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By Kathy Kalenchuk

To develop strategic and tactical strategies to manage seismic hazard in a safe and economic manner, it is necessary to understand the spatial and temporal distribution of mine wide seismogenic behavior. Global mine-scale numerical stress modelling provides a means to forward predict seismic susceptibility thereby facilitating informed decision making for the development and implementation of strategic and tactical risk mitigation measures. To make forward predictions of hazard susceptibility using global stress models with reasonable confidence, it is necessary to rigorously calibrate numerical stress models to the observed history of ground reaction. This paper demonstrates quantifiable methods for calibrating numerical models and developing probabilistic prediction criterion using seismic data. The appropriate applications of model inputs (including boundary conditions, constitutive models and material parameterization) specific to global mine-scale modelling are also discussed. Lastly, this paper demonstrates how the calibration process adds value not only to informed decision making, but also to the improved understanding of site geomechanical conditions; including enhanced identification of geotechnical domains and mechanical indicators required for seismic mechanism domaining.

Apr 26, 2022

By K. Woodward, Y. Potvin, B. McFadyen, M. Grenon

Open stoping has become a popular mining method in hard rock mines, not only due to the safety of the method as a non-entry approach, but also because of the high extraction rate and low costs. At mine sites, stope performance is evaluated by calculating stope overbreak using the stability chart. However, limitations of the stability chart regarding the precision of the predictions, nonconsideration of factors such as the influence of blasting, and the exclusion of underbreak have led to non-optimal designs. The capabilities of today's computers have increased the amount of data being collected and the power of models being built. This article presents a step towards a new stope design approach where stope overbreak and underbreak are measured and georeferenced using octrees at an approximately cubic metre resolution and predicted using multivariate statistical models (partial least square, linear discriminant analysis, and random forest). Results show that overbreak and underbreak location along the design surface and their magnitude are predicted with good precision using a random forest model. These predictions are used to build the expected geometry of the open stope. The resolution of the data and the use of multivariate analysis has enabled the prediction of variation in stope performance along the design surface, going well beyond the simple qualitative per stope face prediction provided by a traditional stability chart approach.

Jun 5, 2023

By H. Schunnesson, H. Fredriksson, D. Johansson, E. Söderström, S. Manzoor, A. Gustafson, M. Danielsson

Fragmentation analysis is an essential part of the optimization process in any mining operation. The costs of loading, hauling, and crushing the rock are strongly influenced by the size distribution of the blasted rock. Several direct and indirect methods are used to analyse or predict fragmentation, but none is entirely applicable to fragmentation assessment in sublevel caving mines, mainly because of the limitations imposed by the underground environment and the lack of all the required data to adequately describe the rock mass. Over the past few years, measurement while drilling (MWD) data has emerged as a potential tool to provide more information about the in-situ rock mass. This research investigated if MWD can be used to predict rock fragmentation in sublevel caving. The MWD data obtained from a sublevel caving mine in northern Sweden were used to find the relationship between rock fragmentation and the nature of the rock mass. The loading operation of the mine was filmed for more than 12 months to capture images of loaded load-haul-dump (LHD) buckets. The blasted material in those buckets was classified into four categories based on the median particle size (X50). The results showed a stronger correlation for fine and medium fragmented material with rock type (MWD data) than coarser material. The paper presents a model for prediction of fragmentation, which concludes that it is possible to use MWD data for fragmentation prediction.

Mar 2, 2022

By M. E. Kuchta

LKAB's Kiruna mine, located above the Arctic Circle in northern Sweden, is one of the world's largest underground mines, and it is one of the most modern. The Kiruna orebody is a high-grade magnetite containing about 80% low-phosphorous iron ore and about 20% high-phosphorous iron ore in situ. The mining method used is large-scale sublevel caving. Due to the blending of high-phosphorous ore with low-phosphorous ore during the extraction process, only about 20% of the run-of-mine ore can be used for the mine's low-phosphorous product. The blending process is highly irregular and difficult for the mine-planning personnel to predict. An algorithm was developed that uses principles of gravity flow to predict the run-of-mine phosphorous grades from the in situ geologic block model. The model can be used for both long- and short-term predictions.

