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By J E. Everett

An essential requirement of product quality control in the mining industry is to be able to reliably predict key quality properties of finished product from the data available before the extraction of the ore. From a production viewpoint, the unit of data collection is generally the input and output data set for each shift of crusher production, but could be any period where mine precrusher data can be reliably matched with product data.This paper demonstrates that linear regression models can be used to predict crush grades from blast grades, even where the crush material is blended from multiple sources or pits for each of which differing regression models might apply. The advantage of using increasingly complex regressions models to match the available data to give increasingly more powerful models is examined. The best model for any application will be a balance between required predictability, available data and the business’ tolerance for complex models.The regression modelling approach has several advantages over the more classic method of run-of-mine (ROM) crusher trials, which will be discussed.The models can use any predictor variable such as grade, geotype and in situ density provided the preextraction data set can be reliably matched with post-crusher data and is significant as a predictor. The models have been used extensively in the generation of the daily crusher plan with the aim of maintaining finished product grade. This approach has also been used associated with exploration drilling and long-term planning.It is acknowledged that there are inherent problems in fitting lump and fines grade to a linear model. However, these problems are minor when they are used for interpolation within the window spanned by the shift blend records used to produce the model. This paper discusses some of the issues limiting linear regression models in this application, and suggests methods enabling consistent models to be formulated

Sep 5, 2011

Predicting Flotation Response from Liberation Information

Sep 13, 2010

By James G. Ulinski, Sebastian Lobo-Guerrero, Vishal B. Patel

"Recently modified design equations used in the transportation industry to calculate side friction and end bearing capacity for drilled shafts are now providing more realistic estimations of capacities than did previous methods. Using multiple case studies and test results from various projects, a more realistic design approach was formulated by the Federal Highway Administration (FHWA), which resulted in greater values of ultimate capacity for side friction and end bearing and in a more efficient design overall.In 2010, the FHWA published GEC-10 – Drilled Shafts: Construction Procedures and LRFD Design Methods, which illustrates a different method of calculating side friction and end bearing resistance and results in greater values for design. In 2014, the Association of State Highway and Transportation Officials (AASHTO) adopted the method put forth by FHWA, which was included in its LRFD Bridge Design th Specifications, 7 Edition. The Pennsylvania Department of Transportation (PennDOT) recently adopted and incorporated the similar methodology as AASHTO for calculating side friction and end bearing for drilled shafts in rock, and these changes are reflected in the 2015 edition of the PennDOT Design Manual, Part 4 (DM-4). This article discusses the past and current design methodology along with a project case study with results from Osterberg Cell (O-cell) load testing, which presents a comparison between the design resistances of ultimate side friction and end bearing and the measured capacities at failure.The Old and the NewEditions of the AASHTO LRFD Bridge Design Manual from 2012 and earlier estimated ultimate side resistance, qs, in rock using the following equation (modified from Horvath and Kenney, 1979):qs=0.65aEpa(qu/pa)0.5 < 7.8pa(f ’c/pa)0.5 where: qu is the uniaxial compressive strength of the rock in ksf; pa is the atmospheric pressure in ksf; aE is the reduction factor to account for jointing in rock; and f ’c is the compressive strength of the concrete in ksi. For intact rock, previous versions of the AASHTO manual required that the ultimate end bearing resistance, qp, was calculated using the following equation: qp=2.5qu."

Jan 1, 2017

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 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 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&apos;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 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 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 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

By R. B. Krog

As part of its mining health and safety research program, the National Institute for Occupational Safety and Health (NIOSH) conducted a longwall methane emission and mining time study at a mine operating in the Pittsburgh Coalbed to access the methane emission consequences of mining a longer face. The methane emission contributors from the mining of a longwall face are: 1) gas released from the coal broken by the shearer, 2) gas emitted from the coal on the face conveyor, 3) gas emitted from the coal transported on the belt, and 4) background gas emitted from the coal face and from the adjoining ribs. Based on the results of the study, a set of site-specific mathematical formulas and constants were developed to characterize each of the four longwall emission contributors. The mathematical formulas were then applied to longer longwall face mining scenarios to predict the methane emissions from these faces.

