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By Richard Thomas Newcomb

The trend towards proportionately rising volumes of surface mining through- out the world and especially in the United States has led some observers to predict that the future of underground mining is quite limited. Certainly, the simple extrapolation of current trends1 gives evidence of a compelling sort for the hypothesis that technological progress is proceeding at a faster pace in surface than in underground mining. This paper attempts to examine the basis for these predictions by searching the historical record on differential rates of productivity as these are observed in iron ore and coal mining. In recent years economists have discussed "technology gaps" among industries or segments of the same industry, by which they mean that mechanisms for invention and its diffusion are faulty or retarded in such a way that feasible cost-cutting innovations are not applied or developed, even though technicians regard them as practical.2

Jan 1, 1968

By J. L. Hoover

The goal of the drill bit consumption study was to develop an equation(s) based on various components that affect bit drill wear and use the equation(s) to estimate the consumption of bits during production drilling. Drill bit data from various geographic locations were compiled and used to develop linear regression equations using the Microsoft Excel Software. Multiple drill bit consumption equations were developed and statistical analysis was used to verify the accuracy of those equations. The equations will allow mining companies to more accurately forecast drill bit consumption when evaluating new mine properties. This capability becomes increasingly beneficial to forecasting as the cost of consumables increases.

Jan 1, 2012

By J. L. Hoover, P. W. Conrad, J. Downey, S. Parrow, S. Rosenthal

The goal of the drill bit consumption study was to develop an equation(s) based on various components that affect drill bit wear and use that equation(s) to estimate the consumption of bits during production drilling. Drill bit data from various geographic locations were compiled and used to develop linear regression equations using the Microsoft Excel software package. Multiple drill bit consumption equations were developed and statistical analysis was used to verify the accuracy of those equations. The equations will allow mining companies to more accurately forecast drill bit consumption when evaluating new mine properties. This capability becomes increasingly beneficial to forecasting as the cost of consumables increases.

Jan 1, 2012

By Otto E. Zwanzig

You may think long before this talk has been completed that the speaker is belaboring the obvious. Please let me put to rest here and now any doubt which you may have in this regard by assuring you that is precisely my intent, and for a very specific reason. We all profess to know and recognize the obvious, but I am impressed increasingly how often we forget or fail to act upon the obvious. Shortly after starting my first job upon graduation from college, I was asked by my boss to participate in a course on practical communications, particularly as applied to conveying proposals effectively to the client -- be he customer or company president. The concept or philosophy underlying this program was so simple and obvious that in my youthful brashness I saw only a gross waste by the company of e good, solid $1930 in compelling me to take the course. The sequel to this experience occurred about five yews ago when I was directing a 300-man marketing group. At that time I enrolled a dozen of my department heads, and subsequently about a hundred other personnel, in the contemporary version of that sane program. Why? -- because they all knew the obvious, but I could see how imperfectly they were implementing it. Truly, a wide chasm exists all too often in our beliefs on the one hand and our practices on the other.

Jan 1, 1962

Forecasting, particularly when applied to meaningful issues, is known irreverently as 'futurology'. There is quite a range of opinion ext.ant in the world when mineral resources are looked at. The opinions range from the doomsday view that there will be acute shortages of all essential commodities, to the optimistic outlook that new 'discoveries will prevent any possibility of shortages. The environment is, as yet, only studied locally and quite parochially; surprisingly there is little integration when considering the spatial atmosphere, the seas in depth and the earth's crust.

