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By Alexander Malahoff

An international research team working through the Hawai?i Undersea Research Laboratory (HURL) undertook a bold challenge to explore the chemistry, geology, mineral formation and superheated hydrothermal vents of the Kermadec Island Arc. With the mothership, R/V Ka?imikai-o-Kanaloa, scientists from six research institutions explored the active submarine volcanoes stretching over a line of volcanoes 1,000 miles long between Samoa and New Zealand. The expedition left Hawaii on the 18th of March 2005 and returned on the 5th of August having covered a distance of 10,000 miles between Hawaii and New Zealand. Scientists from the USA, New Zealand and Germany used the HURL submersibles, Pisces IV and Pisces V, as well as the remotely-operated vehicle RCV-150, aboard the R/V Ka?imikai-o-Kanaloa to dive into active volcanoes to water depths of 2,000 meters. Scientists from New Zealand, the USA and Germany conducted 41 dives on the previously unexplored (from manned submersibles) submarine volcanoes. Ten active volcanoes were explored during this time. Hundreds of specimens of new and unusual marine life, water chemistry, extremophile microorganisms, mineral formations and volcanic rocks were collected during this expedition with the two submersibles and the ROV. Bizarre natural events, such as the oozing of hot liquid sulfur from the ocean floor onto the skids of the submersible were observed. Carbon dioxide gas was observed profusely streaming into the seawater from the volcanoes, making these one of the greatest contributions to hot house gasses entering the atmosphere.

Jan 1, 2005

By D Bruce

OZ Minerals’ Carrapateena mine is a Sublevel cave mine located in South Australia, approximately 160 km north of the regional centre of Port Augusta. Carrapateena commenced underground development in 2016, with production commencing in 2019 and ramping up to 4.25 Mtpa by December 2020. In pursuit of continual improvement, the Carrapateena mining team sought to identify how it could better interrogate its current productivity levels and its drivers (root causes). During this process the team were confronted by a disconcerting number of different opinions (which often conflicted) on our actual productivity performance which led to a need for review. Although reliable data and a well-designed time utilisation model (TUM) existed, it was still unclear what actions needed to be put in place to improve productivity. This was for the following reasons: • Data sources were disparate unless significant effort was applied to compile them. • Data sources were not available in a format that could be easily viewed or analysed. • General understanding of the data which was collected and how it could be used was poor. • Process of regularly analysing performance and forming tangible actions to improve was lacking rigour. A project was then started with the goal to fix the areas stated above. An initial requirements gathering exercise was undertaken to identify key data sources for site which returned a staggering 42 known systems which were then shortlisted to the key systems that contained operational data and could generate tangible improvements when analysed. This paper discusses the approach to selecting the OZ Minerals Insights Hub as the chosen platform, and the effort and collaboration between cross-functional departments that was involved in transforming the data so it could be displayed in a useful way. The data sources identified included a fleet management system, two planning databases, a control system historian, and a mobile fleet machine data system. New processes were also introduced to digitally capture daily commentary; this was achieved utilising SharePoint Forms. Data pipelines were then developed in the Insights Hub platform to automate the ingestion and transformation of the raw mining data into curated data models for end user consumption. Tableau dashboard packages have been created to support facilitation of short interval control, daily review meetings, and weekly review meetings as well as data analysis of key equipment and locations. These dashboards visualise the mining data models in a form that provides the user a clear picture of what has occurred, why it has occurred, and where to focus improvement efforts. This has resulted in a team which is more accountable, more equipped to assess the root cause of performance, and more regularly able to make data driven decisions. The next step? To build this out from Mine to Mill and create a data indexing tool which will drive self-service reporting, a data agnostic platform and predictive tools that help our people continue to be one step in front.

Mar 29, 2023

By Howard Eavenson

The data given in the paper were collected to furnish an idea of the amount required in various operations of bituminous mining to produce a ton of coal, the plants selected being well-managed ones. With these are included some figures compiled from U. S. Coal Commission's report showing the average amount of labor in various classes of mines. THE information contained in the following paper was collected at the request of the U. S. Coal Commission, and is, published with the permission of that body and of the various companies furnishing the data. For obvious reasons, the names of the companies are not given. No data were gotten from States south of Virginia or west of the Mississippi River. The data about the amount of labor, in man-hours, required in producing a ton of bituminous coal have been collected with the idea of showing what is being done at some well-managed plants, and not to show the average amount required by all mines, which undoubtedly is considerably higher than the average of the figures shown.* Almost all of the figures were supplied by the various companies in reply to a questionnaire; when they were obtained from other sources the company was asked to verify them. On account of the amount of work required, the data were only requested for one or two months for each plant, and the figures represent in each case a month when the car supply was good and the work was practically steady. In the case of most of the West Virginia and Kentucky mines, these conditions were best met in May and June, 1922. The mines for which the figures are given are arranged by states and as to whether the mine operates under union or non-union or open-shop conditions. The union mines listed are generally considered to be as well-managed as are the non-union ones. The production per month in a few cases includes the amount of slate which is loaded and hauled out of the mine, as of course this requires nearly as much labor per ton as does the coal. These cases are noted:

