Mining Automation Research Articles

Research of Mine Drainage Automatic Control System Based on PLC

According to the development of mine automation requirements, Siemens S7-300 PLC is adopted in order to do the research of mine drainage automatic control system in this paper. Combined with frequency control technology, the system can schedule the pumps reasonably by monitoring water level and pressure signals. With failure alarm, group network communication and other functions, it’s profitable to achieve the whole mine automatic control and the economic operation of the entire system.

The contributions of human factors and ergonomics to a sustainable minerals industry

This article describes examples of the application of human factors research and development work to a sustainable minerals industry. It begins by outlining human-related aspects of the minerals industry and the key human factors work previously undertaken in this domain. The focus then switches to sustainability in the minerals industry. Sustainability principles are introduced and illustrations provided of how human factors research and development work fits within such a framework. Three case studies of human factors in the minerals industry research are presented and the sustainability implications in each case study are highlighted. Finally, future trends related to human factors work in a sustainable minerals industry are addressed, in particular the opportunities and possible adverse consequences that increasing deployment of mining automation might bring. Practitioner Summary: Minerals industries are a major global activity with significant sustainability implications. Aspects of sustainability in mining are examined using three case studies. These illustrate the contribution of human factors/ergonomics in reducing risks; developing emergency response management systems; and the value of participatory ergonomics in improving the design of mining equipment.

Data fusion with Gaussian processes

This paper addresses the problem of fusing multiple sets of heterogeneous sensor data using Gaussian processes (GPs). Experiments on large scale terrain modeling in mining automation are presented. Three techniques in increasing order of model complexity are discussed. The first is based on adding data to an existing GP model. The second approach treats data from different sources as different noisy samples of a common underlying terrain and fusion is performed using heteroscedastic GPs. The final approach, based on dependent GPs, models each data set by a separate GP and learns spatial correlations between data sets through auto and cross covariances. The paper presents a unifying view of approaches to data fusion using GPs, a statistical evaluation that compares these approaches and multiple previously untested variants of them and an insight into the effect of model complexity on data fusion. Experiments suggest that in situations where data being fused is not rich enough to require a complex GP data fusion model or when computational resources are limited, the use of simpler GP data fusion techniques, which are constrained versions of the more generic models, reduces optimization complexity and consequently can enable superior learning of hyperparameters, resulting in a performance gain.

Research on the Memory Cutting Model of Shearer

Automatic" and "less people" in fully mechanized working face is the target of China's coal industry development, and the key to achieve this goal is the automation of coal mining. According to the previous technology of memory cutting cannot adapt to the complicated geological condition in China, this paper proposed the technology of shearer self-adaptive memory cutting based on fuzzy control theory, the shearer positioning system and fuzzy control system of self-adaptive cutting. These systems can get the message of shearer's position and attitude at any point, trace the memorial cutting path automatically, judge whether the shearer cuts rocks based on fuzzy control theory and find the optimal scheme when it works. The authors make the experiments of path tracing in laboratory and the experiment of shelf-adaptive adjust in Xi'an Coal Mining Machinery. All the experimental results show that this technology not only can realize the shearer memory cutting, but also can discriminate the abnormal state of shearer cutting rocks, then adjust the drawing speed and drum height automatically.

Research on Adaptive Advance System of Hydraulic Support Based on Neural Network PID

Aiming at the problem that contact advance lags seriously and automatic advance with shearer is not in time in ASQ of hydraulic support on fully mechanized coal mining face, this paper proposed a solution of adaptive advance system based on Neural Network PID. This advance method can effectively reduce invalid waiting time in advance and the probability that friction between top beam and roof increases, and thus to improve advancing velocity and achieve continuity of coal mining automation. This paper finally used SIMULINK to carry out simulation. Results show that Neural Network PID controller has better performance than conventional PID controller.

Research on Adaptive Advance System of Hydraulic Support Based on Neural Network PID

Aiming at the problem that contact advance lags seriously and automatic advance with shearer is not in time in ASQ of hydraulic support on fully mechanized coal mining face, this paper proposed a solution of adaptive advance system based on Neural Network PID. This advance method can effectively reduce invalid waiting time in advance and the probability that friction between top beam and roof increases, and thus to improve advancing velocity and achieve continuity of coal mining automation. This paper finally used SIMULINK to carry out simulation. Results show that Neural Network PID controller has better performance than conventional PID controller.