Jan 1, 1981

By C. Ozgen Karacan, William P. Diamond

In This Chapter [Measuring the gas content of coal Predicting gas emissions based on geologic and coal reservoir property data Determining the gas storage capacity of coalbeds and other gas-bearing strata Methane drainage borehole monitoring to forecast the remaining gas-in-place and the influence on mine emissions Forecasting gas emissions during mining as a function of mining parameters and Gas emission prediction based on numerical simulation] This chapter provides guidelines for determining the gas content of coalbeds, estimating the gas-in-place, and predicting gas flow and emissions before and during coal mining operations. The techniques are discussed briefly in the following sections. However, detailed information on the techniques is provided in the cited references. INTRODUCTION Coalbed methane, if not properly controlled in the underground mine environment, is a safety concern due to the potential risk for an explosion. This is a particular problem during longwall mining, where the high rate and volume of coal extraction can result in the release of large amounts of methane from the mined coalbed and other adjacent gas-bearing strata. The variability and potential hazards of these sometimes unexpectedly high gas flows provide the impetus to develop methods to predict methane emissions into the underground workplace. A forecast of the volume of gas that might be released during coal mining is helpful for designing ventilation systems and for implementing optimum methane drainage strategies to help mitigate expected gas emission problems. A complete assessment of the need for methane drainage prior to mine development generally requires both an empirical and a theoretical approach. If there are active mines in the general area with similar geologic conditions and coal characteristics, a review of gas problems in those mines provides an initial insight into the level of gas emissions to be expected at a new location. In addition, relatively simple methods exist to determine the in situ gas content (volume of gas per unit weight of coal) of the coalbeds in a particular mining area, as well as the gas-in-place (volume of gas in the coalbed(s) within a defined geographic area).

Jan 6, 2006

By C. Özgen Karacan

Gob gas ventholes (GGV) are used to control methane inflows into a longwall operation by capturing it within the overlying fractured strata before it enters the work environment. Thus, it is important to understand the effects of various factors, such as drilling parameters, location of borehole, applied vacuum by exhausters and mining/panel parameters in order to be able to evaluate the performance of GGVs and to predict their effectiveness in controlling methane emissions. However, a practical model for this purpose currently does not exist. In this paper, we analyzed the total gas flow rates and methane percentages from 10 GGVs located on three adjacent panels operated in Pittsburgh coalbed in Southwestern Pennsylvania section of Northern Appalachian basin. The ventholes were drilled from different surface elevations and were located at varying distances from the start-up ends of the panels and from the tailgate entries. Exhauster pressures, casing diameters, location of longwall face and mining rates and production data were also recorded. These data were incorporated into a multilayer-perceptron (MLP) type artificial neural network (ANN) to model venthole production. The results showed that the two-hidden layer model predicted total production and the methane content of the GGVs with more than 90% accuracy. The ANN model was further used to conduct sensitivity analyses about the mean of the input variables to determine the effect of each input variable on the predicted production performance of GGVs.

Jan 1, 2009

By W Zirima

A detailed, mine scale non-linear numerical model of Rosebery mine has been built for the purpose of simulating life-of-mine global deformations. The model was calibrated using observations of displacement and damage, and was then used to investigate the correlation between dissipated plastic energy (DPE) and seismic event occurrence. The study is unique because the seismic data was collected over a relatively short period using a temporary portable seismic system. It is the intent to undertake significantly more work, but the analysis of limited data shows that a very good, non-linear relation between DPE and event probability is present. The observed correlation is consistent with measurements from other mines where more data was available. With more data, the strength of the correlation will allow the DPE analysis to be used during the design and sequencing phase of planning to better design the mine to minimise seismic hazards.

Jan 1, 2008

By T. J. DeMull

The business of heap leaching requires accurate forecasting of metal production from the heaps. Business forecasts typically follow a format that project periodic (monthly, quarterly, or annual) product shipments based on anticipated operating results, e. g., tons of ore placed, grade of ore, and rate of leach extraction. Since revenue and cash flow are dependent on the metal production stream, forecasts of metal production are very important for project evaluation and business planning purposes. Nevertheless, production forecasts, particularly for new heap leach projects, have been prone to errors. The present paper identifies some of the pitfalls that would be encountered in preparing production forecasts for new heap leach operations and provides a checklist of considerations to assist in preparing a forecast. Also, the paper presents a case study of copper production forecasting used for the Sanchez Project, a heap leach operation located near Safford, Arizona, that is presently under development by AZCO Mining, Inc.

Jan 1, 1995

By David D. Eyer

In today's world coal market, coal quality is becoming increasingly important. Often the coal quality parameters are one of the crucial considerations in the development of a coal mine. Because of the user's tighter specifications and because of strict environmental regulations, coal producers are being called on to predict with increasing accuracy, not only the quality of their product but also the variability of that quality. This area of reserve evaluation is naturally receiving a great deal of scrutiny. Consolidation Coal Company has developed reliable and sophisticated methods for predicting coal quality, but the company's efforts continue to search for even better techniques. The following is a step-wise summary of The methods currently used by consolidation coal company, with A recent project used as an example. Two of the newer, and as yet experimental techniques will be briefly discussed.