Jan 1, 2003

By S. Makni, A. Faucher, F. Robichaud, A. Bouajila

"Optimizing the ball charge in a grinding mill may be needed to maximise throughput or alternatively, to achieve proper product size in more challenging situations. Efforts to optimise the ball charge however are essentially empirical and suffer from the lack of appropriate, accessible and reliable tools. To address these limitations, a new model-based technology was developed that relies on 3D Discrete Element Method and population balance techniques: it takes on the form of a simulator that aims to significantly decrease the time required to optimise the size distribution and the grinding media load within a ball mill. This paper summarizes the research work related to the development of this simulator. The calibration of the different constituting sub-models will be highlighted. The successful use of the simulator to predict the effect of ball make-up on the grinding product size distribution in one plant is also reported.INTRODUCTION Adjusting the characteristics of the grinding media charge in grinding mills holds significant potential for optimisation of mill performance. For example, properly adjusting the ball size distribution in a ball mill can help achieving specified targets for final product size distribution and/or throughput capacity. However, several problems appear when reviewing the process and the available tools to optimise ball mill load: • The current empirical simulation tools are lacking with regard to the ability to predict the performance of grinding when changes to mill ball make-up are made; • Generally, these tools estimate the top size of the charge so it is difficult to predict the effect of mixed ball make-up; • As the quality of prediction of these tools is low, the operators try to optimise the ball charge by a ""trial and error"" method and proceeding with conservative changes. The influence of these changes on grinding performance is often less than the measurement precision and the improvements then remain imperceptible; • A change in ball make-up may require an average of about 6 months waiting period before reaching the stationary state of mill ball load. This exercise is due to be costly if applied change decreases the mill performance. In its effort towards the comminution process optimisation, COREM R&D portfolio had addressed the herein described opportunity during the last five years This paper describes the technology already developed for the optimisation of ball charge. It takes on the form of a simulator that aims to predict the evolution of the grinding media load, the grinding energy available in the ball mill and the final product size distribution. The simulator was first validated in a pilot scale ball mill and then applied in an industrial case."

Jan 1, 2012

By William Gleason

"What will the mine of the future look like? Will it resemble the current operations that supply the world with the products it needs to function, or will it be something totally different? How will the demands from the communities that are most affected by mining operations shape the mine of the future? How will technology and innovation be used to help miners reach deposits that are getting harder to access, and what are the dangers of this new world? Who will work at the mine of the future, and what will that work look like?These are questions that do not yet have definitive answers, but they are questions that need to be asked for the mining industry to move into a future that is as filled with uncertainty and challenge as it is with promise and opportunity.On Feb. 16, the 2015 SME Annual Conference and Expo and the 117th CMA National Western Mining Association conference began with a keynote address, "The mine of the future: Forecasting opportunities and challenges for the global mining industry." Panelists from five sectors of the mining industry participated in the session, which was followed by the conference and expo, that in large part, were also trying to answer the question of what the mine of the future will look like.In coming months, Mining Engineering will continue the discussions and answer some of the questions submitted by members of the 2,000-person audience during the keynote address in a monthly feature called, "Predicting the Mine of the Future," and in Thought Leaders Podcasts with the panelists and other industry experts."

Jan 1, 2014

By Michel Dagbert

In the exploitation of porphyry-type deposits, the quality of the ore is mostly defined according to its grade. Besides the simplest distinction, ore-waste, several grade categories may exist and they correspond to the different ways in which the ore is further processed. At the time of mining the selection of the different categories is done from extensive information available from blast holes. But planning has to be done before this information is available and it is necessary for the geologist to forecast, from exploration and development data alone, the possible outcome of the selection done later on a block, a level, or the whole deposit and ultimately predict the ore reserves and value of the deposit. A good statement of this problem has been presented at the 1976 AIME annual meeting by Williamson and Hueller (1976). They have shown that, if the cut-off values are directly applied to the estimated grade of blocks, a gross overestimation of the recovered tonnage of ore (14%) will occur. This problem, now termed "the vanishing tons problem" (DaVid, 1977) seems to be common in many open-pit operations and it has been explained several times (DaVid, 1972; Huijbregts, 1975). In order to solve it l.Jilliamson and Mueller proposed a solution based on compiling masses and masses of blast hole data available from mined-out portions of the deposit. They derived empirical correction factors which provide for every block, given its estimated grade, the expected tonnage and average -grade of each category of material within that block. The purpose of our work is to repeat the same exercise but ignoring the blast holes data i.e. we want to derive the correction factors from the only DOH data. Doing this, we are more in the real conditions where the valuation of the mine and are reserve analysis has to be made before production actually starts. Two possible ways to achieve this goal will be investigated and the emphasis will be on recently

Jan 1, 1977

By P McCarthy

The relatively new field of geo-metallurgy promises to improve the predictability of mill feed quality and enhance processing outcomes. However, it is subject to the same limitations as the prediction of grade using geostatistics and has additional limitations due to the spatial distribution of the variations being measured and the sample sizes required. Schedules based on geo-metallurgical models will, in turn, be subject to variations arising from geological control and practical mining constraints.This paper discusses the key drivers of variations in mill feed quality and quantity from a mining perspective. It concludes that geo-metallurgical modelling addresses only one aspect of the problem. A case study is provided to illustrate poor predictability of metallurgical performance even with very detailed geological data.

