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By F. Vincent, A. Boulet, M. Noaparast, F. Woodcock, A. R. Laplante

A novel methodology to estimate gold recovery by gravity is presented. It makes use of a population balance model which represents gold liberation, breakage and classification behaviour and applies pre-concentration and recovery data to gravity recoverable gold (GRG) to predict overall recovery. The methodology is designed for gravity circuits located in the circulating load of secondary grinding mills. Gold liberation is estimated by processing a sample of representative ore with a laboratory Knelson Concentrator (LKC) in three successive steps, each at a finer grind than the previous one. Breakage behaviour is represented by a matrix H, which redistributes GRG in the feed of the secondary mill in the various size classes of its discharge. Provision is also made to account for gold overgrinding, which decreases the overall amount of GRG. Classification, pre-concentration and recovery data are estimated from plant performance, and are represented by the diagonal matrices C, P and R. The methodology is demonstrated with case studies of greenfield, retrofit and optimization applications.

Jan 1, 1995

By A. R. Laplante

A novel methodology to estimate gold recovery by gravity is presented. The methodology makes use of a population balance model which represents gold liberation, breakage and classification behaviour and applies pre-concentration and recovery performance curves to gravity-recoverable gold to predict overall recovery. Derivation of the necessary parameters is explained and demonstrated using data from an actual gravity circuit and one projected application. The methodology makes it possible to estimate the performance of circuits with large gold circulating loads and relatively low unit recoveries (e.g. jigs), or with high recoveries on a bleed of the circulating load (e.g. Knelson concentrators).

Jan 1, 1994

By M. Noaparast, F. Woodcock, A. R. Laplante

A novel methodology for estimating gold recoveries in gravity-separation circuits is presented. The methodology makes use of a population-balance model. The model represents gold liberation, breakage and classification behavior and applies preconcentration and recovery performance curves to gravity-recoverable gold. The derivation of the necessary parameters is explained and demonstrated using data from an actual gravity circuit and from a projected application. The methodology makes it possible to estimate the performance of a gravity circuit having a large gold circulating load and a relatively-low unit recovery (e.g., a jig) or having a high recovery on a bleed of the circulating load (e.g., a Knelson Concentrator).

Jan 1, 1996

By Wojciech Klecan, John H. Raymer

Baseline quantities of difficult ground and support types were estimated for the Chattahoochee Tunnel using the Rock Mass Rating system. RMR classifies the ground into five categories, ranging from “very good” to “very poor,” based on five main parameters: intact rock strength, RQD, joint spacing, joint condition, and groundwater inflow. For the Chattahoochee Tunnel, these parameters were independently measured in the cores and shown graphically on the boring logs. A 55 matrix was calculated to obtain the statistical distribution of RMR for the project. Three baseline ground types were defined based on the RMR. Geologic mapping inside the tunnel during construction has verified the accuracy of this method.

Jan 1, 2003

By E. Azrag, A. MacDonald

A hydrogeologic investigation was conducted to predict inflow to underground mine workings and mining-induced hydrologic impacts for a proposed underground mine beneath a lake in northern Canada. Flow and shut-in tests were conducted to determine hydraulic conductivity values and to estimate the hydraulic head. A conceptual hydrogeologic model was developed and incorporated into a numerical ground-water flow model (utilizing MINEDW, a three-dimensional, finite-element code with special attributes designed for mine dewatering). The mining sequence was represented in detail(e.g., driving ramps and drifts, extracting panels, and backfilling mined areas). Due to limited field data, a quantitative assessment of uncertainty was implemented to estimate limits of possible inflows.

Jan 1, 2003

By John H. Raymer

The accuracy of inflow estimates depends largely on how well permeability is characterized. Permeability is best evaluated as a statistical distribution. For the Chattahoochee Tunnel, this distribution was obtained with a large number of packer tests performed in 50 boreholes along the alignment. The distribution of packer-test data may not accurately reflect permeability, however, due to the limits of the test method and the luck of the field investigation. These discrepancies may be overcome by using log-normal plots of the data to develop a permeability model that will be use din lieu of the data to calculate inflow.

