Min Metall Explor

Given the recent focus on powered haulage incidents within the US mining sector, an appraisal of collision avoidance/warning systems (CXSs) through the lens of the available research literature is timely. This paper describes a rapid review that identifies, characterizes, and classifies the research literature to evaluate the maturity of CXS technology through the application of a Technology Readiness Assessment. Systematic search methods were applied to three electronic databases, and relevant articles were identified through the application of inclusion and exclusion criteria. Sixty-four articles from 2000 to 2020 met these criteria and were categorized into seven CXS technology categories. Review and assessment of the articles indicates that much of the literature-based evidence for CXS technology lies within lower levels of maturity (i.e., components and prototypes tested under laboratory conditions and in relevant environments). However, less evidence exists for CXS technology at higher levels of maturity (i.e., complete systems evaluated within operational environments) despite the existence of commercial products in the marketplace. This lack of evidence at higher maturity levels within the scientific literature highlights the need for systematic peer-reviewed research to evaluate the performance of CXS technologies and demonstrate the efficacy of prototypes or commercial products, which could be fostered by more collaboration between academia, research institutions, manufacturers, and mining companies. Additionally, results of the review reveal that most of the literature relevant to CXS technologies is focused on vehicle-to-vehicle interactions. However, this contrasts with haul truck fatal accident statistics that indicate that most haul truck fatal accidents are due to vehicle-to-environment interactions (e.g., traveling through a berm). Lastly, the relatively small amount of literature and segmented nature of the included studies suggests that there is a need for incremental progress or more stepwise research that would facilitate the improvement of CXS technologies over time. This progression over time could be achieved through continued long-term interest and support for CXS technology research.CC999999/ImCDC/Intramural CDC HHSUnited States

Risk Assessment as a Tool for Mobile Plant Operators for Sustainable Development: Lessons from the Western Australian Mining Industry

Mobile plant is used extensively not only in the Western Australian (WA) Mining Industry but internationally as well. The use of mobile plant has inherently high risk and every year is associated with a significant number of workplace fatalities and injuries. Prior to this research being conducted there was no specific data published related to mobile plants incidents and fatalities for the Western Australian mining industries. The aim of this research was to improve the safety performance of mobile plant operators in the Western Australia (WA) mining industry by identifying the causes of mobile plant incidents reported to Resources Safety between 1/1/2007 and 31/3/2020

MULTIPLE DISCRETE-EVENT SIMULATION AND ANIMATION MODELS TO ASSIST MODERN MINING OPERATIONS

This research investigation was conducted to develop, execute, and analyze a collection of discrete-event system simulation and animation models for different modern mining operations and systems, including two open-pit gold mines, an aggregate mine (sand and gravel), an open-cast (strip) coal mine, and an underground mine evacuation operation. The mine simulation and animation models aimed to study and assess a wide range of practical unique and common "what if?" scenarios that the mine engineers and managers of the case studies posed in different aspects during the research. A comprehensive and detailed literature review was also performed to provide a summary of the published discrete-event system simulation projects and their applications in the mining and mineral industry. The simulation results of the investigation were effectively implemented to assist the engineers in maximizing the productivity of the mines, improving the operation processes, reducing the environmental impact of the haulage operations, and enhancing the equipment utilization in various case studies. In addition, due to the shortage of powerful and flexible computer simulation tools in designing and analyzing underground mining evacuation operations and rescue equipment with respect to the mine operating characteristics and layout, the discrete-event system simulation and animation technique was innovatively implemented for modeling these complex systems. GPSS/H® and PROOF Professional® were the simulation language and animation software used for this research work

DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES

In recent years, autonomous solutions in the multi-disciplinary field of the mining engineering have been an extremely popular applied research topic. The growing demand for mineral supplies combined with the steady decline in the available surface reserves has driven the mining industry to mine deeper underground deposits. These deposits are difficult to access, and the environment may be hazardous to mine personnel (e.g., increased heat, difficult ventilation conditions, etc.). Moreover, current mining methods expose the miners to numerous occupational hazards such as working in the proximity of heavy mining equipment, possible roof falls, as well as noise and dust. As a result, the mining industry, in its efforts to modernize and advance its methods and techniques, is one of the many industries that has turned to autonomous systems. Vehicle automation in such complex working environments can play a critical role in improving worker safety and mine productivity. One of the most time-consuming tasks of the mining cycle is the transportation of the extracted ore from the face to the main haulage facility or to surface processing facilities. Although conveyor belts have long been the autonomous transportation means of choice, there are still many cases where a discrete transportation system is needed to transport materials from the face to the main haulage system. The current dissertation presents the development of a navigation system for an autonomous shuttle car (ASC) in underground room and pillar coal mines. By introducing autonomous shuttle cars, the operator can be relocated from the dusty, noisy, and potentially dangerous environment of the underground mine to the safer location of a control room. This dissertation focuses on the development and testing of an autonomous navigation system for an underground room and pillar coal mine. A simplified relative localization system which determines the location of the vehicle relatively to salient features derived from on-board 2D LiDAR scans was developed for a semi-autonomous laboratory-scale shuttle car prototype. This simplified relative localization system is heavily dependent on and at the same time leverages the room and pillar geometry. Instead of keeping track of a global position of the vehicle relatively to a fixed coordinates frame, the proposed custom localization technique requires information regarding only the immediate surroundings. The followed approach enables the prototype to navigate around the pillars in real-time using a deterministic Finite-State Machine which models the behavior of the vehicle in the room and pillar mine with only a few states. Also, a user centered GUI has been developed that allows for a human user to control and monitor the autonomous vehicle by implementing the proposed navigation system. Experimental tests have been conducted in a mock mine in order to evaluate the performance of the developed system. A number of different scenarios simulating common missions that a shuttle car needs to undertake in a room and pillar mine. The results show a minimum success ratio of 70%

AUGMENTED REALITY AND MOBILE SYSTEMS FOR HEAVY EQUIPMENT OPERATORS IN SURFACE MINING

U.S. federal laws mandate that mining companies ensure a safe workplace, implement approved training programs, and promptly report work-related injuries. The mining industry\u27s commitment to innovation reflects a history of adopting advancements to enhance environmental sustainability, workplace safety, and overall productivity, while simultaneously reducing operational costs. This thesis proposes the integration of Augmented Reality (AR) technology and digital applications to enhance the surface mining industry, presenting two innovative solutions: an AR Training System and an Operational Digital System. These business solutions have been developed and applied at a surface mine in the southwest of the US, having the potential to improve the mining industry by enhancing safety, training, operational efficiency, and data-driven decision-making, which comprehends a significant step toward a more sustainable, effective, and technologically driven mining sector, contributing to the industry\u27s evolution and growth. The AR Training System leverages Microsoft´s Power Platform and HoloLens 2 capacities to provide operators with immersive and step-by-step training guides in real working conditions for Dozers, Motor Graders, and End Dump trucks. These AR guides combine 3D models, videos, photos, and interactive elements overlapping mining equipment to enhance learning and safety. The system also offers an efficient approach to data collection during operator training, which has the potential to modify the training guides based on user performance. On the other hand, the Operational Digital System addresses the industry\u27s operational challenges. It streamlines the pre-operation inspection process, tracks equipment status, and accelerates defect identification, shift timing, delays, and loaded tonnage. The system offers a holistic approach to mining operation optimization, facilitating data sharing and management among different departments, enhancing collaboration, and expediting maintenance processes