Aug 8, 2011

By I. O. Ondicho, E. K. Mutinda, B. O. Alunda, E. Agyekum

Driven by the necessity to improve blast performance regarding fragment size distribution in limestone mines, this paper introduces the prediction and measurement of blast fragmentation distribution through the modified Kuz-Ram (MKR) and Kuznetsov-Cunningham-Ouchterlony (KCO) models. Precise prediction and measurement of rock fragmentation resulting from blasting play a critical role in hard rock ore extraction. This is due to the substantial influence of post-blast ore size distribution on the efficiency of all subsequent downstream rock handling and processing operations. Additionally, employing secondary breakage of boulders, when necessary, leads to an unjustified rise in production costs. In this paper, the MKR and KCO models were utilised to identify the most appropriate model for application in Bisil and Simba quarries situated in Kajiado County, Kenya. Split-Desktop was used for blast fragment measurement by processing digital images obtained after blasting and computing the fragment size distribution of the blasted rock. As per the investigation, the recorded percentages of boulders generated at the sites ranged from 7% to 12.3% for the Simba quarry and from 11.2% to 23.9% for the Bisil quarry, respectively. Regression, correlation, and root mean square error (RMSE) were employed as the primary performance indicators. The study concluded that KCO outperformed MKR in accurately predicting blast fragment sizes in the examined quarries.

Mar 5, 2025

By M R. Asef

Although generation of environmentally harmful hydrogen sulfide is considered to be related to sour gas fields, sweet gas reservoirs and waste fluids, travertine springs and geothermal fields may also produce acid gas (H2S and CO2) under certain conditions. The potential parameters influencing such a possibility need to be fully investigated to evaluate the extent of the consequences and predict hydrogen sulfide generation accordingly. Reports on hydrogen sulfide production at different regions and fields were gathered worldwide for the cases when some of them did not have such a problem previously. In order to inspect different aspects of these case studies, some other regions with no hydrogen sulfide content were selected as control. In all cases water samples were taken carefully for chemical analysis in the laboratory. Besides, mineral compositions were characterised in some cases for better evaluation. In order to give a thorough prospect of hydrogen sulfide production, a geochemical model was prepared. In situ engineering geological conditions, especially pressure and temperature, were assigned in the model to investigate the possibility of H2S generation using PHREEQC software. The results of modelling revealed that although many similarities existed between mineral compositions of the cases, certain formations were particularly prone to hydrogen sulfide generation. For better evaluation, important exchangeable cation ratios such as potassium/sodium, calcium/magnesium and ion proportions like sulfate and chloride were calculated as trends of the formations. As expected, the model could successfully monitor a higher ratio of SO42- and Cl- in potential H2S generator formations. Wide variation in percentage of HCO3- was considered as a secondary observatory parameter for better estimation. In addition, it was noticed that higher anion content created an environment ready to absorb any cations and this makes H2S generation easier. Finally, using correlations between predictors and estimators for low depth and deep samples defined the degree of effectiveness for every sample.

Sep 26, 2011

By E. Anelli

Based on experimental continuous cooling transformation (CCT) diagrams, an artificial neural network has been trained to predict the amount of microsturctural constituents and hardness resulting from and controlled cooling at rates from 150 to 0.015 K/s. It can be applied to quenching and normalizing processes of low-alloy steels with 0.05-0.30 C% and 0.4-2.0% Mn -with possible microalloying additions. Examples of the good predictive capabilities about 15 Vickers standard deviation for hardness and a few percent for volume fraction of phases) are reported' Keywords: Austenite decomposition, low. And medium-carbon steels, artificial neural networks