Jan 1, 1980

By A Vakili, F Harvey, H Greaves

Mine seismicity is one of the most difficult geotechnical risks to manage in seismically active mines. There are currently some established empirical and statistical methods that provide quantitative measures for seismic hazard assessment in operating mines. However, these methods are mostly suitable for quantifying the current or historical seismic hazard and often cannot be directly used to forecast the future hazards. Numerical modelling is the only analytical tool that can capture the level of complexities required for a meaningful analysis of mine seismicity and therefore to facilitate a reliable forecast of future hazards. However, there is currently no widely accepted and established guideline and methodology for analysing and forecasting mine seismicity using numerical modelling. This paper first provides a background on previous published studies on this subject and then outlines the key aspects that can affect the reliability of the forecasting for mine seismicity. These aspects include a) processing of historical seismic data, b) selection of suitable numerical modelling approach, c) Post-processing and interpretation of the model outputs. A case study is then presented for the Gwalia Deeps Mine where a new framework was developed to forecast future mine seismicity. High-quality seismic data for a period of five years was used for back-analysis and comparisons with the numerical modelling outputs. After development of the method, the future seismicity was forecast for the next 12 months and the actual seismicity was monitored against the forecast during this period. Results of this study shows promising potential for the proposed framework to be applied more routinely and at other mine sites. However, more validations are required at Gwalia Deeps mine as the mine proceeds to deeper levels. In addition, the method needs more improvements and should be tested and validated at other mining operations before it can be established as a proven method. CITATION:Vakili, A, Harvey, F and Greaves, H, 2018. Forecasting mine seismicity – towards a more established methodology, in Proceedings The Fourth Australasian Ground Control in Mining Conference (AusRock), pp 207–222 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 30, 2018

By J. Cygankiewicz

Efficient prevention of mine fires is impossible without predicting the self heating processes in the given geological and mining conditions. The respective forecasting method is presented in this paper based on the following assumptions: - factor deciding on fire hazard in the underground workings is the process of heating of coal from the primary rock temperature to the critical temperature when the heating rate growth rapidly - another important factor is the fire incubation time defined as time needed to achieve the critical temperature. This time is determined by balance of heat and mass in the heated coal - contact of crushed coal with oxygen in air cause some exothermic processes as : physical and chemical sorption of oxygen , oxidation reaction and evaporation of humidity Heated coal transfers its thermal energy to the environment by conductivity to the rocks and by convection to the flowing gases. The heating is possible if thermal energy generated from coal is greater then the share transferred to the environment. The paper presents the laboratory method of measurement of heat generation in adiabatic conditions. Also the mathematical model with examples of computation enabling to forecast the self heating process of crushed coal is presented.

Jan 1, 2003

By A. McQuillan, N. Bar

Geotechnical engineers are routinely tasked with advising suitable stand-off distances below highrisk sections of slopes in open-pit mines that are identified to have potential to deform or collapse. Accurate prediction of failed material runout can mean the difference between continuous safe mining and unwanted high-potential incidents that result in loss of production, equipment damage, injury, or loss of life. This paper updates previous empirical relationships presented by the authors for estimating the volume and runout distance of excavated slope failures, in an open-pit mine operation, using slope geometry as the primary predictor. Cases are sourced from varying slope geometries (fall heights up to 385 m, slope angles up to 80°) and a range of commodities (iron ore, coal, nickel, gold, copper, boron, and limestone), excavated in sedimentary, banded-sedimentary, epithermal, and copper-porphyry deposits, across all six inhabited continents. Analysis of these cases identified positive correlations between slope height and runout distance, and slope height and failed material volume. In general, negative correlations were identified between Fahrböschung angle and slope height, and Fahrböschung angle and failed material volume; however, significant scatter is observed in these datasets. A definitive relationship could not be derived comparing Fahrböschung angle with failed material runout. Slope angle was also found to be a poor indicator of runout. Of the parameters analysed, slope height (i.e., fall height) was found to be the simplest and best predictor of runout distance. This paper presents new charts for predicting failed material runout distance for rock slopes. Relationships are defined for structurally and rock-mass driven slope failure mechanisms at average, 75%, and 95% prediction intervals

