future 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:- reduces equipment downtime by identifying issues before failure, thereby enabling convenient scheduling of equipment service and extending equipment lifetime- determines the root cause of the failure, enabling appropriate service to be performed without utilising resources to determine a diagnosis- avoids the unnecessary costs of maintenance and unexpected failures- improves safety and product quality.Algorithms and data are critical to predictive maintenance success. Sensor data is first preprocessed using advanced statistical and signal processing techniques; it is then combined with predictive modelling techniques, such as machine learning, to estimate equipment health.Once tested, predictive maintenance algorithms may be used in an operational setting within 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.CITATION:Willingham, D and Xie, Y, 2017. Predictive maintenance modelling for bearing-based equipment, in Proceedings Iron Ore 2017, pp 627–630 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 24, 2017

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 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 Greg Partington, Darryl Thorburn, Charlene Wildman, Elisa Puccioni, Connie Payne, Campbell McKenzie

"Mineral prospectivity modelling of polymetallic nodule deposits has been completed over the Cook Islands Exclusive Economic Zone (CIEEZ) using the GIS based Weights of Evidence modelling technique. Polymetallic nodules, also known as manganese nodules, have been explored as a possible resource for a number of metals including cobalt since the early 1970s. Recent data on the nodule composition and abundance in the CIEEZ has led to a renewed interest in the prospectivity for manganese nodules in the region. The predictive modelling has used the most recently accepted mineral model for polymetallic deep-sea nodule deposition to identify factors that are most likely to map the presence of this type of mineralisation in the CIEEZ.Manganese nodules form on the deep seafloor as concretions formed by concentric layering of iron and manganese hydroxides around a core and they grow at a very slow rate, on the order of millimeters per million years. The available data for the CIEEZ indicates a greater abundance in the area of nodules formed by hydrogenetic processes, which are the result of slow precipitation of metallic components from seawater. The environment in which the nodules are most abundant is at depths between 4,000 m and 6,000 m in areas of low sedimentation rate. Hydrogenous nodules commonly grow on siliceous and pelagic clay sediment, they are less common on other types of sediment."

Sep 1, 2014

By M J. Nugus, F Clark, E J. Hill, J T. Carswell, J S. Cleverley, N H. S Oliver

High-nugget gold orebodies are difficult to model in 3D because samples from diamond drill core are generally too small to provide reliable estimates of the gold content of the larger body of rock, and therefore interpolation of gold assay values between drill holes is also unreliable. As a result, gold resources are generally underestimated in areas of high nugget. In practice, proxies for gold mineralisation, such as presence of alteration, specific structures or high vein density, have been used by geologists to predict rocks which are favourable for mineralisation. However, there has been no automated method for combining the predictive capabilities of several proxies, except by human judgement.We have been developing a method to combine several different types of geological features or geochemical analyses into a single measure using conditional probability. Conditional probability provides a meaningful numerical measure for the favourability of rocks for containing high-grade gold mineralisation. This measure can be interpolated in the exploration space, providing a sound basis for a 3D model of the orebody. We have developed this technique using data from the Sunrise Dam Gold Mine, which is a high-nugget orebody hosted in a structurally complex Archaean greenstone belt and which has historically proved to be a very challenging orebody to model.CITATION:Hill, E J, Nugus, M J, Clark, F, Oliver, N H S, Cleverley, J S and Carswell, J T, 2013. Predictive three-dimensional modelling of nuggety gold orebodies, Sunrise Dam, Western Australia, in Proceedings World Gold 2013 , pp 463-468 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 26, 2013

By M. Sadegh Safarzadeh, Stanley M. Howard

The two-dimensional predominance area (Kellogg) diagram (PAD) surfaces bounding the three-dimensional Cu-As-S-O system were constructed at 900 K. The computations employed gases As4O6(g), O2(g), and SO2(g) as the independent coordinate species to generate diagrams of As4O6(g) − O2(g),O2(g) − SO2(g), and As4O6(g) − SO2(g), while the third gas (As4O6, O2, or SO2) was fixed. The results indicate that there is no shared boundary between enargite (Cu3AsS4) and copper sulfate (CuSO4); however, at PO2 < 10−6 atm, PAs4O6 < 10−10 atm, and PSO2 > 10−4 atm, CuSO4 is the stable phase at 900 K. The chemical driving force for oxidation of intermediate phases between enargite and copper sulfate is substantial except for basic sulfate (CuO · CuSO4).

Jan 1, 1888