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By Timothy J. Matthews, Ted Klemetti, Timothy J. Batchler, Daniel J. McElhinney

Unplanned tailgate collapses and difficult mining conditions in U.S. longwalls have necessitated the use of innovative tailgate support systems. Severe ground movement induced by longwall loading has caused operators to utilize secondary support systems that can match yield and strength requirements with expected ground reactions. Standing support performance requirements depend on the nature of the ground deformation behavior, and therefore require a good understanding of the interaction between the support and ground to achieve an optimum roof support system. Mining operations often employ standing support strategies that are unique to their mine. A support system that functions successfully in one situation could fail in another situation. It is important that the design and installation of the standing support systems provide the highest probability of preventing roof falls. Pumpable roof supports are currently being used to provide a safe working environment for longwall mining. A full understanding of the impact of these performance characteristics is necessary to optimize the support application and to provide a foundation for making further improvements in the support performance. Optimization of roof support applications helps to ensure the protection of mine workers by preventing roof falls due to inadequate support design. The load capacity and stiffness characteristics of a 27-in.-diameter pumpable support system was evaluated using the National Institute of Occupational Safety and Health (NIOSH) Mine Roof Simulator (MRS). Previously, tests to evaluate the performance characteristics were performed on a single pumpable support. The goal of this new research is to evaluate the performance characteristic effects of the pumpable support system and compare it to an individual pumpable test examination. This research of a pumpable support system configuration simulates the current installation practices in U.S. coal mines.

Jul 1, 2023

By Jiachen Wang, Nan Wang, Yang Li

The close-multiple coal seam is an important component of China’s coal resources and plays a key role in total coal reserves. However, the overburden movement in the mining process of close-multiple coal seams is usually extensive, which is seriously affected by overmining. It is therefore essential to study the mechanism of roof breakage, the relationship between the shield and the surrounding rock, and the mechanism of overburden movement to ensure the safe operation and the efficient exploitation of close-multiple coal seams. This work first proposes a technical route for intelligent ground control. Second, the principle of cooperative control between the shield and overburden roof is revealed, and the shield support capacity calculation method is summarized. Third, the quantitative criterion for disturbance degree of key stratum (KSDD) is proposed based on the key strata theory. Finally, several intelligent evaluation and analysis systems have been developed in terms of overburden breakage and shield support capacity, multi-information analysis of ground control, and disturbance degree of overburden. The research results may provide an essential reference for the development of intelligent ground control in the mining process of close-multiple coal seams

Jul 1, 2023

By Ernesto Maldonado, Zach Agioutantis, Jesus Romero

Reliable prediction of dynamic deformations is important when planning to undermine important structures that cannot tolerate large relative deformations and or large horizontal strains. This paper presents examples of dynamic deformation profiles that include subsidence, horizontal displacements, and horizontal strain for a number of points over a retreating longwall panel in the eastern United States. The prediction points are located at different distances from the excavation rib and therefore present a different response in terms of dynamic movements. The formulation is based on the influence function method as implemented in the Surface Deformation Prediction System (SDPS). Results show that the subsidence development curve may be reliably predicted for surface points in the middle of the panel. However, in cases where points may be affected by the magnitude of the edge effect offset, subsidence predictions may be very conservative, that is, higher than actual movements.

Jul 1, 2023

By Kevin Saeler, Patrick Willaman

Utilizing automation is one way to improve competitiveness. Roof and rib bolting in underground coal mines has not changed significantly for more than 40 years. Most roof bolting is still a largely manual process involving high levels of staff carrying out repetitive work. Roof bolting statistically has one of the highest levels of underground accidents, including finger, hand, and arm injuries. Operators are exposed to falling objects and becoming entangled in roof bolting apparatus while roughly moving awkward 6-ft drill steels and roof bolts. It is estimated that over 25 million roof bolts are installed annually in the U.S. underground coal market with well over 1,500 operators involved in roof bolting on a day-to-day basis. Recently electrohydraulic roof bolting rigs have been installed to offer partial automation of the drilling cycle and the bolting cycle. The next stage is to provide a storage and loading system to allow full automation of fullcolumn resin roof bolts. This paper reviews the latest electrohydraulic roof bolting equipment and discusses the testing of a fully automated drill rig. Full electronic control allows each step of the roof bolting process to be recorded, providing a digital record of each installation, including hole depth, spin time, gel time, torque, and cycle time. Other benefits include monitoring of water pressure and flow, used to prevent blocked drill steels as well as digital fault finding. A fully automated roof bolting machine greatly improves operator safety and gives high-quality repeatable roof bolting with reduced cycle times.