Jul 10, 2024

By P. M. Solozhenkin, O. I. Ibragimova, I. P. Solozhenkin

Molecular models of complexes of poorly studied bismuth, gold, and arsenic mineral clusters and reagents were created for use in collector activity forecasting using Chem Bio 3D and ChemOffice2005, with optimization by MM2. The density functional theory approach was used to determine HOMO, LUMO, SOMO, and total energies, atomic charges, and s, p, d- populations for various compounds. The structure-based virtual screening (SBVS) and docking technique was proposed to reduce the vast number of compounds to a more manageable number for selection of potential flotation candidates. The prognosis of collector activity evaluation (PCAE) strategy was proposed as a consistent approach to estimate the interaction between a collector and a mineral cluster as the difference between the total energy and sum of cluster energy and collector energy to search the analogue of butyl xanthate. The negative relationship between PCAE and flotability was shown. The combinations of dialkylamino sulfhydryl collectors can be considered as prospective reagents for gold-bearing sulfide ore flotation. These results were confirmed by experimental data in ore flotation. The increase in recovery of gold and bismuth with the aid of a combination of reagents has been established and additional cyanidation of tailings has been avoided.

Jan 1, 2016

By Nick D. Rose

Outline ?Background ?History of inverse-velocity for failure prediction ?Methodolog ?Case Histories ?Three case examples of predicted slopefailure to actual date of occurrence ?One case example of mitigated slope failure using inverse-velocity to forecast failure time and schedule remedial measures ?Conclusions

May 1, 2006

By Richard Newcomb

Since the report of the 1972 Rapid Excavation Conference in the city of Chicago, speculation has continued concerning the estimates of tunneling demand. Little has been published that would persuade me to modify substantially the rather tentative estimates that originated with the National Academy of Sciences-National Academy of Engineering (NAS-NAE) study of 1968 which were recomputed by the United States in the survey of the Organization for Economic Cooperation and Development (OECD) in 1970. The OECD estimates, of course, hedged the really difficult question of forecasting civil construction tunneling demands. In the United States contribution, this was done by predicting both an upper and lower bound for important categories of demand. A discounting procedure was then applied to each of the major sectors of expenditures, permitting a forecast that reflected the current subjective assessment of how extensively and how likely cities were to adopt plans that required tunneling. This was done for water, utilities, transportation and other novel underground facilities. The purpose of this paper is a modest one. In it I wish to describe the efforts made since 1972 to generate more thoughtful and more robust estimates of urban sector demands, and to apply what has been learned to the most difficult category of predictions: those for urban mass transit systems. The estimates of the Underground Construction Research Council funded by the National Science Foundation have been described at an earlier panel of this Conference. The principal effort I am familiar with is that of

Jan 1, 1974

By Anthony Lannacchione

There has been a persistent need to forecast roof falls so that miner's exposure to hazardous underground environments can be minimized. Several monitoring techniques have been developed and are used today with varying levels of acceptance in the mining industry. This paper examines the potential for monitoring microseismic emissions activity as a means of forecasting roof falls. The use of this activity to forecast roof falls has drawn only limited attention, resulting in a lack of published field performance data. This deficiency is being partially addressed by analyzing data obtained during longwall mining at Moonee Colliery in 1998. The Moonee data base contains a wealth of information concerning roof fall forecast parameters and the seismic alarm criteria used to develop these forecasts. Four seismic alarm criteria were developed and used by Moonee with varying degrees of success. Roof falls were forecasted 73% of the time with average forecast times of 54 minutes. Ninety percent of the predicted roof falls had a warning time, i.e., the time between warning and roof fall event, of greater than 1 minute. The fraction of forecasted roof falls decayed logarithmically as a function of warning time, until only 20% of events were predicted more than 100 minutes prior to roof falls. False alarms occurred in 50% of the warnings. Many of the false alarms were quickly followed by a cessation in mining which could have temporarily halted the on-going failure process. If mining had continued immediately after the false alarms, a roof fall may have occurred soon after. The microseismic activity collected .from Moonee Colliery demonstrates that techniques to forecast roof rock instabilities in underground mines are possible.