Jan 1, 1924

The Olympic Dam Mining Operation involves the supply of some 2.2 million tonnes per annum of ore feed for the metallurgical plant. To achieve this approximately 9 km of tunnels are developed per year to gain access to the long hole open stopes that supply the majority of the production tonnes.Vast amounts of data are produced in the mining activity from the geology, mine design, scheduling, surveying, shift reporting through to the statistical reporting and analysis of the performance of the mine activities. Care must be taken when obtaining the data to maximise the usefulness of the data to other users so not to impose constraints or additional processes on future users.

Jan 1, 1992

By G. A. Witwicki

"The Thyssen Mining Group of Companies has successfully applied ground freezing technology to numerous projects throughout Europe and North America for over 100 years. Recently, the aspect of data acquisition and data verification has received greater focus.Thyssen Mining has been engaged in several relatively deep shaft sinking projects over the past five (5) years, all of which have required ground freezing to successfully sink through water bearing rock. To safely and economically sink through a quantity of water similar to Saskatchewan’s Potash Mines, it is imperative to understand freeze wall thickness and growth rate.Critical to understanding freeze wall thickness and growth rate, is the collection of accurate data as well as the interpretation of those data. The data can then be utilized to gain a better understanding of the freeze wall growth rate in active projects as well as to provide data to be utilized in modeling for future projects.This paper elaborates on the installation and operation of the data acquisition system utilized at the Newmont Leeville project, Nevada’s first frozen shaft project. In addition, this paper elaborates on the methodology developed by Thyssen Mining to manipulate the freeze wall data derived through the data acquisition system.The successful incorporation of the new system of collection and analysis of freeze wall data for the Leeville Project has given Thyssen Mining a high degree of confidence in the understanding, as well as prediction, of the ground conditions during shaft sinking.This paper will include:•,A brief description of ground freezing and its objective;•,An overview of previous data acquisition methods employed;•,Current data acquisition methods;•,Installation requirements; and,•,Data manipulation and presentation methods."

Jan 1, 2015

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 Thomas V. Falkie, R. Venkataramani

The method of analysis was to test a large model containing potentially significant variables and on the basis of the results obtained, to reduce the model to one containing only the essential significant variables. This stepwise procedure of "scanning" a large proposed model and subsequently condensing the model consisted of two cycles of the same series of computational procedures. Data Preparation A majority of the variables measured were continuous over a specific range of values; for example, the variable "Width Ratio" varied in practice from 0.25 to 1.00. However, "dummy" values were assigned to certain operational variables in order that these varia¬bles would have numerical expression. The following variables were described in this manner. Variable "Dummy" Value x3: Development/Pillaring 0, if pillar extraction was being done 1, if development mining was being done x4: Boxcut/Open Cut 0, if a box cut was being made 1, if an open cut was being made The variables under consideration are represented symbolically as follows: Y: Loading rate (tons per minuts): TONS/MIN x1: Ratio, width of cut/width of cutter: WDRATIO x2: Ratio, height of cut/diameter of cutter: HTRATIO x3: Development or pillaring: D/P

