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By A D. T Goode

Data Metallogenica (DM) is unique as a global mineral sample collection and deposit database with over 70 000 rock samples and a wide range of descriptive, photographic and measurement data. Subscribers have access to approximately four gigabytes of on-line data with many examples included on the public pages. DM commenced its life on the Pacific rim in 1971 and much recent growth has focused on countries of the Pacific margin, particularly Australia, Fiji, Chile, Indonesia and Peru. Approximately 600 separate deposits and mineral occurrences in this region are represented in DM. The sets of rock samples have been photographed at high resolution and included on the web site, together with unpublished descriptions, deposit cross-sections, bibliographies, field and mine photographs, petrology and reflectance mineralogy. Figures and abstracts from PhD theses are a new addition and the donation of proprietary databases by companies and individuals is an encouraging trend. DM increasingly is being viewed as an acceptable and important repository for æfragileÆ and irreplaceable personal and company data, it is also being recognised in its true light as a high level information system, not solely a collection of rock samples. Data Metallogenica is a not-for-profit venture of AMIRA International that requires self-funding from December 2004. Growth and development must be funded entirely out of subscription and sponsorship revenues because no government subsidy is provided. The Pacific rim is in the mainstream of mining and exploration activities worldwide and those involved have much to gain from a dynamically sustaining DM. The home page at www.datametallogenica.com contains much example data and details of subscription and membership rates.

Jan 1, 2004

By Sylvain Létourneau

Help organizations maximize the benefits of the data that they collect ? perform scientific research in: machine learning, data mining, statistics, artificial intelligence, soft computing, information retrieval, information systems, and natural language processing ? preferred application areas: Equipment Health Management, Bioinformatics, Health, Web Mining ? today?s talk limited to Equipment Health Management While the aircraft is still in the air, IDS: ? clusters relevant WRN, FLR, and snag (pilot) messages ? identifies probable causes based on TSM and provide links to corresponding TSM pages ? assesses MEL conditions (GO, NOGO, GOIF, ?) ? displays relevant maintenance history, suggests fixes based on similar cases ? Commercialization with MxI Technologies Ltd and Air Canada in 2000

May 1, 2008

By S. Dessureault, M. Yildirim

More mining companies are now investing in IT, including updating one of the traditional IT process control tools used in the mining industry: real time in-pit truck allocation control and monitoring systems, or fleet management systems (FMS). These systems have been used to maximize the overall mine production by improving equipment utilization and reducing production costs. Most modern operations monitor the in-pit operations using GPS and wireless communications to produce data. Improvements in bandwidth, sensors, and computing power are causing modern systems to become more complex and generate more data. However, human analysis by simple regression or table generation in spreadsheets of the data is becoming impossible. Data mining solves this data management problem. Data mining finds patterns and relationships in very large data sets. This paper describes the application of modern data mining techniques on truck dispatching systems in a real open pit hard-rock copper mine. The analyses conducted included determining how the trucks performed under particular conditions and allocation systems. The long-term benefits of this work are to identify strengths and improvement opportunities in truck assignment systems and establish the skill sets and IT infrastructure needed in undertaking more complex datadriven technology such as a truck dispatcher trainer.