Global navigation satellite system (GNSS) technology for automation of surface mining

Satellite technologies have undergone a rapid development during the last two decades – even in surface mining. The possibilities of using this technology for bucket wheel centre spatial determination are investigated at the Libouš Mine in the North Bohemian brown coal basin (Czech Republic). The position of the bucket wheel centre is computed by means of global navigation satellite system (GNSS) data, inclinometer and incremental measurements. Data is transferred to a base and saved in a database. The main goal is to improve the control system for opencast mining. Visualisation of the bucket wheels' spatial positions and their movements in real time is performed by KVAS software – mine modelling and planning software ‘GNSS MineModel’. The article describes applications, which are used for automation of mine surveying and control of mining.

Human Factor Issues with Automated Mining Equipment

This paper presents an extended literature review of the human factors impacts of current and future automation in the minerals industry. It begins by defining what is automation and stating why it is being developed and deployed. It then outlines why it is important to consider operators and maintainers when designing and deploying mining automation. To put mining automation into context, lessons learnt from automation in other industries and past problems with automation from a human-element perspective is then presented before specific issues in mining automation are discussed; these include ‘degrees of automation’, automation trends and the deployment issues. Conclusions about the human-element impacts of mining automation are made; these include the likely problems and some potential ways of reducing such problems.

Assessing the impact of driverless haul trucks in Australian surface mining

Australia has been a leading mining nation and its mines continue to attract substantial investment due to its strong mineral endowment, mining tradition and high skills base. In recent years Australian mining companies have been facing higher labour costs, which need to be offset by other savings. Automating mine-site machinery offers one potential source of such savings. Underground mines pioneered automated technology largely for safety and mine accessibility reasons and to reduce operating costs. Yet these operations now represent a much smaller portion of the total ore tonnes mined due to the changing mining techniques meaning larger lower grade deposits are becoming economic. It appears the future of mine automation will be directed by surface mining as this maturing technology finds further applications above ground and in the next decade large scale open pit automation trials gain more momentum. This paper assesses the implications of introducing driverless haul trucks in a typical large remote Australian open-pit mine. Such automation will save employee and associated costs, increase operational productive hours and ultimately will reduce mine site workforce numbers. While there will be fewer jobs per mine, with reduced costs and higher productivity some previously uneconomic mines may again be profitable. The social implications of greater mine site automation are the reduction in population of remote mining towns and a decrease in the lower skilled labour requirements for the mining sector. There will be an increase in fly-in fly-out mining operations and companies will establish remote control centres for automated mines in larger cities. This may decrease overall labour requirements and so reduce employment in the sector; therefore, the government should be mindful of implementing policies that ensure a fair return on the economic rent of mineral leases.

A Mine on Its Own

This article presents a study of Gaussian process (GP) models applied to the problems of modeling and data fusion in the context of large-scale terrain modeling. The proposed model naturally provides a multiresolution representation of space, incorporates and handles uncertainties aptly, and copes with incompleteness of sensory information. These attributes are considered essential to support most field robotics applications, including autonomous mining. GP regression techniques are applied to estimate and interpolate (to fill gaps in occluded areas) elevation information across the field. GP approximation methods are introduced to enable the application of the proposed techniques to large data sets. To obtain a comprehensive model of complex terrain, typically, multiple sensory modalities and multiple data sets are required. The GP modeling approach is consequently extended to fuse multiple, multimodal data sets to obtain a best estimate of the elevation given the individual data sets. Two different GP-based concepts are applied to perform data fusion-heteroscedastic GPs and dependent GPs (DGPs). Thus, this article presents a report on an ongoing study of the use of GPs and several GPbased concepts to the problem of large-scale terrain modeling in the context of mining automation.