Jan 1, 2001

By Nn Li

Energy consumption in commercial buildings has been increasing rapidly in the past decade. The knowledge of future energy consumption can bring significant value to commercial building energy management. For example, prediction of energy consumption decomposition helps analyze the energy consumption patterns and efficiencies as well as waste, and identify the prime targets for energy conservation. Moreover, prediction of temporal energy consumption enables building managers to plan out the energy usage over time, shift energy usage to off-peak periods, and make more effective energy purchase plans. This paper proposes a novel model for predicting heating, ventilation and air conditioning (HVAC) energy consumption in commercial buildings. The model simulates energy behaviors of HVAC systems in commercial buildings, and interacts with a multiagent systems (MAS) based framework for energy consumption prediction. Prediction is done on a daily, weekly and monthly basis. Ground truth energy consumption data is collected from a test bed office building over 267 consecutive days, and is compared to predicted energy consumption for the same period. Results show that the prediction can match 92.6 to 98.2% of total HVAC energy consumption with coefficient of variation of the root mean square error (CV-RMSE) values of 7.8 to 22.2%. Ventilation energy consumption can be predicted at high accuracies (over 99%) and low variations (CV-RMSE values of 3.1 to 16.3%), while cooling energy consumption accounts for majority of inaccuracies and variations in total energy consumption prediction.

Aug 1, 2013

By Y. C. Kim

Three rock fracture parameters define three points along empirical cumulative size (of rock fragments) distribution curves at Inspiration copper mine. These parameters are: rock quality designation (RQD), longest piece of intact rock and % of +1 inch. The RQD and the longest piece are actually measured on drill hole cores whereas % of +1 inch is visually estimated on the same cores. These size distribution curves are then utilized in a variety of engineering applications such as; slope instability analysis, blast design, copper recovery prediction from heap leaching, etc. The three rock fracture parameters for two dominant rock types (porphyry and schist) in the pit had to be estimated separately due to different rock fracture characteristics.' Both ordinary kriging (OK) with measured hard data only and soft kriging (SFTK) with hard and soft data were used to estimate each parameter. The cross validation of 100 measured sample values served as the-basis for evaluating the potential benefits of SFTK over OK. Use of subjective information during SFTK required obtaining (prior) probability distributions for all three rock fracture parameters. These distributions were obtained from the typical rock fracture parameters observed in drill hole cores falling inside the geologically interpreted structural domains with varying degrees. of fracture intensity and also through direct mapping along the exposed bench faces. Two different types of soft information were developed for this study; namely, Type "A" and Type "K". The former is based on sample distribution of fracture parameters whereas the latter is based on two step estimation designed to narrow the range of variation observed in data Type "A". The results of the cross validation were analyzed using a linear regression of actual values versus estimated values. The results confirm an improvement on the estimates made by SFTK with hard and. both types of soft data over those produced by OK with hard data only.

Jan 1, 1989

By İsmail İnce

The purpose of this study is to investigate the index-mechanical properties of igneous rocks by using the electrical resistivity method. Electrical resistivity, index (P-wave velocity, dry density, and porosity), and uniaxial compressive strength values of 48 different igneous rock samples were measured in the laboratory. Simple regression analysis was performed between index-mechanical properties and electrical resistivity values. Strong exponential and logarithmic correlations were found between electrical resistivity and the index-mechanical properties of the igneous rock samples.