Jan 1, 2007

By B L. Dickson, E, M F. Lee, P A. Mikula

Computational techniques are needed to assist in the analysis and interpretation of the increasing amounts of geoscientific and mining related data and information that are routinely gathered at mine sites. New interpretation methods are needed to provide an integrated approach so as to establish relationships (cause and effect) or to be predictive about possible outcomes based on acquired data. Traditional multivariate statistical methods are often confused by variable relationships that are non-linear, data distributions that are non-normal, and by the data themselves that may contain missing values, text and both continuous and discontinuous numeric values. Modern data mining methods are available to understand the relationships within and between diverse data sets, and identify relationships or trends associated with processes, measurements and mine design parameters in mines. The self-organizing map (SOM) is a data mining approach with the advantage that all input data samples are represented as vectors in a data-space defined by the observations (variables). The SOM procedure is an exploratory analysis tool that highlights patterns and relationships. Results are internally derived, in an unsupervised fashion based on measures of vector similarity. Our SOM outputs are highly visual, which assists in understanding and illustrating the dataÆs structure and internal relationships. Two studies are presented. The first uses SOM to investigate the relationships between a range of measured and derived rock property parameters and their observed behaviours during mining. Our aim was to determine whether a SOM analysis would allow a better understanding of observed and predicted rock behaviours from their rock properties. The SOM analysis showed that there were indeed relationships between æbrittleÆ, æsugaryÆ, æslabbyÆ and æsqueezingÆ rock behaviours and a number of the input parameters. The computed SOM æstructureÆ was then used to assess the likely behaviour of unknown samples by assigning them into behavioural fields based on their measured and derived geotechnical responses. The second study was aimed at assessing whether a SOM analysis of microseismic data at the Kalgoorlie Consolidated Gold MineÆs (KCGM) Mt Charlotte mine can contribute towards understanding and potentially forecasting/minimising seismicity at the mine-design stage. The database comprised 40 sample æclustersÆ of microseismic parameters from the MS-RAP software package, which were derived from 2500 individual seismic events. The SOM analysis showed that some of the initial mining conditions are related to the seismic parameters. In each of the studies, the SOM procedures assisted in understanding the data and provided new insights. Because of its æordered vector quantisationÆ foundation, the SOM procedure is a substantial improvement over traditional statistical methods in three main areas:its ability to analyse data from disparate sources, including different data types; its ability to analyse sparse data sets with missing values; and its ability to analyse and visualise non-linear relationships within a data set.

Jan 1, 2006

By S. Dessureault

The relatively recent explosion of available data in all business sectors has induced a common expression of “drowning in data, starving for information.” The mining sector is affected by the information age through equipment-based sensors, process monitoring, and traditional enterprise data sources. Information technologists have developed techniques such as data mining that apply new and old statistical algorithms to large datasets. Mine planners and managers can apply these tools for improved planning and control, thereby increasing the use of the highly underutilized information system infrastructure in most modern mines. This paper describes the use of data mining and its enabling technology, data warehousing, in the development of an energy consumption model for large surface mines using real historical data.

Jan 1, 2007

By Austin Metz, Kevin Stanton, Ramin Motamed

"A recent study calibrated Load and Resistance Factor Design (LRFD) resistance factors for local soil conditions in the Las Vegas Valley. As a part of this calibration, an extensive database of drilled shaft load tests and associated subsurface soil information were collected and compiled. This database, deemed the Nevada Department of Transportation Deep Foundation Load Test Database or NDFLTD, was designed with the presentation of load test data, the subsurface soil conditions, and the spatial distribution of the data in mind. In addition, the NDFLTD was developed in a way that allows additional data to be added as it becomes available. As such, the rules of database normalization were followed to allow for additional and different types of data to be added to the database and reports. Microsoft Access was chosen to present the data because it is easily accessible to anyone with Microsoft office software, it has a quick learning curve, and it has the ability to present a relational database. The largest obstacle faced when creating the NDFLTD was normalizing the data used in the LRFD calibration, with the goal of reaching the 5th normal form.INTRODUCTIONLRFD is based on statistical performance of engineering components, as opposed to the more traditional Allowable Strength Design (ASD), which divides the strength of a component by a safety factor. Calibration of resistance factors for LRFD requires a robust statistical analysis of past performance. For drilled shafts this requires a database of full scale load tests (Abu-Hejleh et al. 2014). The FHWA developed the largest US based deep foundation load test database, deemed the DFLTD, collecting data from 1985 to 2003 when funds ran out (Abu-Hejleh et al. 2014). Recently, the DFLTD has been updated to modern standards (Petek et al. 2016). In October of 2007, the FHWA mandated state department of transportations (DOTs) to follow AAHSTO LRFD guidelines for their highway foundation designs (Abu-Hejleh et al. 2014). Since then multiple state DOTs have developed their own, region specific deep foundation load test databases. Florida has a long history of developing databases for deep foundation LRFD resistance factor calibration, with the two main databases being the FL database, and The Deep Foundations Database (Abu-Hejleh et al. 2014). Iowa developed a Microsoft Access based database in 2014 (Garder et al. 2014). Oregon developed a database compiling pertinent data from the DFLTD, Florida DOT’s Deep Foundation Database, as well as from other databases (Smith et al. 2011). Illinois developed the “International Database” for static and dynamic reliability based calibration (Long et al. 2009). Louisiana developed a database for LRFD Calibration of driven piles in 2009 (Abu-Farsakh et al. 2009). The California Department of Transportation has its own internal foundation database, which is housed within the Foundation Testing Branch archive (Smith et al. 2011). In 2016, the Nevada Department of Transportation (NDOT) presented its own LRFD calibration for the Las Vegas Valley (Stanton et al. 2017), and the NDFLTD was developed as a part of that project (Motamed et al. 2016)."