Jan 1, 1972

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 Y Tang, G Si

The analysis of rock fragment sizes plays a pivotal role in various stages of geotechnical and mining engineering. Traditional case-based methods often falter in addressing the variability in rock fragment images due to fluctuating geological conditions and imaging environments. To counter this challenge, AI-based models necessitate training on expansive and diverse data sets, encompassing a wide range of conditions and fragment types. However, the creation of such data sets is often hampered by financial and environmental constraints. This paper introduces a novel approach for rock fragment recognition, which leverages the power of AI for data augmentation, automated annotation and prediction. The methodology integrates three key components: Style-Based Generator Architecture for Generative Adversarial Networks (StyleGAN) for data augmentation, the Segment Anything Model (SAM) for automated annotation, and the multiscale version of you only look once (YOLO-MS) real-time instance segmentation model. The proposed method begins by training a new StyleGAN model using the original data set. It then delves into exploring the latent vectors of different rock fragment images to generate specific ‘style representations’. These latent vectors form the foundational elements of each image in a high-dimensional space, encapsulating critical features such as size, shape, texture, and colour variations. The next step involves conducting quality checks on the generated images to ensure their realism and suitability as training data. Subsequently, the augmented image set is annotated using modified SAM module, followed by training the YOLO-MS real-time instance segmentation model. The performance of this model, trained on the augmented data set, is compared with a model trained on the original data set, particularly in terms of generalisation abilities in new scenarios. The results exhibit a significant enhancement in accuracy, robustness, and generalisability of the model trained on the augmented data set, underscoring the potential of StyleGAN as a powerful tool for data augmentation in geotechnical engineering image analysis.

Sep 1, 2024

By Dan Chun

Abstract-To facilitate the efficient handling of large volumes of information generated by logging exploration drill cores, a computer data bank system (GEOBANK) has been developed to store and retrieve the normally difficult to handle geologic information as well as the physical testing data. The data bank systematized the logging of drill core by first developing a standard list of adjectives or descriptors (with the corresponding assigned numerical codes) for the various stratigraphic categories that are also assigned numerical codes. The system uses the "floating-slot" concept and handles the data bank with one single input form.

Jan 9, 1978

By Alfred Weiss

When I first received the announcement of this meeting I was pleased to see that this 14th APCOM Symposium had expanded its range of interest. One new topic to be considered this year is long range or strategic planning--the continuous process by which current risk taking decisions are made systematically, and with the greatest knowledge of future impact. Long range planning should receive our attention. It is the first step in the management process and the foundation of many--if not all --key managerial decisions. Improvements made at this stage of managing are almost certain to be of benefit in later stages of decision making. It is obvious to many of us that in a physical process such as mineral beneficiation, one does not concentrate all of one's attention on the flotation design without also considering the grinding, cycloning, and desliming stages of the process. Taking this a step further it would be equally erroneous to find a mineral engineer designing a mill without due consideration for mine, smelter and refinery and beyond that the sales goals, growth and profit objectives of the firm itself. And yet, how many times have we seen organizations staff and reorganize without consideration of an overall plan; without adherence to the management process which by its definition, calls for a plan first, followed by the appropriate organization and staff. The computer applications traditionally discussed at APCOM meetings have concentrated on areas dealing with things and people-- the implementation of the plan, rather than with concepts and ideas, the formulation of the plan.

Jan 1, 1977

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 Reinaldo J. Villa, Joshua C. Adams

The I-395/SR-836/I-95 reconstruction project is an $818-million design-build construction effort that includes construction of viaduct, segmental and signature bridges in Miami leading into Miami Beach in Florida. Based on the load testing performed during the design phase, the design-build team determined that conventional driven pile and drilled shaft foundations were not feasible for support of segmental and signature bridges. Due to the weak limestone formation and successful use of auger cast-in-place (ACIP) piles in the Downtown Miami area on commercial developments, ACIP piles were selected as a foundation system to support bridges. This is the first time in history where the bridges will be supported by ACIP pile foundations in an interstate highway in the State of Florida. The design build-team worked closely with the Florida Department of Transportation to develop requirements for the load test program. The paper will provide an overview of the geotechnical data collected, results of dynamic/bi-directional load testing performed on test piles, data collected, installation of piles, and lessons learned. The load test program included a total of fifteen test piles with dynamic, bi-directional, and lateral load testing on 30 and 36-inch diameter ACIP piles and instrumented with thermal integrity profiling wires.

Sep 8, 2021

By Thomas V. Falkie, R. Venkataramani

The empirical nature of this investigation required measurements of variables under operating conditions in underground coal mines. The variables to be measured were based on the postulated character¬istics of the loading function, as described in the previous chapter. One class of variables presumed to influence the loading function are geological variables. Accordingly, the sites for data collection were selected in different bituminous coal seams at dispersed locations in order that variation in geological conditions would exist. Seven mines in central and western Pennsylvania and one mine in northern West Virginia were selected as data collection sites. Five different coal seams were represented by these mines. The remaining classes of variables - geometric, mechanical, and procedural - were measured as they occurred in the mines. A description of the measurements, and the conversion of these measurements to forms which could be-analyzed for effect on the loading function is given. Mining Dimensions At the underground working face, the following dimensions were measured: a. Thickness of coal excavated. b. Thickness of shale excavated. c. Total height excavated. d. Width excavated. e. Depth of penetration of each machine cut. With the exception of the last of these dimensions, all were measured