Jan 21, 2022

By J E. Everett, D Linden, P Maney

In response to the implementation of a new schedule for iron ore fines in the International Maritime OrganizationÆs (IMO) International Maritime Solid Bulk Cargoes (IMSBC) Code, improved measures for the management of moisture have been developed. In particular, prediction of cargo moisture allows management of iron ore fines within the supply?chain to facilitate the safe?shipping of iron ore fines.An ARIMA model, augmented to allow for shipment tonnage, has been developed to predict the moisture level from previous shipments of the same product. The model explains about 60 per cent of the moisture variance. It was found that inclusion of other available information, such as ore composition, train/rake assays and recent rainfall at port and mine, did not add to the predictive power of the model.CITATION:Everett, J E, Linden, D and Maney, P, 2013. Predicting iron ore fines shipment moisture, in Proceedings Iron Ore 2013 , pp 17-26 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 12, 2013

By D Roldpo, M Vidigal, J F. C L Costa

The local cumulative distribution function (lcdf) of a selective mine unit (SMU) can be estimated by conditional simulations using the statistics derived from the sample points, generating multiple equally probable scenarios. Conditional simulations permit the evaluation of the lcdf at any support, taking into account the spatial variability, ie the variogram, and the two statistical moments, mean and variance, of the conditioning samples. The SMU ore losses and dilution depend on the mining method and mining equipment selectiveness that, in its turn, depends on the predicted block size. A new approach to predict ore loss and dilution was developed in this study. Monte Carlo simulation is used to randomly generate a grade value derived from the lcdf at the mining stage (Zs_mc(t)) in order to make a decision about the SMU destination, ie processing plant (ORE) or waste dump (WASTE). If Zs_mc(t) is lower or equal to the cut-off (Z_cut-off) the block is ORE and, conversely, if Zs_mc(t) is higher than the cut-off (Z_cut-off) the block is WASTE. Considering these binary destinations ORE/WASTE, at the mining time t, and combining each realisation of the simulated random function Zcs_i (X), there are four possibilities for each SMU simulated value: 1. the block was simulated as ore and is actually ore (OO), 2. the block was simulated as ore and is actually waste (OW),3. the block was simulated as waste and is actually ore (WO), and 4. the block was simulated as waste and is actually waste (WW).The dilution factor is given by possibility 3 (WO), while the ore loss factor is represented by possibility 2 (OW). These results comprise the classical classification and reconciliation plots used in the mining industry. The new procedure proposed in this paper is based on the use of all the equally probable simulations to define the ore loss and dilution factors. An application of this methodology was carried out at Vale's iron mine in Brazil. The grade of silica was simulated and 50 multiple equally probable 3D models were generated. The mean and the standard deviation of the ore tonnage (OO + WO) for the 50 realisations, was 233 Mt and 0.5 Mt, respectively. The mean grade increased for the diluted ore (OO + WO) as well as their respective coefficient of variation (CV). The CV increases from 62 per cent in the undiluted ore (OO), to 101 per cent in the ore, considering the dilution factor (OO + WO). The mean ore loss (OW) was 10 Mt and the waste tonnage dilution (WO) was 9 Mt.

Jan 1, 2009

By M I. Pownceby, N A. S Webster, R Pattel, N Ware

The decrease in quality of Australian iron ore, coupled with the demand for more efficient energy use, necessitates closer monitoring and optimisation of process conditions for iron ore sinter production. Sinter properties are correlated with the mineralogical content and traditionally, quality control has relied on methods such as Fe2+ determination, point counting via optical microscopy and automated electron beam techniques such as QEMSCAN; these can have a typical turnaround time of several days between sampling and reporting of results. As well, there is additional uncertainty around quantifying some of the complex mineralogical phases in sinter such as silico-ferrite of calcium and aluminium (SFCA) and SFCA-I, which are key phases influencing sinter quality. There is a clear need for the implementation of alternative analytical methodologies that offer a turnaround in hours or even minutes to provide information to accurately maintain production conditions.X-ray diffraction (XRD) is a technique widely used for the quantitative mineralogical phase analysis (QPA) of ores and processed materials in the iron ore industry. Partial least squares regression (PLSR) analysis methods have recently been suggested as an indicator of sinter Fe2+ content from powder XRD data without the need for rigorous and often time-consuming mineralogical analysis by the operator. In this study we provide results from the implementation of PLSR to predict the strength of iron ore sinter. Results are sourced from a well-constrained data set comprising strength properties (as quantified by a tumble index (TI)) from a series of compact laboratory test samples. Using this data set it was determined that PLSR has potential to be used as a method for calculation of sinter TI from powder XRD data and, therefore, process control.CITATION:Webster, N A S, Pownceby, M I, Ware, N and Pattel, R, 2017. Predicting iron ore sinter strength through X-ray diffraction analysis, in Proceedings Iron Ore 2017, pp 331–334 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 24, 2017