Jan 1, 2017

Jan 1, 1915

By Ken Witherly

Modern airborne EM surveys typically acquire more data than is processed or analyzed at the time of acquisition Suitable additional processing can provide more diagnostic information, thereby allowing for more effective ranking and hopefully, follow-up of outcomes

Apr 1, 2005

The objective of the paper is to develop a material balance calculation procedure for a production unit involving two plants separated by a pipeline. The case study is taken from the oil sands industry. The raw sands are first mixed with water in a conditioning plant, then transported through a pipeline to an oil extraction plant. The combination of pipeline dimensions and nominal throughput creates a significant variable time lag between the two plants. As usual, mass balance calculations required for the evaluation of plant performance indicators, such as bitumen recovery, are inaccurate due to measurement errors. In addition, in the present case study, the time lag of the pipe creates an additional source of uncertainty, which may lead to misinterpretations and wrong decisions. Raw data are usually reconciled through steady-state mass balancing, to correct for measurement errors. However, because of the significant pipeline dynamic lag, standard steady-state daily mass balance may fail at giving an appropriate picture of the operations. The proposed solution to the data reconciliation problem includes the use of a pipeline plug-flow model in the mass balance calculation. To fulfil the industrial constraint that current data reconciliation must not modify past reconciled values, while still satisfying dynamic constraints, a daily one-step method, i.e. a unitary filter-ing horizon, is recommended. The method is illustrated with simulation data, and the results com-pared to those of standard data reconciliation methods.

Jan 1, 2014

By K. El-Ouassiti, M. Zouadi, C. Bazin, D. Hodouin

Data reconciliation techniques are used to attenuate the effect of measurement errors and estimate unmeasured variables. This paper illustrates the application of data reconciliation to hydrometallurgy processes using measurements from leaching and solvent extraction obtained from laboratory experiments. The application of data reconciliation provides an efficient way to detect gross and systematic errors and produces reliable performance indices for process evaluation.

Jan 1, 2005

By A. Alfantazi, M. Mahon

The current efficiency in zinc electrowinning is a key indicator of the performance in a cell house. This is typically measured by comparing the weight of the cathode to the electric current applied over the deposition period. This calculation can be cumbersome because the deposition history of each pulled set of cathodes will be unique. It will depend on the applied current and electrolyte conditions from onset of deposition until the cathode is removed and stripped. Not accounting for these can introduce errors to the calculated current efficiency. To track this history and reconcile the current efficiency, a time based computer process simulation was made of an industrial cell house. Two months of hourly recorded measurements from the cell house were input to the simulation, including current to the cell houses, concentrations, flow rate, and temperature of the purified leach solution. The simulation’s predictions of the concentration of zinc in the circulated, spent, and returned electrolyte all showed good agreement with the industrial data over the two months giving a validated zinc balance. The simulation was then tuned to predict the instantaneous current efficiency for all times, as well as the expected masses for the deposits given their deposition history.