Jan 1, 2006

By M Astorga, J Jackson, J Gaunt

The Ban Houayxai mine (BHX) is a relatively low-grade, low-cost, open pit gold-silver deposit in Laos operated by Phu Bia Mining, a subsidiary of PanAust. The ore production rate at BHX is 4.5 Mt/a, with direct tipping to a semi-autogenous grinding/ball mill circuit with a carbon-in-leach process plant. Approximately 100 000 oz of gold is produced per annum.The operation is located in mountainous terrain with minimal run-of-mine stockpiling. This results in limited capacity for blending from stockpile, with the mill instead reliant upon direct feed ex-pit. Since commissioning in 2012, the plant has seen significant variation in milling rates due to variability in the feed properties of the oxide, transition and primary ores. As part of an ongoing continuous improvement program, an integrated approach was initiated, focusing on maintaining and enhancing production in the future as the proportion of harder primary ore increases. This will specifically focus on direct blending from the loading face using the ore properties and blast fragmentation to maintain mill throughput.This approach was based on the concept of physical assets management, commencing with improving information and knowledge of the condition of the orebody through modelling the characteristics, variability and performance of the feed for processes relevant to throughput. These models are used to support both mine and process plant production planning.The key ore feed characteristics and parameters modelled for the life-of-mine are blastability index, powder factor, crushability or impact resistance and grindability. These predictive spatial models were based on the data from diamond drill holes used in resource definition and geotechnical drilling programs by integrating geotechnical, geological, geochemical and metallurgical data.At the early stages of implementation, the models are being utilised for ore blending decisions to provide guidance and support for budgeting, long-term mine planning, blast design for mill feed and providing the mill with an expectation of performance.CITATION:Jackson, J, Gaunt, J and Astorga, M, 2014. Predicting mill ore feed variability using integrated geotechnical/geometallurgical models, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 165–176 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

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 F. Chiesa

Experimental results on thin and thick castings poured in aluminum A356 and magnesium AZ91E by the low pressure permanent mold process have been compared to the result of filling simulations for thin and thick castings. If the results of modeling is reliable when the liquid remains above the liquidus temperature, this is no longer the case when partial solidification occurs. The visco-elastic behavior of a partially solidified liquid is difficult to put into equation as it depends on the amount of shear it has been been subjected to. Attempts to predict misruns with a commercial software has been proven to be very difficult if not impossible; divergence of the numerical solution has been observed unless prohibitively long computing times are used. In order to overcome these difficulties from a practical standpoint, a simple method is proposed which consists in tracking the temperature of the advancing liquid metal front so as to make sure it will always be above so-called `Misrun Critical Temperature&apos; (MCT) determined experimentally for thin and thick aluminum A356 and magnesium AZ91E castings. If the liquid metal front falls below this temperature, a cold-shut or misrun is likely to happen at this location. The values of MCT for thin and thick in aluminum A356 and magnesium AZ91E were determined experimentally on misrun castings of extreme geometries (a thin 2.5 mm wall cover and a 6mm to 25 mm thick bell housing). MCT was found to be lower for magnesium AZ91E and for the thicker castings.

Jan 1, 2011

By F. D. Ford, Z. Dai, A. Byers

Manitoba Mining and Milling operations consist of three underground mines (Thompson T1, T3 and Birchtree), feeding the Thompson Mill. Three years ago, a collaborative effort involving Mines Exploration, Mines Project Development Group, Mill Technical Services and Vale Base Metals Technical Excellence Center was initiated to establish and verify the cause of high variability in concentrate quality and Ni recovery at the Thompson Mill. One outcome of this work was a program to systematically characterize the mineralogy and metallurgical performance of current and future orebodies feeding the mill. Mineralogy results show that high value enabling ores typically have a high pentlandite to pyrrhotite ratio and benign silicate mineralogy (mostly quartz and feldspar from sedimentary host rocks), while metallurgically challenging ores contain some combination of pyrite and/or hexagonal (non-magnetic) pyrrhotite and/or elevated quantities of talc (with associated carbonates) derived from altered ultramafic host rocks. The metallurgical performance of these two ore types has been simulated by an Excel particle flotation model, which calculates the relative floatability of particles based on the particle’s mineral composition and mineral locking determined from MLA images. The relationship between these mineral properties measured by MLA and metallurgical performance was established using 13 monthly composite samples from Thompson Mill. The model was then validated with results of laboratory flotation experiments on individual orebodies submitted for evaluation. The validated model can be used to predict plant metallurgical performance based on individual ore bodies feeding the mill. Individual orebody’s grade-recovery curves are assumed to be additive, and can be weighted in proportion to the mill feed to predict mill recovery over the same time period. The ore mixing can be done with a simple Excel spreadsheet and comes with a graphical output for easy visualization of the resulting grade-recovery curve.

Jan 1, 2015

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