DEVELOPMENT OF A METHODOLOGY FOR THE EVALUTATION OF UAV-BASED PHOTOGRAMMETRY: IMPLEMENTATION AT AN UNDERGROUND MINE

Autonomous systems in underground mining are increasingly being implemented as tools to collect data in inaccessible areas and improve the safety of mine personnel. There are many areas in the underground mining environment that cannot be accessed by personnel due to the high potential for ground fall and insufficient ground support. By combining unmanned aerial vehicles (UAVs) with technologies such as photogrammetry and LiDAR (Light Detection and Ranging) scanners, 3D point clouds can be created for inaccessible sites. A 3D digital point cloud can provide valuable geotechnical information such as the ability to measure discontinuities, inspect rock conditions, generate accurate volume estimates, and obtain a georeferenced geometry of the inaccessible opening. There are many challenges to operating UAVs and collecting high-quality imagery in underground environments including poor lighting and visibility, dust, water, confined spaces, air turbulence, and a lack of GPS coverage for navigation and stability. Due to the difficult flying conditions and GPS-denied environment, several companies are developing UAVs with LiDARbased simultaneous localization and mapping (SLAM) to enhance the obstacle detection and avoidance capabilities of the platforms and minimize the potential for a collision. The objective of this research was to develop a methodology that can be used to evaluate UAV-based imaging tools designed to fly in underground environments. A series of demonstrations was designed to test the functionalities of available UAVs and to identify the most effective platforms for collecting UAV-based photogrammetric imagery in an underground mine. Each of the four participating teams was challenged to fly their UAV-based systems (Hovermap, Elios, M2, Ranger/Batonomous) in underground drifts and long-hole stopes while capturing high-quality imagery that could be used to create a 3D digital photogrammetric model of the opening. The demonstrations were held at Barrick Gold Corporation’s Golden Sunlight Mine (GSM) in Whitehall, MT. The systems were evaluated based upon the performance of the collision avoidance (or recovery) system in the underground environment and the quality and accuracy of the data provided. By successfully completing the underground flights and demonstrating well-developed SLAM-based collision avoidance, the Hovermap system proved to be the most reliable, robust, and easily controllable system. The Elios system, relying on collision recovery rather than avoidance, is an affordable alternative for flights in difficult environments. The imagery collected by each system was used to generate photogrammetric point clouds using three software packages: Agisoft PhotoScan, Bentley ContextCapture, and Pix4Dmapper. The point clouds were qualitatively compared based on completeness and detail and quantitatively evaluated for accuracy by comparing the geometry of the point cloud to LiDAR scans of the stopes. Based on the results of the qualitative comparison, the point clouds considered in the accuracy evaluation were built using the photogrammetry software Bentley ContextCapture. When the photogrammetric point clouds were compared with the LiDAR point clouds (assumed to be an accurate baseline reference), the mean error values ranged between 0.47 and 2.86 feet. Despite the different conditions and locations in which the imagery was collected for each model, the observed error varies by less than one order of magnitude. Improvements in the coverage and overlap of the imagery as well as in the method used for georeferencing could further increase the accuracy of the photogrammetric point clouds

e-minesafe Safety and Training Simulator. The integration of knowledge and skills to achieve safe human responses

The Joint Coal Board (JCB) is concerned about the number of accidents and fatalities associated with the operation and maintenance of equipment in the New South Wales coal industry. In the last decade, equipment has become more sophisticated and the manner in which work is organised and performed in the industry has changed significantly. Therefore, through its Health and Safety Trust, the JCB commissioned a consortium comprising Mine Site Technologies and the School of Mining Engineering at the University of New South Wales to research the introduction of equipment training simulators into the industry. The research is planned to be undertaken in four stages. This report presents the outcomes of the first two stages. It recommends the development and testing of an interactive, immersive, virtual reality prototype simulator providing true to life imagery. The simulator will be modular in design such that various items of equipment can be plugged in as required. It is proposed that the evaluation of the prototype simulator be based on a continuous miner and a roof bolter, with the option to add a dump truck. This is because a high accident rate is associated with these items of equipment. Most of the research undertaken on the project to date has focused on these machines. The research has confirmed that JCB Simulators have a huge potential to improve: Mine Safety Productivity Business Performanc