Jan 1, 2014

By Jeffery L. Kohler, Harvinder Singh, Jon G. Bredeson

A survey of electromagnetic noise reports was performed and a summary of the results are given. Industrial concerns were surveyed for techniques to minimize the effects of electromagnetic noise and are presented. The common types of communication links used in mines today are presented. A mathematical development was presented which determines the probability of bit errors versus distance for various types of communication links designed for in mine use. The probability of undetected errors for the protocols of the communication links is given together with burst error detection.

Jan 2, 1981

By B. Samanta

Abstract - Ore reserve estimation, based on sparse drill hole data, was conducted for a placer gold property in Nome, Alaska. A problem with sparse data is that random subdivision of the data into modelling and evaluation subsets (as is commonly done) becomes a problem, as random selection may result in biased/skewed subsets. Therefore, a technique that combined data segmentation with genetic algorithms (GA) was applied to divide the samples into three equivalent subsets: training, validation and testing. Data segmentation was done on the basis of the distribution of gold values. Neural network and a variety of kriging techniques were used to estimate gold grades. A multi-layer feed forward neural network along with "early/quick stop" training was used for neural network modelling. A comparative evaluation of kriging and neural network methods was then performed. The results revealed that neural network was generally superior to the kriging techniques for gold grade estimation in the Nome deposit.

Jan 1, 2002

By C. Sunderman, R. Jacksha

"Mine monitoring through various sensors is a vital component of successful miner safety and health programs. Data from environmental, geotechnical, infrastructure, and other types of sensors are increasingly being used to discover and mitigate health and safety concerns in underground mines. In many smaller underground mines, as well as in new development headings of larger underground mines, leaky feeder communication systems may be the only available means to transport crucial monitoring data. In addition, data transport is increasingly being delivered using Internet Protocol (IP) while older forms of serial communication are being retired. This paper presents the selection, configuration, and testing methodologies that were employed by researchers from the National Institute for Occupational Safety and Health (NIOSH) to integrate commercially available land mobile data radios into an existing leaky feeder communication system to provide Internet Protocol data transport. INTRODUCTION To enhance the health and safety of their mining workforce, mine owners and operators are increasingly installing environmental monitoring and control systems in underground mines. Real-time, mine-wide monitoring is becoming a reality with the implementation of the Industrial Internet of Things (IIoT) into the underground mining industry [1]. However, not all underground mines, especially smaller mines and new development headings in larger mines, have the information technology (IT) infrastructure in place to handle the data transport requirements for the newer IP-based technologies that are part of IIoT. This was the case with a mine in the Western United States; it lacked the infrastructure to perform critical monitoring in remote headings. The mine partnered with the U.S. National Institute for Occupational Safety and Health’s (NIOSH’s) Spokane Mining Research Division (SMRD) to develop and evaluate a cost-effective solution for real-time monitoring of geotechnical conditions in relatively remote sections of the mine. This paper describes SMRD’s solution to leverage the mine’s existing leaky feeder system to achieve IP data transport using commercially available land mobile data radios. DATA RADIO SELECTION The primary selection criteria for the data radios were network data format, modulation type, and radio channel spacing. The network data format decided upon was Internet Protocol. This format allowed for straightforward integration between the IP-enabled data acquisition system located in a remote heading and the network switch located in the control room. It also eliminated the added complexity of additional network hardware such as Ethernet to Serial converters. IP additionally provided a direct path to integration into higher bandwidth networks, such as fiber optic, should they become available."