Jan 1, 2005

By John L. Edwards, Anthony T. Iannacchione

There has been a persistent need to forecast roof falls so that miner’s exposure to hazardous underground environments can be minimized. Several monitoring techniques have been developed and are used today with varying levels of acceptance in the mining industry. This paper examines the potential for monitoring microseismic emissions activity as a means of forecasting roof falls. The use of this activity to forecast roof falls has drawn only limited attention, resulting in a lack of published field performance data. This deficiency is being partially addressed by analyzing data obtained during longwall mining at Moonee Colliery in 1998. The Moonee data base contains a wealth of information concerning roof fall forecast parameters and the seismic alarm criteria used to develop these forecasts. Four seismic alarm criteria were developed and used by Moonee with varying degrees of success. Roof falls were forecasted 73% of the time with average forecast times of 54 minutes. Ninety percent of the predicted roof falls had a warning time, i.e., the time between warning and roof fall event, of greater than 1 minute. The fraction of forecasted roof falls decayed logarithmically as a function of warning time, until only 20% of events were predicted more than 100 minutes prior to roof falls. False alarms occurred in 50% of the warnings. Many of the false alarms were quickly followed by a cessation in mining which could have temporarily halted the on-going failure process. If mining had continued immediately after the false alarms, a roof fall may have occurred soon after. The microseismic activity collected from Moonee Colliery demonstrates that techniques to forecast roof rock instabilities in underground mines are possible.

By S. Avalos, J. M. Ortiz, W. Kracht

Semi-autogenous grinding mills are intensive energy consumers. Current models base their energy consumption inference on operational variables and feeding ore characterization. While they provide adequate design guidelines, they are not suitable for real-time energy forecasting dealing with gaps in operational dataset and up/downstream process bottlenecks. This work explores the capability of Gated Recurrent Units to learn the semi-autogenous grinding mill consumption behaviour. Gated Recurrent Units are trained to forecast different time-support consumptions under a time-series operational dataset. The results show high performance on small supports (30 minutes) to capture local variability and long-trends while larger time supports (8 hours) can only capture time-trends but not local variability. The proposed technique seems promising for further research and implementation on real mine cases since the input variables are low-cost and easy to obtain. INTRODUCTION Current changes in the Chilean energy matrix (Román-Collado, Ordoñez, & Mundaca, 2018), from fossil fuels to renewable energies, have impacted different industries and particularly the mining sector. Solar energy incorporation into greenfield/brownfield projects will help reducing costs and will lead to rethink foundational paradigms (production and processing strategies) (Pamparana et al., 2017). Also, integration of new space-time predicting tools together with automated real-time updating models are some of the main challenges in the geometallurgical framework (Ortiz et al., 2015; van den Boogaart & Tolosana-Delgado, 2018). Those contexts encourage and demand the development of tools capable of predicting the energy consumed by mining systems, being comminution the greatest energy consumer with an average close to 50% of the entire mine consumption (Cochilco, 2013). In comminution, the semi-autogenous grinding mill (SAG) represents the largest energy consumer (~10 nominal each SAG). Theoretical and empirical energy consumption models (Jnr & Morrell, 1995; Morrell, 2004; Silva & Casali, 2015) base their inferences mainly on feed/product size distributions, SAG sizing, bearing pressure, feed hardness, water addition and grinding charge level, and commonly assume steady-state and work isolated from up and downstream processes. While those techniques provide adequate design guidelines, they are not suitable to predict hourly, daily or even weekly energy consumptions on interconnected comminution circuits where upstream/downstream bottlenecks lead SAGs to operate below designed regimes. Forecasting energy consumption is required by short-term energy management, medium and long-term planning energy consumption, designing the nominal capacity of renewable energy sources, among other. Also, when models are input-sensitive and some of those input measurements are expensive and/or time consuming, model robustness suffers when real-time predictions are required.

Jan 1, 2019

By James R. Evans

Forecasting demand is an art as well as a science, and much personal judgment is required. National forecasts made for sand and gravel, crushed stone, and/or aggregate may be misleading or unhelpful locally. This is because of significant variation in demand within different geographic and/or political areas in the US, and/or the use of an inadequate data base and forecasting methodology. The best forecasts may be made for local or regional market areas, irrespective of geographic or political boundaries, provided a proper data base is available.1 The Bureau of Mines compiles detailed statistics on commodities sold or used in the construction industries and, therefore, is in an advantageous position to make national aggregate demand forecasts.

Jan 1, 1979