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By A. Wright, P. Lacombe, R. del Villar, D. Hodouin, C. Sepulveda

"The data acquisition from industrial plants is a fundamental step for process modelling in process optimization, process analysis and automatic control.The introduction, some years ago, of modern distributed control and data acquisition systems in concentrators imposes a new approach in designing experimental schemes for data acquisition campaigns and new criteria for the treatment of the obtained discrete information.This paper presents the methodology applied to sample a zinc flotation circuit at Les Mines Selbaie, Joutel, PQ. Values of 85 process variables were obtained form the Distributed Control and Data Acquisition System (Bailey Network 90) and transferred to the DOS environment. Another 11 variables (flowrates and valve positions) were directly sampled using an IBM-PC compatible computer equipped with a data acquisition card and a signal processing circuit.All variables, under the vector form, were processed in the PC-MATLAB environment to either filter or eliminate the non-linear disturbances and noises, producing useful data for model development. The filtering algorithms are also presented.INTRODUCTIONProcess control does not need to be justified. Whether manual or automatic, any processing plant requires some form of control to accomplish its main objectives: a good quality product and a target throughput. Process control of mineral processing plants has steadily evolved from manual control, where the operator skills and/or experience play an important role, to some level of automatic control, usually considered better, as less human subjectivity is involved, at least when actions have to be taken.The design of these control systems requires the knowledge of the dynamics of the process it is deemed to control. Process modelling is therefore an essential step in the assessment and the design of a control strategy. The choice of model will depend on the available resources and manpower."

Jan 1, 1991

By I Canbulat, J Emery, L Yao and C Zhang

This paper documents a process of data analytics and machine learning applied to coalmine roof convergence data. It follows a publication documenting the formation of the database. For each set of analyses Python scripts were utilised to extract data from an SQL database, with subsequent analyses also executed in Python, utilising open-source tools such as scikit-learn. Preliminary analyses with classic machine learning algorithms included multiple logistic regression, K-nearest neighbours, decision tree, random forest, support vector machine, kernel support vector machine, artificial neural network (ANN), naïve Bayes and multiple linear regression. Results indicated that relationships were complex and non-linear, with the artificial neural network most suitable. An ANN model was then refined through a standard process of hyperparameter optimisation and data augmentation to arrive at a final model. Due to the black box nature of the ANN further insight into how the parameters interacted was sought and found with Shapley additive explanations (SHAP values). SHAP values utilise Game Theory, where each input parameter or feature is a ‘player’ while the data set is the ‘team’. The SHAP value is the impact of each player on the target value, essentially establishing the contribution of each input parameter. The results from all analyses are delivered in the paper, together with insights, applications, strengths, and limitations of the methodology.

Nov 29, 2022

By Greg Sheppard, Eduardo Nebot

Australian researchers have developed new technologies aimed at improving safety and productivity at openpit mines. A data acquisition and feedback system for electric rope shovels has been shown to substantially reduce cycle times and decrease excessive machine duty caused by stalls. This data acquisition system was developed by the Cooperative Research Center for Mining Technology and Equipment (CMTE). It has been tested on a P&H 4100A shovel at Coal & Allied’s Hunter Valley operations and a P&H 4100XPB at BHP Billiton Mitsubishi Alliance’s Goonyella Riverside Mine in Central Queensland. In addition, CMTE member P&H Mine Pro Services has used the technology to acquire field data from a P&H 4100XPBrope shovel at RAG’s Belle Ayr Mine in Wyoming’s Powder River Basin.