Jan 1, 2018

By C. Sunderman, R. Jacksha

"Mine monitoring through various sensors is a vital component of successful miner safety and health programs. Data from environmental, geotechnical, infrastructure and other types of sensors are increasingly used to discover and mitigate health and safety concerns in underground mines. In many smaller underground mines, as well as in the new development headings of larger underground mines, leaky feeder communication systems may be the only available means to transport crucial monitoring data. In addition, data transport is increasingly being delivered using Internet Protocol (IP), while older forms of serial communication are being retired. This paper presents the selection, configuration and testing methodologies employed by researchers from the U.S. National Institute for Occupational Safety and Health (NIOSH) to integrate commercially available land mobile data radios into an existing leaky feeder communication system to provide IP data transport.IntroductionTo enhance the health and safety of their mining workforce, mine owners and operators are increasingly installing environmental monitoring and control systems in underground mines. Real-time, mine-wide monitoring is becoming a reality with the implementation of the Industrial Internet of Things (IIoT) into the underground mining industry (Zhou et al., 2017). However, not all underground mines, especially smaller mines and new development headings in larger mines, have the information technology infrastructure in place to handle the data transport requirements for the newer Internet Protocol (IP)-based technologies that are part of IIoT.This was the case with a mine in the Western United States. It lacked the infrastructure to perform critical monitoring in remote headings. Therefore, the mine partnered with the U.S. National Institute for Occupational Safety and Health’s (NIOSH) Spokane Mining Research Division (SMRD) to develop and evaluate a cost-effective solution for the real-time monitoring of geotechnical conditions in relatively remote sections of the mine. This paper describes SMRD’s solution to leverage the mine’s existing leaky feeder communications system to achieve IP data transport using commercially available land mobile data radios (NIOSH, 2013; Forooshani et al., 2013; Novak, Snyder and Kohler, 2010; Yarkan et al., 2009). The solution, fully evaluated in laboratory and mine environments, requires no modifications to the existing system, is economical, simple to implement, and reliable. It also allows for transition to higher-bandwidth IT networks, such as fiber optic or WiFi, should they become available."

Dec 1, 2018

By Frédéric Flament

The aim of this paper is to present the development of a data validation software as component of a commercial mass balance package. Data reconciliation through mass balance is basically an optimization problem constrained by the law of mass conservation. As any optimization problem solved with a modern and robust software application, erroneous information or starting point will always lead to a solution, whether it is realistic or not. Moreover, good information wrongly weighted by an incorrect error model will lead to the same bad ending. In an industrial context where competition requires accurate information, data reconciliation without validation can be disastrous and calls for data validation before and after reconciliation. The challenge in automated data validation resides in the development of general theoretical solutions. Until now, none has been presented and only partial solutions are available. The development presented in this paper incorporates some of these partial solutions. The integration of human knowledge into the application paved the way to a more general solution. The combination ?theoretical validation ? human expertise? proved to be flexible and reliable enough to be integrated in a commercial data validation package. Numerous examples though industrial applications are presented. These applications demonstrate the validation of raw measurements, user support in the development of error models and validation of reconciled measurements.

May 1, 2001

When the writer first began his search for the early history of coal he was amazed at the paucity of information in literature about it, and while after six years many of the reasons for such a scarcity are understood, there are still many cases, even within the last two centuries, when one who can appreciate the tremendous part coal has played in the development of modern times, finds it hard to realize how little attention was paid to this material in those early clays. Occasionally a traveller or observer is found who foresaw how valuable coal was to be when industries were developed, but these were few and to most of those recording its occurrence it was just another mineral. George Washington was farsighted in his view of the development of this country and the need for means of transportation across the mountain barrier, but it was at least sixteen years after he must first have known of coal before he made any reference to it in his journals. He did, however, knowingly or not, select some valuable coal properties in the lands lie obtained in the West. This was then almost entirely an agricultural country, and what industries there were found a great abundance of wood fuel within easy access, there were no steam plants, and all iron was made with charcoal; in fact, the "captive mines" of the iron industry then were large timber "plantations," and when these were exhausted the furnace usually was abandoned and another one built elsewhere, as transportation of charcoal was quite expensive. Along the seaboard the cities and industries began to import coal from England and Nova Scotia early in the eighteenth century, but until the war of the Revolution began the total quantity of coal used yearly in this continent did not exceed nine thousand tons. In 1800 only two places, Pittsburgh and Richmond, Virginia, were using coal to any extent for domestic purposes and it was much later. than this that the amount of coal used in Philadelphia, New fork, and Boston exceeded that of wood. Practically all of the early travellers and writers mentioning coal are noted in the following pages. Several Englishmen who were evidently familiar with its use and value at home, mention its presence as being of great future promise to the young nation, but to nearly all it was more or less of a curiosity and the smoke caused by its use an abomination, and it