Robust Linear Feedback Control of Attitude for Directional Drilling Tools (13th IFAC Symposium on Automation in Mining, Mineral and Metal Processing)

This paper describes the design of an inclination- and azimuth-hold controller for directional drilling tools typically used in the oil industry. A control input transformation that partially linearizes and decouples the plant dynamics is proposed. A pole-placement method is used to design the controller and an analysis of the stability robustness is performed using the small gain theorem. Results for a transient simulation of the proposed controller are also presented.

Longwall mining automation an application of minimum-variance smoothing [Applications of Control

This article reviews the development of the minimum-variance smoother and describes its use in longwall automation. We describe both continuous- and discrete-time smoother solutions. It is shown, under suitable assumptions, that the two-norm of the smoother estimation error is less than that for the Kalman filter. A simulation study is presented to compare the performance of the minimum-variance smoother with the methods of H.E. Rauch et al. (1965), and D.C. Fraser and J.E. Potter (1969).

Present situation and developing trend of coal mine automation and communication technology

Introduced developing process of coal mine automation and communication technology, analyzed present features and characteristics of coal mine automation and communication technology, and put forward a few key technical problems needed to be solved.

AN INNOVATIVE APPROACH USING TELEROBOTICS FOR INTELLIGENT BLOCK CAVING OPERATIONS

Telerobotics focused on mass mining is currently being introduced into production systems around the world. Mining companies in Canada, Sweden, South Africa and Australia have tended to lead the international charge to this form of technology for mining. While this introduction is taking place a few basic questions have yet to be answered. How many machines can a single operator run? How many types of machines can a single operator run? How would you control the dispatching of telerobotic machines? A new Canadian Research Chair in Robotics and Mine Automation has been established at Laurentian University. This chair will investigate these questions and many more through a series of experiments in a newly established telerobotics laboratory that connects Laurentian teleoperation workstations to model mining machines located several kilometres away. The laboratory will support many experiments allowing researchers to physically run multiple robot scenarios differing the numbers and types of machines at scale. Further experiments are being designed to investigate the potential for managing time delays in telerobotics. This paper describes the laboratory, experiments and some preliminary results.

Optimizing Wireless LAN for Longwall Coal Mine Automation

A significant development in underground longwall coal mining automation has been achieved with the successful implementation of wireless LAN (WLAN) technology for communication on a longwall shearer. WIreless-FIdelity (Wi-Fi) was selected to meet the bandwidth requirements of the underground data network, and several configurations were installed on operating longwalls to evaluate their performance. Although these efforts demonstrated the feasibility of using WLAN technology in longwall operation, it was clear that new research and development was required in order to establish optimal full-face coverage. By undertaking an accurate characterization of the target environment, it has been possible to achieve great improvements in WLAN performance over a nominal Wi-Fi installation. This paper discusses the impact of Fresnel zone obstructions and multipath effects on radio frequency propagation and reports an optimal antenna and system configuration. Many of the lessons learned in the longwall case are immediately applicable to other underground mining operations, particularly wherever there is a high degree of obstruction from mining equipment

Bringing Chemical Data onto the Semantic Web

Present chemical data storage methodologies place many restrictions on the use of the stored data. The absence of sufficient high-quality metadata prevents intelligent computer access to the data without human intervention. This creates barriers to the automation of data mining in activities such as quantitative structure-activity relationship modelling. The application of Semantic Web technologies to chemical data is shown to reduce these limitations. The use of unique identifiers and relationships (represented as uniform resource identifiers, URIs, and resource description framework, RDF) held in a triplestore provides for greater detail and flexibility in the sharing and storage of molecular structures and properties.

GenRel—a computer model for prediction of mining equipment failures based on genetic algorithms (GAs)

This paper discusses ongoing research to formulate, develop and test a reliability assessment model (GenRel) based on genetic algorithms (GAs). GAs are powerful and broadly applicable stochastic search techniques based on the principles of natural selection, heredity and genetics. The reason for selecting GAs is the fact that the reliability of mining equipment changes over time due to its dependence upon several covariates/factors (e.g. equipment age, operating environment, number and quality of repairs). These factors combine to create a complex impact on a piece of equipment's reliability function. This impact encapsulates and inherits to some degree the individual characteristics of the factors as they evolve over time. 1 BestFit, Palisade Decision Tools (http://www.palisade.com/html/bestfit.asp) Theoretical probability distributions are commonly used to fit equipment failure data. GenRel uses the exponential probability distribution as its engine to generate predictive patterns based upon historical failure data. Overall, this paper suggests a methodology for applying GAs for reliability assessment of mining equipment. An example is given to demonstrate the effectiveness of using GAs in reliability studies. The research discussed in this paper was carried out by the Laurentian University Mining Automation Laboratory (LUMAL).