Jan 1, 2003

By T. G. Sitharam, P. B. Choudhury

Prediction of ground vibration (peak particle velocity, PPV) and frequency is an important task in geoscience. Till date, many empirical equations are derived from conducting trial blasts in the mines, to arrive at attenuation predictors. These predictors are usually site dependant and hold good for one particular type of rock/strata. These predictors cannot be used for geologically different rocks/strata. This paper presents the application of support vector machine (SVM) model, for predicting blast induced ground vibration and frequency, using a one comprehensive model for three different types of rock. Inputs to the SVM model formulation involve twenty one parameters consisting of geotechnical properties of the rock mass, blast design geometry and explosive properties, and the output of the formulated model is peak particle velocity and its associated frequency. The data has been acquired from eight limestone mine, five coal mine and two manganese mines. The comprehensive SVM model is formulated using 400 datasets and tested with 143 datasets. The performance indicators to the model not only justify its formulation but also the advantages it has over site specific predictor models. The SVM model not only predicts PPV, with higher correlation coefficients as compared to predictor equations, but also the associated frequency.

Jan 1, 2010

By M. Kamali

In this research, in order to predict the peak particle velocity (PPV)(as vibration indicator) caused by blasting projects in the excavations of the Karoun III power plant and dam, three techniques including statistical, empirical, and neural network were used and their results were interpreted and compared. First, multivariate regression analysis (MVRA) was used as statistical approach. Next, PPV was predicted using some widely used empirical models. Lastly, an artificial neural network was used. In the ANN model, maximum charge per delay, total charge per round, distance from blast site, direction of firing, blasthole length, number of blastholes, total delay in milliseconds, number of delay intervals, and average specific charge were taken into consideration as input parameters and consequently the PPV as output parameter. The results of the techniques were interpreted from two points of view. Firstly, the correlation between the observed data and predicted ones, secondly the total error between observed data and predicted ones. The MVRA had a satisfactory correlation but its error of estimation was comparatively very high. The empirical model had reliable correlation and a small error of estimation; in total the results of empirical method were more reliable than those of MVRA. Generally, the ANN approach showed very high correlation and a very small error. The results of this research indicated that the ANN model is the best predicting model for PPV in comparison with other approaches.

Jan 1, 2010

By M. P. Roy, P. K. Singh, A. K. Mishra, Ashish K. Vishwakarma, Vivek K. Himanshu

Ground vibration is one of the major hazards produced by rock-blasting operation. The accurate prediction of vibration is necessary for designing controlled blasting parameters. The existing vibration predictors consider maximum explosive charge weight per delay and distance as the parameters responsible for ground vibration. These predictors are based on the assumption that the geometrical parameters of the blast will be constant for a site. However, the mining sites with bigger production targets have varying geometrical parameters to suit the excavator utility. Accordingly, the other blast design parameters will also have an impact on ground vibration intensity. A principal component analysis is a dimension reduction technique. This technique along with multivariate logarithmic regression has been used in this paper to predict the ground vibration. The technique has classified the blast design parameters into four principal components. The regression with the scores from these principal components has been carried out. The evaluation of the model performance of predictors along with the existing empirical predictors has been carried out using R2 and RMSE values. The evaluation suggests that the predictor with logarithmic regression followed by principal component analysis gives better performance with respect to the existing empirical predictors.

Jul 30, 2022

By H Khoshrou, A Siamaki

Blast designs can have productive and non-productive impacts on downstream stages, mine productivity and operating costs. On the other hand, ground vibration, fragmentation, and back break caused by blasting impose damage and financial penalties and which must be controlled by blast design. One of the significant variables in blast design is the burden. In this study, the potentials of artificial neural network are investigated in prediction of burden in the Sungun copper open-pit mine. Input data were assembled through 18 blasting blocks according to different levels and experimental geomechanical investigation. To construct the model blastability index, uniaxial compression strength, hole diameter, specific weight of rock, rock quality designation and cohesion strength are taken as input parameters, whereas, burden is considered as an output parameter. Mean square error was used as the performance function and back propagation algorithm as the training function, containing four hidden layers and 14 data sets. Four sets of data were used to make sure that correct training had been carried out. This produced the coefficient correlation of 0.662.

Sep 26, 2011

By G. Akdogan, Q. G. Reynolds, S. J. Baumgartner

A computational model of a submerged arc furnace (SAF) used in the production of ferrochrome is presented. The model‘s intended use is to investigate the extent to which intrinsic and extrinsic properties of the raw materials affect burden distribution and electrical resistance within the furnace. The model is built on the discrete element method and calculates the mechanical interactions of particle distributions resulting from the flow motion of typical raw materials used in the smelting of chromium ore. This model excludes the effects of thermodynamics, furnace chemistry, and heat transfer. It illustrates how the consumption of materials (chromite pellets, flux, and reductant) is affected by changes in electrode length, reductant fractions, and reductant sizing and density during the formation of a reductant bed. The resistance calculation algorithm developed by Mintek was applied to construct networks developed from particle contacts, which can quantitatively generate estimates of the electrode-to-electrode and electrode-to-bath electrical conduction conditions.