Jan 1, 1942

By F. Wang, P. Rosario, A. Vazirizadeh

Advances in data science, machine learning, and artificial intelligence are transforming mining and mineral processing, and making them more algorithm intensive. The paradigm is shifting from one of detection and control to one of prediction and optimisation. To build up reliable prediction and optimisation models these steps should be passed: collect, aggregate, cleanse, and process large amounts of complex, structured, and unstructured data as a first stage. Next, the right tools must be used to extract meaningful features from the operational data for the build-up of a prediction model. A grinding circuit is one of the challenging areas in terms of determining its overall efficiency and the prediction of product quality. In a processing plant, some of the KPIs are typically unmeasurable or infrequently measured for real-time application, e.g. ore grindability and grind size. However, these variables are required to calculate the grinding circuit efficiency and to model the operation performance. In this paper, advanced analytic tools have been used to extract one selected KPI (cyclone overall P80 or final grind size) and its relationship with measured variables for an industrial dataset. The results illustrate that the performance of the grinding circuit can be predicted for real-time monitoring by means of measured data. Keywords: Grinding circuit, data-driven, machine learning, process modelling, hybrid modelling

Jan 1, 2020

By M. Szumny, P. Mertuszka, K. Fuławka

Multi-face destress blasting is the basic method of reducing rock bursts hazard in Polish copper mines. Blasting operations of this type are carried out regularly, twice a day in order to minimize the geomechanical threat while maintaining high extraction efficiency. Unfortunately, the assessment of the effectiveness of this type of work until recently was based solely on determining whether a given destress blasting triggered a seismic tremor or not. Information on the characteristics of seismic waves generated by blasting has so far been omitted in analyzes of this type. The lack of reliable knowledge about the generated seismic effect makes the introduction of constructive corrective actions impossible, which in turn significantly reduces the triggering potential of conducted destress blasting. This study presents the results of comprehensive work aimed at increasing the effectiveness of active rock burst prevention in Lubin underground copper mine. For this purpose, an innovative system for continuous monitoring of seismic waves induced by multi-face blasting was developed, and a new method for seismic assessment of the effectiveness of the destress activities was developed. Based on the monitoring data and the comprehensive analysis of waveforms, the effectiveness of individual blasting operations was determined, and on this basis, modifications to the drilling and blasting patterns aimed at increasing the generated seismic effect were proposed. The use of innovative methods of monitoring and data analysis made it possible to increase the effectiveness of the conducted activities, in terms of the magnitude of seismic vibrations, up to 250%. It should be emphasized that the introduced modifications were aimed at increasing the effectiveness of blasting operations while maintaining the current level of costs. As a result, the proposed solution turned out to be highly effective, significantly affecting the safety of the work carried out in Polish copper mines. Thus the proposed approach is planned to be implemented in other Polish copper mines belonging to KGHM in the near future

Jan 1, 2024

By C Zhang, R Liang, I Canbulat, S Saydam, B Li

The integration of digital twins in mining represents a significant advancement towards enhancing operational efficiency and decision-making. Central to this development is the role of visualisation, a crucial element in mining digitalisation. This paper aims to develop a data-driven visualisation solution for the construction of mining digital twins, highlighting how this innovation revolutionises the representation of physical mining environments. It spotlights the shortcomings of conventional visualisation methods, particularly their inability to accurately depict complex underground environments and their predominantly historical orientation. To address these challenges, this paper proposes a novel, data-driven approach. This approach extensively utilises CAD files, encompassing data from geological surveys to intricate mine plans, thereby significantly elevating the precision of digital twins. It supports real-time updates and enables flexible interaction with a variety of data types. Moreover, it introduces a comprehensive data diagram alongside the adaptation of a 3D tile index. This strategy ensures the efficient rendering of extensive mining environments, allowing for a more dynamic and interactive digital twin model. In conclusion, the implementation of data-driven visualisation in the construction of mining digital twins marks a pivotal advancement in the industry’s digital transformation. It promises a more detailed, accurate, and dynamic depiction of mining operations.

Sep 1, 2024