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By Andrea Donadon

This article introduces the Danieli Total Asset Management System, which involves 360 degrees service methods and equipment. Danieli Condition Monitoring System and Danieli Torque Monitoring System are fundamental to evaluate the conditions of the working components in heavy duty transmissions. The first one monitors the vibrations and the lubrication fluid conditions with the purpose to identify the presence of malfunctions, by detecting any variation of the signals from the standard values. The second one monitors the transmitted torque for each spindle and gearbox, with the aim of detecting any anomaly in torque distribution and magnitude, and evaluating the residual life of the mechanical parts, on the base of a dedicated fatigue damage algorithm. All these features are useful in avoiding serious damages to machines, facilities and people, but allow also planning the maintenance and overhaul activities with efficiency, in order to achieve the best plant availability and reliability. The data analysis is made by the IntelliCMS software, which interfaces with plant and Danieli operators and includes the possibility to cooperate by remote connections, simplifying service activities and support. All these features allow providing new full service programs, where the client is totally supported in maintenance, overhauls and substitutions. This is the best way to ensure the most efficient, high quality and reliable after sales assistance.

Aug 16, 2017

By Alessandro Pigani

Over the last 25 years the thin slab casting and rolling process has been gaining a major market share in the production of hot rolled strip, progressively eroding the areas previously of exclusive domain of conventional mills. This is mainly thanks to the competitiveness of the process over conventional ones, and to the growing ability of this technology to cover the majority of market niches, well beyond the boundaries of the commodity market. After having significantly contributed to the continuous expansion of this production mode and after an extensive campaign of theoretical research and physical testing in reference plants, Danieli has progressively developed over the years a new generation of thin slab casting and rolling layout, marking a new phase in the evolution of this process. The new step-ahead is epitomized by the name QSP-DUE. This configuration belongs to the Quality Strip Production (QSP) family, which is Danieli’s strip-quality-oriented thin slab based plants. The innovative features of the process are embodied in the acronym DUE, which stands for Danieli Universal Endless (Danieli patented technology – US patent n. 8087449, January 3rd, 2012). What makes this new concept different is the ability of “universal” rolling modes thanks to the fact that, now, within one single plant it is possible to perform coil-to-coil, semi-endless and endless rolling modes, making this plant a major technology innovation compared to the previous generation.

Aug 16, 2017

By Alim Ullah

Danieli entered into the Direct Reduction (DR) field in order to offer its clients complete turnkey facilities for steelmaking?from processing of raw materials through finished products. The Danarex® Process produces a passivated product, called the Supermetallic (SPM), without hot-briquetting. One of the main features of the Danarex® Process is its ability to produce approximately 50% of the Direct Reduced Iron (DRI) through reduction gas generated in a reformer and another 50% through recycling preheated furnace exit gas. Some other distinguishing features are: separation of carburising and cooling zones and the use of double bustle for better distribution of gas inside the shaft furnace. A pilot plant was set up at Hazen Research, Inc., Golden, CO in order to show that the integrated process works as expected and to obtain process data. The SPM obtained was of a very high quality, evidenced by metallization levels of 92% to 98%. The carbon content could be controlled between 1.5% to 4.0% by varying the operating conditions and approximately 75% of this carbon was present as Fe3C. The product was tested for inertness by Chilworth Technology, England, using UN standard tests and was found to be safe for outdoor storage and transportation.

May 1, 2003

By Nicola Santangelo

The current economic crisis and growing environmental issues have led the steel industry to face the simultaneous effects of low demand and production overcapacity in a globalized steel market, whilst confronting high energy prices and the need to comply with the green economy. Heat Recovery from waste gas of the Electric Arc Furnace (EAF) represents one of the greatest opportunities to reduce the consumption of primary energy and reduce the CO2 emissions in steel industry. The heat of the off-gas, if recovered and converted into electrical power, can make the whole Fumes Treatment Plant autonomous from the electric point of view, thus making it a “Zero-Energy FTP”. The concept is to substitute the Cooling Components (Water Cooled Panels and Air Cooler) are substituted with Heat Recovery Components (Hot Water Cooled Panels and Heat Exchanger). The recovered heat can be converted into power by using an Organic Rankine Cycle (ORC) turbine. The heat recovered by the HWCP and the Heat Exchanger is characterized by a variable thermal power, which affects the efficiency of the downstream ORC System. Danieli, in collaboration with the University of Udine, has developed the Thermal Stabilizer Unit (TSU). Danieli has realized the first step towards Zero Energy FTP at ABS steelmaking plant (Italy) by installing a CHR® - Clean Heat Recovery. This plant has been started up on 17th October 2014. The HMI of the CHR® is integrated in the HMI of the furnace, so all the process could be kept under control by the furnace operator. At ABS recovered thermal power reaches 5.5 MW. The max power production reached with CHR plant in ABS (over 1MW of electric power) consents an annual saving of approx. 500,000 EUR.