Apr 16, 2024

By A. Adeli, A. V. Metcalfe, E. Addo, E. Sepulveda

"The accurate modelling of geometallurgical data can significantly improve decision-making and help optimize mining operations. This case study compares models for predicting copper recovery from three indirect test measurements that are typically available, to avoid the cost of direct measurement of recovery. Geometallurgical data from 930 drill core samples, with an average length of 19 m, from an orebody in South America have been analysed. The data includes copper recovery and the results of three other tests: Bond mill index test; resistance to abrasion and breakage index; and semi-autogenous grinding power index test. A genetic algorithm is used to impute missing data at some locations so as to make use of all 930 samples. The distribution of the variables is modelled with D-vine copula and predictions of copper recovery are compared with those from regressions fitted by ordinary least squares and generalized least squares. The D-vine copula model had the least mean absolute error. IntroductionIn this paper we compare the use of D-vine copula, generalized least squares (GLS), and ordinary least squares (OLS) for modelling geometallurgical data from an orebody in South America. The first objective is to construct models for predicting copper recovery (Rec) from the Bond mill index test (BWi); resistance to abrasion and breakage index (A*b); and semi-autogenous grinding (SAG) power index test (Spi). This involves fitting a D-vine copula and regression models fitted by OLS and GLS. The second objective is to investigate the performance of the fitted models for predicting Rec (Willmott and Matsuura 2005).Traditional resource model approaches either ignore the mineral processing characteristics of extracted tonnages or treat processing as an independent component of a mining operation. The net present value (or any other objective) can be truly optimized only by considering the mining operation as an integrated system in which net value is defined as the end-product that the company sells. This approach requires the resource model to be extended to include all relevant rock properties and processing responses."

Mar 1, 2019

By Kerry Burns

Contrary to active subsidence, which occurs concurrently with mining operations, or is completed within a few days following coal extraction, delayed subsidence may take many years to appear at the surface after coal mines are abandoned. There are two principal morphological types of delayed subsidence: troughs, which are shallow depressions, and sinks, which are steep-sided crown pits. Both types are damaging to surface structures, and a variety of methods have been introduced to deal with the problem, ranging from subsidence insurance to site restitution. In planning insurance or restitution measures, a predictive model is of value in estimating the magnitude of the problem and the size of long-term budgetary commitments. Only one model is known, which was developed for the Bureau of Mines by GAI Consultants of Monroeville (l,2,3). The GAI model is presented in qualitative terms. This report develops a formal basis for the model and tests a numerical implementation on one of the best-described study areas, Allleqheny County in Pennsylvania.

Jan 1, 1982

By A. B. Holland-Batt

Deposition velocities play an important role in many fields of engineering and an impressive range of measured data has accumulated in the literature. Numerous workers have addressed the problems that are associated with the prediction of deposition velocity because of its particular relevance to the design of pipelines and pumping stations. However, despite this extensive effort, the calculation of friction heads still requires reference to experimentally based graphs to determine Durand factors and there is still scope for improvement in the prediction of the deposition velocity at different solids concentrations. Equations to characterise the relationship between the Durand factor and the volume concentrations of solids in the slurry are presented. A correlation for the prediction of the deposition velocity is also developed and tested against some of the classical experimental data. The application of predicted values of deposition velocity in assessing the potential for the sanding-up of spiral separators is then described and a case study is presented.

Jan 12, 1992

By Ernesto Maldonado, Zach Agioutantis, Jesus Romero

Reliable prediction of dynamic deformations is important when planning to undermine important structures that cannot tolerate large relative deformations and or large horizontal strains. This paper presents examples of dynamic deformation profiles that include subsidence, horizontal displacements, and horizontal strain for a number of points over a retreating longwall panel in the eastern United States. The prediction points are located at different distances from the excavation rib and therefore present a different response in terms of dynamic movements. The formulation is based on the influence function method as implemented in the Surface Deformation Prediction System (SDPS). Results show that the subsidence development curve may be reliably predicted for surface points in the middle of the panel. However, in cases where points may be affected by the magnitude of the edge effect offset, subsidence predictions may be very conservative, that is, higher than actual movements.

Jul 1, 2023