Estimation of capital costs for establishing coal mines in South Africa

Coal is one of the most abundant mineral resources in South Africa and it is predominantly used in electricity generation in the country. Of all the mineral resources in South Africa, development of coal resources attracted most of the financial investment between 2010 and 2013. Development of mining projects requires estimation of capital and operating costs in the early stages of the project’s life. Estimation of costs is an essential exercise that assists on deciding the future of mining projects. Despite all the investment in the South African coal mining sector, there is still little consistency in unit capital costs invested/required to develop coal mining projects. Lack of research within the area of coal mining projects’ costs is attributable to a lack of publicly available information. Research in this area will enable investors and operators in the coal mining sector to be able to assess financial viability early in the project life. This study reviewed coal mining projects across the world, looking at publicly available capital costs. The study further recognised similarities between the South African and Indian coal mining sectors thereby enabling the research to leverage data from the Indian coal mining sector to estimate capital costs in South Africa. The parametric estimating technique was used to estimate capital costs in this study. Finally, six formulae were initially developed to estimate the capital costs of establishing coal operations in South Africa. The six formulae were then reduced to three formulae by eliminating outliers. The formulae can be used to estimate capital costs to an error of magnitude error level of -30% to +50%. An estimation formula for underground longwall operation was not developed due to an insufficient number of underground longwall operations in both South Africa and India. In conclusion, this study recommends further research to develop more formulae which can be used to estimate capital costs more accurately

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Analysis of Haul Truck- Related Fatalities and Injuries in Surface Coal Mining in West Virginia

Trucks are the primary means of haulage in surface coal, metal, and nonmetal mining operations. The number of fatal accidents involving trucks is higher when compared to all other mining equipment. The Mine Safety and Health Administration (MSHA) reports that 137 fatalities were haul truck- related in the United States between 1995 and 2011. A total of 12 truck-related accidents, including 13 fatalities, were recorded in surface coal mining operations in West Virginia (WV) during this period. The objectives of this research were to (i) analyze the root causes of these accidents, and (ii) develop effective intervention strategies to eliminate these fatalities. The Fault Tree Analysis (FTA) technique was used to systematically analyze truck related fatalities. Data on truck-related injury accidents in West Virginia surface coal mines during 2012 and 2013 were also analyzed in this study. Results of the study indicate that inadequate or improper pre-operational check and poor maintenance of trucks were the two most common root causes of these accidents. A total of eight accidents occurred on haul roads, while 10 accidents occurred while the trucks were moving forward. The two most violated provisions of Code of Federal Regulations were 30 CFR§77.404 - Machinery and equipment; operation and maintenance (six times), and 30 CFR§77.1606 - Loading and haulage equipment; inspection and maintenance (five times).;A total of 223 reported injuries were recorded at West Virginia surface coal mines. With the exception of two missing data, a total of 178 accidents were equipment-related and 43 accidents occurred without equipment being involved. The equipment categories accounting for the most number of injuries were: truck (57 times) and bulldozer/dozer/crawler tractor (43 times). The majority of the truck-related injuries occurred within the worker\u27s first five years at the mine and within the first five years at their current job title. Workers between ages 25 and 39 had the greatest percentage of injuries. Most injuries were recorded during Section I (6:00 a.m. - 2:00 p.m.), and the fall season has the greatest number of truck-related injuries of all four seasons. Regarding the nature of injury, sprains and strains made up about 32%, topping all other types of injuries. The most commonly injured body part in truck-related injuries was the Multiple parts. .;A two-pronged approach to accident prevention was used: one that is fundamental and traditional (safety regulations, training and education, and engineering of the work environment); and one that is innovative and creative (e.g., applying technological advances to better control and eliminate the root causes of accidents). Suggestions for improving current training and education system were proposed, and recommendations were provided on improving the safety of mine working conditions, specifically safety conditions on haul roads, dump sites, and loading areas. Currently available technologies that can help prevent haul truck-related fatal accidents were also discussed. The results of this research may be used by mine personnel to help create safer working conditions and decrease truck-related fatalities and injuries in surface coal mining