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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 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 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 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 Guido Deissnann, Jennifer L. Kirkaldy, Stephan Kistinger, Carol M. Pettit

Contaminated mine drainage presents a potential hazard for aquatic ecosystems and groundwater resources. Reliable predictions of long-term developments in seepage quality are necessary to select reasonable decommissioning options and remediation measures for waste-rock heaps during mine closure and/or mining site rehabilitation to minimize future risks and environmental hazards. Extensive geochemical modeling work was used to assess the consequences of various remediation options for waste rock heaps at a former uranium mine in Eastern Germany. The predicted contaminant discharges are used for an assessment of long- term benefits of decommissioning alternatives to derive conclusions for sustainable remediation measures using risk-based cost/benefit analyses.

Jan 1, 2000

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 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

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).

Reduce longwall equipment transfer time between panels by utilizing a predriven recovery room, and thus eliminate the premove preparation associated with most equipment recovery methods. Background and Approach The recovery point for a retreating longwall is a predetermined position near the end of the panel where mining is terminated and equipment is disassembled and removed. However, to prepare for equipment removal at the recovery point, bolting and wire meshing arc required before the recovery point is reached. Usually, preparations for equipment recovery begin about 35 ft from the recovery point, significantly impeding the normal rate of advance of the longwall and, consequently, production. Recently, BethEnergy Mines, Inc., and the Bureau of Mines cooperated to evaluate the applicability of utilizing predriven recovery rooms to improve longwall equipment recovery. A predriven recovery room is an entry driven parallel to the longwall face at the end of the panel, having standard dimensions (for that particular mine) and supported with bolts, wire mesh, and supplemental support. The preparation of this entry is completed before it experiences mining-induced effects from the approaching longwall face. This enables the face to continue to mine at its normal rate of advance until the panel is completed. The supplemental support is required to control the loads imposed by the front abutment and is the most vital component of the recovery room.

Jan 1, 1989