By G. E. Agar

This paper deals with the prediction of locked cycle flotation results from data developed from individual batch tests. A postulate is made that the distribution of material in a separation stage is determined by the chemical conditions and the flotation time and that this distribution can be characterized by split factors which are constants for a fixed set of conditions. With this premise, it is demonstrated that a steady-state balance can be calculated for a circuit. Although the necessary equations can be solved algebraically, a computer program was written that uses an iterative technique to arrive at the steady-state balance. The program is written so that a variety of circuits can readily be simulated. A number of examples are examined to demonstrate the validity of this simulation procedure. The sensitivity of the material balance to changes in the split factors was examined. Data developed from different flowsheets are presented on sulphide ores, on copper-nickel separation and on silver flotation.

Jan 1, 1978

By G. E. Agar, W. B. Kipkie

"This paper deals with the prediction of locked cycle flotation results from data developed from individual batch tests. A postulate is made that the distribution of material in a separation stage is determined by the chemical conditions and the flotation time and that this distribution can be characterized by split factors which are constants for a fixed set of conditions. With this premise, it is demonstrated that a steady state balance can be calculated for a circuit.Although the necessary equations can be solved algebraically, a computer program was written that uses an iterative tec1nique to arrive at the steady state balance. The program is written so that a variety of circuits can readily be simulated.A number of examples are examined to demonstrate the validity of this simulation procedure. The sensitivity of the material balance to changes in the split factors was examined. Data developed from different flowsheets is presented on sulfide ores, on copper-nickel separation and on silver flotation,"

Jan 1, 1978

By D. A. Swanson

Predicting when and at what rate seepage will occur from waste-rock facilities is becoming an integral part of permitting and closure at mines situated in dry climates. Such predictions require development of a conceptual framework that accurately describes how water will percolate through a waste-rock facility. Also needed is a detailed hydrological characterization of the waste-rock facility, including measurement of the hydraulic properties of the waste rock and any potential cover materials. This paper presents a conceptual framework describing unsaturated water flow in a waste-rock facility and describes methods for characterizing the hydrology of a waste-rock facility in a dry climate. The methods are designed to collect site-specific data to support accurate and defensible predictions of long-term seepage rates.

Jan 1, 1998

By Navid Mojtabai, Jaak J. K Daemen

Some facilities require ground vibration limits that are far below typical vibration levels of interest in most blasting situations. A possibly extreme example of such a facility might be the proposed SSC (Supercollider Super Conductor) high energy particle study system. The applicability of conventional blast vibration predictors for such a low amplitude vibration is highly uncertain. For this preliminary study, a series of field measurements are made, and the results integrated in a set of vibration predictor equations. Ground vibrations induced by blasting operations at one proposed SSC site are monitored at close distances (hundreds of feet), intermediate distances (miles), and long distances (tens of miles) from the blasts. Peak particle velocities are recorded at short distances, ground displacements at other points, as well as frequencies. Theoretical analyses are performed by integrating all data in a variety of ground vibration predictors. From the statistical analysis of the data, ground motions at various points along the ring site are predicted for a given charge weight. The approach taken appears adequate for making tentative estimates of exceedingly low amplitude vibrations at great distances from mine blasts.