An Efficient Approach to Identifying and Validating Clusters in Multivariate Datasets with Applications in Gene Expression Analysis

Gene expression data analysis has become an important topic in bioinformatics due to its wide application in the biomedical industry. Effective analysis of gene expression data is an essential pail of various data mining methods, especially the clustering techniques. Various kinds of clustering methods have been proposed, yet they do not satisfy for the requirements of high efficiency, high quality and automation in the mining of gene expression data. In this paper, we propose an efficient and automatic clustering approach that is suitable for gene expression analysis. The proposed approach primarily employs similarity-matrix based clustering techniques, complemented by new heuristics for reducing the computation cost. In particular, a novel validation technique is incorporated for evaluating the quality of the discovered gene expression patterns. Because it includes empirical evaluation of different gene expression datum, the proposed approach is able perform better than other methods in terms of efficiency, clustering quality and automation.

Fast and Robust Stereo Matching Algorithms for Mining Automation

The mining environment, being complex, irregular, and time-varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper evaluates a number of matching techniques for possible use in a stereo vision sensor for mining automation applications. Area-based techniques have been investigated because they have the potential to yield dense maps, are amenable to fast hardware implementation, and are suited to textured scenes. In addition, two nonparametric transforms, namely, rank and census, have been investigated. Matching algorithms using these transforms were found to have a number of clear advantages, including reliability in the presence of radiometric distortion, low computational complexity, and amenability to hardware implementation.

MAINTENANCE AND COST EVALUATION OF AN AUTOMATED HAULAGE TRUCK AT INCO LIMITED

Mineral Resources EngineeringVol. 08, No. 02, pp. 181-194 (1999) No AccessMAINTENANCE AND COST EVALUATION OF AN AUTOMATED HAULAGE TRUCK AT INCO LIMITEDMARK ASHCROFT, NICK VAGENAS and GREG BAIDENMARK ASHCROFTInco Limited, Copper Cliff, Ontario, P0M 1N0, Canada Search for more papers by this author , NICK VAGENASLaurentian University Mining Automation Laboratory, Sudbury, Ontario, P3E 2C6, Canada Search for more papers by this author and GREG BAIDENInco Limited Copper Cliff, Ontario, P0M 1N0, Canada Search for more papers by this author https://doi.org/10.1142/S0950609899000207Cited by:0 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractIn 1983, Inco Limited began a program to design a rapid ore transportation system. This program was designed to serve two purposes: to provide a new effective material transport system for ramp haulage and to perform research and development to develop enabling technologies for future mine automation. As a result of the program, Inco designed and constructed a prototype automatic haulage truck. The premise of the truck operation was to serve as a haulage system on mining horizons where conveyor belt systems were not feasible. The intention was for the truck to haul material from a loading chute to a dumping area, using a self-guiding mechanism. The truck was commissioned to operate at Inco's Little Stobie Mine in May 1991.This paper discusses the maintenance characteristics of the automated haulage truck at Little Stobie mine of Inco Limited in Ontario, Canada. The paper also discusses the truck's performance compared to conventional truck haulage systems of similar capacity. The maintenance study of the 70-ton trolley-driven automatic truck presented in this paper covers the period of May 1991 to December 1993. The study discusses failure characteristics, frequency of occurrence of faults and calculation of the truck's availability. Furthermore, indexed cost information related to the truck's productivity is also presented.The outcome of this paper will indicate that automation of truck haulage systems is a reliable and cost-effective alternative for underground mines. FiguresReferencesRelatedDetails Recommended Vol. 08, No. 02 Metrics History PDF download