Jan 1, 2002

By Márcio Aurélio dos Santos

A Automação Industrial utiliza de forma intensiva os mesmos equipamentos e softwares de TI como servidores, historiadores, switches, firewall, dentre outros. No entanto, são poucas as áreas de automação que aplicam de forma eficaz todos os controles e cuidados que esses ativos requerem. Neste cenário, a garantia de altos níveis de disponibilidade dos sistemas de automação inicia-se pelo ambiente físico no qual tais sistemas estão instalados. É comum a improvisação de ambientes inadequados dentro das áreas industriais ou a instalação em subestações elétricas que não foram construídas com o rigor necessário para receber os ativos de TI Industrial utilizados nos sistemas automação. A solução para manter elevados os índices de disponibilidade destes ativos é, sem dúvida, instalá-los em Data Centers, comumente utilizados nas áreas de TI, o que é facilmente justificável pelo fato de que a indisponibilidade dos ativos de automação representa um impacto maior aos resultados das indústrias ao se comparar com a indisponibilidade dos ativos de “TI Corporativa”. Assim, este trabalho conceitua um ambiente de Data Center, discute as particularidades relacionadas à Automação Industrial (projetos de cabeamento estruturado e as interligações entre os níveis 1 e 2 de Automação) e apresenta as estratégias que a Samarco Mineração está adotando com relação ao tema.

Jul 30, 2018

By G. S. Hill

A large amount of the stereological error associated with liberation studies can he eliminated by assessing areas instead of linear intercepts. The presentation of a simple geometrical model as a stereological 'worst case' allows calculation of the worst possible grade/recovery curve for a given assemblage of areas. The true results are expected to lie in the window between this worst case and the hest possible case, represented by uncorrected data. The width of this window typically corresponds to an error margin of about 5% grade at a given recovery. An indication of where the data lies within the range of possible grade/recovery results may he obtained from a qualitative analysis of the locked section complexity. Assemblages consisting of texturally complex locked sections (ie. containing many mineral nuclei) are likely to exhibit minimal stereological bias and therefore will tend to lie close to the uncorrected side of the grade/recovery window.

Jan 11, 2011

By S. Möllerherm

"The evaluation of data and information provided by the existing traditional indicators in sustainability assessments is most important. Therefore the Institute of Mining Engineering I from the RWTH Aachen University developed indicators and an assessment system for the mineral raw materials industry on operation, company and sector level on behalf of the Federal Ministry of Economics and Labour (Bundesministerium für Wirtschaft und Arbeit, BMWA) and the Federal Institute for Geosciences and Natural Resources (Bundesanstalt für Geowissenschaften und Rohstoffe, BGR). In the course of the development of indicators for the raw materials industry quality indicators on different levels of the sustainability assessment procedure have been identified and developed. The following paper describes some of the results focussing on the development and integration of quality indicators into sustainability assessment systems."

Jan 1, 2005

By S. Dessureault, J. Porter, M. Woodhall

Information and communication technology (ICT) is seen as a key source of future productivity improvements in mines. The information technology infrastructure (ITI) necessary to achieve these productivity improvements will come in the form of data integrated from multiple source systems throughout the mining value chain. Two pure approaches are available: supplier-controlled or operations controlled data structures; each strategy has its own advantages and disadvantages. Case studies involving the South African mining industry are used to demonstrate the advantages of using hybrid-controlled data structures. Depending on the level of operational control, new skills are also necessary to be learnt by mining professionals including: information engineering; creating consistent and quality data; and process improvement techniques that use the integrated data for improving the operation.

Jan 1, 2004

Today?s Topics ? Data to decision value chain ? Practical Considerations ? Example ? Before / After DSS ? Quick Decision Support Demo Practical Considerations ? More data is not necessarily better ? Organization and process silos ? Good vs. ?optimal?, but impractical Solutions

May 1, 2008

By Laurel E. Blackman

The DFI Project Information Management Systems (PIMS) Committee cooperated with the US Army Corps of Engineers (USACE) to develop a specification for PIMS used on USACE projects. Once completed the Committee developed a more generic version of this specification for general use, which is contained in this document. This first edition has simply removed the USACE-related requirements from the document. The committee will continue to work on this specification to enhance the content and also incorporate recent technological development for these systems. It is the Committee's intention that a second edition will be issued in 2022.

Jan 1, 2021

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 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 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 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