Jan 1, 1972

By S Versace, D Arnott, D Bojcevs, A Shaw

The George Fisher deposit is a significant sediment hosted silver-leadzinc resource located 2 km north of the Hilton Mine in NW Queensland. The George Fisher feasibility project involved the completion of underground and surface drilling programs to delineate Measured and Indicated Resource which could be converted to an Ore Reserve through mining and commercial studies. The geological information which was collected and used for the feasibility study included a major program of diamond drilling, predominantly from underground sites, but with some drilling also completed from surface. All drillcore was geologically and geotechnically logged, sampled and assayed. Data was also collected from underground cross-cut and drive development which was mapped for rocktype, ore distribution, structure and geotechnical condition. This new geological data, generated during the Pre-Feasibility and Feasibility stages, was combined with results of all previous drilling campaigns in the George Fisher area to produce a comprehensive database on which geological modelling and Resource estimation could be based. To allow fast and accurate collection and utilisation of data from the drilling program, digital collection processes were used where possible. This resulted in rapid data entry into the project database, significantly enhanced data quality, 100 per cent data ownership within the project team and increased productivity levels. Processes used during the project data collection program are described, lessons learned are related and suggestions for ænext timeÆ are listed using the benefit of hindsight.

Jan 1, 1998

This paper presents a new idea in field data collection for geological and scientific data. The computer keyboard has been replaced by a barcode wand driving a spreadsheet style editor. To use the full capabilities of computer hardware currently available, geotechnical and geological data can be entered and validated in the field, in a structured format, for later transfer into modem geological modelling databases. An example, using a geotechnical borehole log, explains the principles discussed. The generic nature of the Datcol system is illustrated by the application developed using the Norwegian Geotechnical Institute Rock Mass Classification system. A brief description of a modem spatial relational database is also given with particular reference to underground engineering.

Jan 1, 1993

By Jeonghwan Kim

Intelligent Excavation System (IES) has been considered to be one of the desirable solutions to improve the productivity, quality and safety of the current earthwork processes. IES is composed of various hardware and software modules including task planner (work command generator), local and global work environment sensors, GPS and other sensors to acquire the excavator?s status, robotic controller and electronic valves, and the manipulator (excavator). A data communication manager was required to coordinate the signal or data-flow in between these modules considering the operational status of IES such as; excavation start/completion, truck detection, terrain model update, emergency stop, local sensing, GPS/motion sensor data handling, and so on. This paper presents the role and functions of this data communication manager. After every possible operational status of IES operation was identified, a protocol of each communication process in the form of string-type was made. The components of IES are connected with either wired or wireless network. Therefore, they can communicate with the manager using socket (TCP/IP) communication. The data communication manager is also capable of accessing a web-based project management information system, which constantly updates the performance of excavation robot to online database.

Aug 1, 2013

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 D W. Evans

Research into the dynamic properties of ground support elements continues to grow at a global level, given the increasing importance of this field of application within underground hard rock mining. Measuring the mechanical response of such elements under the rapid onset of simulated rock burst loads forms the basis for this experimental work, typically utilising gravitational drop testing methods. This field of research is fundamentally driven by an increasing need for dynamic performance data, given the constant evolution of rock bolting product designs, materials selection, combination testing and specific application needs based on ground types and loading scenarios. A smaller number of globally recognised dynamic test facilities have predominantly lead research within this arena, typically in association with academic institutes, larger industry groups and government research institutes. However, due to the increasing requirement for dynamic performance data, additional facilities have now emerged in order to satisfy the overall demand for test work. These newer facilities are more typically associated with suppliers to the underground hard rock industry, with experimental designs looking to replicate, at least in part, the work of the primary facilities. For standardisation, repeatability and calibration purposes, the cross validation of test data between primary and secondary facilities becomes an obvious and important consideration across this field of research. This paper outlines the development of a new dynamic drop test facility for testing rock bolts, including the structural design and associated high speed data capture system. Based on the utilisation of identical bolt types, material grades, anchoring systems and decoupled ‘free-lengths’, comparative data has been generated to provide cross validation of the new test facility against data generated by a globally recognised primary facility. The cross validation of data provides increasing confidence in the repeatability and accuracy of the new facility as activity continues towards the provision of further experimental test work.

Nov 29, 2022