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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 Randall Morris

After years of steadily declining employment and production, the U.S. coal industry is having a moment. In 2016, when Donald Trump was running for U.S. President, he cast a spotlight on coal mining in his campaign speeches, promising to end the “War on Coal.” Fast forward to 2025. On the first day of his second term, President Trump signed the National Energy Emergency Act, which emphasizes domestic energy production as a matter of national security and focuses on incentivizing domestic energy resources. Then on April 8, President Trump issued an executive order opening federal land to coal mining to reinvigorate “America’s beautiful clean coal industry.” As the nation seeks to identify reliable solutions to meet its ever-growing energy needs, the administration is going all in on proven, reliable and readily available natural resources, including coal. This means the coal industry is positioned to play a pivotal role in the administration’s stated energy policy, and highwall mining could be an integral factor in helping the industry optimize the extraction of ore to make the most of this moment. The White House noted in a statement that the United States’ “abundance of energy and natural resources … have historically powered our Nation’s economic prosperity” and vowed to remove regulations that have hindered developing these resources — regulations that resulted in job reductions and higher energy costs for U.S. consumers, while weakening national security. “There has been an increase in electricity prices in the United States of over 30 percent in the past four years,” said Rich Nolan, president and chief executive officer of the National Mining Association (NMA). “This administration wants to focus on that, inflation and on increasing electricity availability to help lower the price over time by incentivizing energy policy. Coal is definitely a high priority for this administration.” Part of the White House’s strategy is to facilitate investment in coal-fired plants. The shift comes at a time when the reliance of coal-powered energy plants has been waning. According to the U.S. Energy Information Administration, 7.5 GW of electrical capacity from coal-fired plants was retired in 2024, following the retirement of 22.3 GW of electric generating power the two years prior.

Jun 1, 2025

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 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

A review of all major TBM projects from 1974 to 1990 has been undertaken and the results entered into a database. A similar database was prepared from information obtained from a principal TBM manufacturer covering the period 1952 to 1990. The database includes information such as machine specifications, machine performance data, tunnel specifications and ground characteristics. The database can be used as a guide to gauge potential machine performance in a range of ground conditions and tunnel geometries. The review also indicates that current TBM technology is limited to certain ground strength conditions.

Jan 1, 1993

By Xin J

In China, the State-run mines are currently practising an information card management system, to collect data about mine production and safety condition. Since large volumes of data will be obtained every day, efficiently and effectively processing data and sorting out valuable information will take enormous resources of manpower, if by hand. Therefore, an intelligent database system with easy data inputting was developed to assist information processors in data classifying, filing and retrieving. For analyzing and improving production performances, reliability models and production functions were embodied. The system was implemented on AST-286 in DBASE M. Preliminary applications have proved effective and successful.

Jan 1, 1991

By Deborah A. McCombe

"There are a number of important dates that must be disclosed when a Qualified Person (QP) is preparing a technical report for securities purposes. The dates are discussed in NI43-101 and the Form of the Technical Report, NI43-101F1. •,Effective date of the technical report •,Possible revision date of the report •,Effective date of mineral resources and mineral reserves •,Date of the personal inspection (site visit) •,Signature date of the date report (execution date) •,Date of the certificate of the QP •,Date of the consent of the QP The effective date of the report is basically the cut-off date for the information that is included in the technical report. The effective date should be close to the signature date. If there is too long a period between these dates, the QP runs the risk of new material information becoming available and then the technical report would no longer be current. The effective date is disclosed on the title page of the report and on the signature page. If a technical report needs to be revised to correct problems or disclosure deficiencies identified by the regulators, the QP will normally need to include a revision date on the title page of the report to distinguish the revised version from the original version. In these circumstances, the regulators will request that the original date of the report remain on the title page and the revised date will be below it. When a technical report must be revised, the QP must resign and date the signature page of the report and provide a new certificate and consent."

Jan 1, 2003