Jan 1, 1987

By K Jupp, T J. Howard

Cliffs Natural Resources Pty Ltd (CNR) operates iron ore mines in the Koolyanobbing region of Western Australia. Ore is mined from three locations, separated by many kilometres. The ore is stockpiled at these locations, according to an in-house ore classification system (based on grade and source) and then trucked to the crushing and screening plant at Koolyanobbing. Lump and fines products are railed to Esperance for ship loading and export to Asian customers. CNR prides itself on the relatively low inter-shipment grade variability of the products. The Koolyanobbing crusher is fed using a daily blend plan, generated to maintain lump and fines product grades within acceptable tolerance ranges around targets. Achieving low variability requires predicting lump and fines grades as accurately as practical from the estimated head grades of the ROM ores potentially going into the blend. The grade prediction model may be either a direct prediction of crushed lump and fines grades and lump percentage, or be split into two stages: the bias between blasthole estimated head grade and crusher head grade and then the lump-fines algorithm for splitting the head grade between lump and fines products. The lump-fines algorithm comprises the percentage of lump and the difference between the lump and fines grades. We describe a weighted least squares (WLS) regression model for predicting crusher grades and lump proportion from the estimated head grade for Fe, P, SiO2, Al2O3, Mn and S. The method is applied to CNR production data, where the regression model explains approximately 60 per cent of the variance in the crushed ore grade, for both lump and fines. A further small but significant improvement in prediction can be achieved by including the ore classification data in the model. The regression errors exhibit strong positive serial correlation, indicating trends in grade error across multiple blend records. To compensate for the longer-term error, an exponentially smoothed model was developed and applied to the daily grade blend errors. This gave an increase in the longer-term variance explained and therefore an improvement in grade prediction.

Jan 1, 2009

By Trueman R

If continuous mining systems are to be successfully applied in metalliferous mines then reliable models will be needed to predict rock cutting machine advance and tool wear rates for effective machine selection before mining commences. Many of the predictive models developed to assess cuttability in situ are over-simplistic. As a result they tend to be both site and/or machine specific. Nevertheless very detailed models have been developed for full face machines which are capable of accurate cuttability predictions in a variety of rock mass conditions and for a variety of machine and tool configurations. However, models to predict productivity and tool consumption for partial face machines are not as well advanced. There is a need to develop reliable models for partial face machines such as Roadheaders, the Robbins Mobile Miner and the HDRK-Wirth CMM, as these types of machine are likely to predominate in a mining environment. The paper outlines the current state-of-the-art for predicting cutting machine performance and the work being carried out within the Centre for Mining Technology and Equipment (CMTE), in collaboration with the Australian mining industry, to advance our capability in this area.

Jan 1, 1995

By H. F. Haller, H. C. Pattison

The research program described in this paper is sponsored by the Advanced Research Projects Agency of the Department of Defense and monitored by the Bureau of Mines, United States Department of the Interior. PROGRAM DEVELOPMENT While Holmes & Narver is better known in other fields, the company has been active in many phases of underground excavation for over twenty years. In the 1950's, H&N participated in design and construction of nearly twenty miles of waste disposal tunnels in Southern California. As Architect-Engineer for the Atomic Energy Commission, we had design and management responsibility for many miles of tunnels and for thousands of feet of exploration and big hole drilling, from coast to coast in the States, and for gas well completions and exploration drilling for the Navy and the National Science Foundation from Pt. Barrow to Byrd Station in Antarctica. In my ten years with the company, we contributed to the development of big hole drilling techniques at a number of sites and analyzed the capability of boring machines for many of the tunnel projects. In a 1965 engineering study of a hundred miles of vehicular tunnels in rock under New York City, we investigated

Jan 1, 1997

By J. Hilgers

Metal prices are often predicted by relying on time trend projections of various economic indicators or even the trend in price itself Thus, time is the underlying explanatory variable acting through at best, a limited selection of economic variables. The price prediction method described in this paper differs from this by analyzing hundreds of economic variables to develop a few principle components that automatically capture the fundamental changes in the economy and delete duplicate or irrelevant information. This leaves a few principle variables to describe a state-space representation of the economy which can then be used for prediction. Forecasts of the price of a metal are then made based on these principle variables.

Jan 1, 1995