Aug 16, 2017

By Marco Quirion

The Daniel-Johnson dam, formerly named Manicouagan-5 (Manic-5), is located 800 km northeast of Montreal (Quebec) in Canada. This is the largest multiple-arch concrete dam in the world consisting in 13 arches and 14 buttresses. Since its construction in 1969, various type of cracks have appeared on both downstream and upstream faces of almost all the arches of this concrete dam. As a part of the Daniel- Johnson’s dam safety assessment program, Hydro-Québec is currently performing structural analysis of the dam with advanced numerical models to better understand and predict the dam behaviour. Because the buttresses transmit all the arch loads to the rock mass, determination of the properties of the dam foundation should not be neglected in dam global safety analysis and also to insure that reliable input parameters are used in the dam numerical simulations. This paper summarizes the past and the more recent investigations of this foundation used to assess its safety and stability. Moreover, the use of rock mass classification to estimate the deformation modulus, based on empirical equations, gives values in the range of 30 to 40 GPa that are higher than expected values. Using those values in the numerical simulations for dam structural analysis gives an excellent correlation with displacements monitoring of the buttresses.

Jan 1, 2015

By Roy Moreby

Dartbrook Coal is currently developing a modern, high production (3.5 Mtpy) single longwall retreat coal mine in the Upper Hunter Valley of NSW, Australia. The location is a green field site, with no surface or underground mining operations ever having taken place in the initial target seams. The coal seams of the working section, floor and immediate roof average a total 20 m in thickness and contain a mixed carbon dioxide 1 methane seam gas ranging from 6.5 to 11.0 m3/t. The target seams have undergone detailed characterisation to assess hazards and quantify the controls required to minimise associated risks during mining. Risks identified during initial phases of exploration and 14 months of development include: • High gas emission during development. • Probable high longwall gas emission rate. • Medium to high propensity to spontaneous combustion • Possible carbon dioxide outbursts on geological structures • The main controls currently in place include, • Mine geometry and ventilation circuits • Exploratory drilling ahead of development • Intensive in seam pre drainage • Goaf drainage plants • Comprehensive monitoring systems • Underground environmental management plan. • Pre mining predictions of gas emission provided an indication of gas emission rates which have proven to be an under estimate. Current mine issues are; • Both surface fans are at full capacity (250 m3/s 1.4 kPa) • Both gas drainage pumps are at full capacity (2.3 m3/s) • Alternatives to extensive in seam pre drainage being investigated (surface to in seam hydro fracture) Review of mine design underway This paper describes the seam characterisation, assessment of mining hazards and summary of the management plan developed to address the risks identified. A review of current mine underground environmental performance is also provided

Jan 1, 1997

Any project carries a number of challenges and risks. Inappropriate design, poor project management, time, industrial disputation and capital over runs are all areas which can impact on a project cash flows and return on investment. In the resource Industry you have the added complexity and risk of geology which is difficult to predict and sometimes unforgiving. The Dartbrook Mine was designed and constructed as a high output longwall mine and has overcome a number of hurdles to produce over 2.3 million tonnes for the first year of operation. A number of problems were encountered during construction which resulted in the inseam development being delayed for six months. Dartbrook people have demonstrated they can manage adversity and produce world class results. The mine has been through a very steep learning curve during 1997 and with this experience behind the mine is now producing at an annualised rate of 3.4 MTPA.

Jan 1, 1998

Dartbrook mine is a new greenfields thermal coal longwall mine located n the Upper Hunter Valley ten kilometres north of Muswellbrook Figure 1). Construction began in April 1993 with coal extraction starting n October 1994. Longwall production is scheduled to commence in the first quarter of 1996 with production expected to exceed 3.5 million onnes per annum. The permanent workforce will number 150 full-time ,mployees and productivity rates will be 22 000 per employee per annum m a ROM basis. Being a greenfield site Dartbrook has had the opportunity to develop and operate the mine using the best practices and latest technology available to meet high standards of safety, quality and production.

Jan 1, 1995

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