Jul 1, 2023

By Zachary E. Wedding, Zach Agioutantis

Excavation of underground openings results in the redistribution of the in situ stress into the rock adjacent to the opening. Stress redistribution depends on opening geometry, pillar geometry, material properties, possible confinement, and constitutive models describing the behavior of adjacent rock. This paper presents typical pillar stress distributions for elastic and yielding pillars as a function of opening geometry, material properties, and material constitutive models and highlights the differences between them. Stress distributions are compared with empirical strength profiles that have been developed for coal pillars as well as actual pillar geometries documented for stone pillars.

Jul 1, 2023

By A. Adhikari, P. Tukkaraja, S. Jayaraman Sridharan

Shafts are critical components of the mine ventilation systems and contribute significantly to the mine ventilation pressure. Estimation of shaft pressure losses is an important aspect of mine ventilation planning and shaft frictional pressure losses are extensively studies. Shock losses contributed by shaft buntons are estimated using the interference factor model developed half a century ago. This study aims to develop a better performing prediction model for shaft buntons using state of the art experiment and numerical data, and machine learning techniques. Results show that the present prediction model is performing poorly, and the prediction performance can be improved by incorporating additional predictor variables. The Artificial Neural Network standardized Machine Learning model developed in this study is able to predict the shaft bunton shock loss with high accuracy (R2 = 0.956).

Jun 25, 2023

By Alireza Kamrani, Yashar Pourrahimian, Hooman Askari-Nasab

In-pit crushing and conveying (IPCC) is an option to decrease the enormous operating costs that a truck shovel can introduce in an open-pit mine. Finding the best locations for the IPCC over the mine life will impose a new set of requirements for the mine planning. Through a two-step mathematical programming model alongside the road network, this method finds the optimal in-pit crusher locations and relocation times first and then optimizes the long-term mine schedule. The model is verified by a real mine. The results show that the truck travel time could be reduced by 500% and the discounted cash flow increased by 8% with an optimized schedule considering the crusher placement.

Jun 25, 2023

By Javad Sattarvand, Amin Moniri-Morad

Predicting equipment failure behavior plays a critical role in providing a safe operation. Although various reliability evaluation methods are available for estimating failure behavior profiles, these approaches are based on mathematical formulation, leading to actual operation simplification and improper estimation of failure trends. This study proposes a discrete-event simulation (DES) approach to model fleet failure behavior. This approach can model the maintenance strategies in multi-component systems with minimum simplifications. In this regard, the first step was to collect a historical failure time dataset (e.g., the time between failures). Then, the DES model is established to predict the severity of failure events and design maintenance strategy considering actual operation circumstances. Afterward, the maintainability analysis was performed to estimate equipment downtime and availability. The achieved results showed that the system availability was about 85%. Also, the findings demonstrated that the DES could efficiently predict the equipment failure behavior using various stochastic events.

Jun 25, 2023

By Robert Bissonette, Samir Sbai

A critical component of implementing autonomous systems is the ability to use information from a robust sensory and information system to make sound, predictable, and repeatable decisions to assure safety. This means that any such system would need to evaluate what objects (people, equipment, boulders, etc.) of interest exist in that environment (with relevant data about identity, trajectory, characteristics, etc.), project a future continuum of those objects (including considerations for variabilities), and make control intervention choices to minimize risk due to exposures in the present operational area. The components of an overall system would include: a) sensor and data fusion, b) probability projection of future states, c) generation and evaluation of alternate futures, and d) implementation of best course of action to reduce risk. The National Institute for Occupational Safety and Health (NIOSH) Spokane Mining Research Division (SMRD) is studying this framework for applications for autonomous mining equipment. This paper focuses on using information from the sensor fusion engine to generate a continuous projection of future and alternate states (b and c above) incorporating the factors of confidence, accuracy, tolerance, and other variabilities. We refer to this as Machine Situational Awareness (MSA).

Jun 25, 2023

By Błażej Doroszuk, Robert Król

Mill drums are essential equipment in various industries for the comminution of materials. To optimize their design and operation for efficient and cost-effective processing, it is necessary to understand the behavior of the mill drum under various operating conditions. This study investigates the behavior of a mill drum using a combination of experimental and numerical methods. The factors that can influence the behavior of the mill drum, such as the size and shape of the mill, grinding media, rotational speed, filling level, and material properties, are discussed. Experimental methods can be limited in obtaining measurements of internal processes, so numerical methods such as the Discrete Element Method (DEM) are also used to simulate the behavior of the mill drum. In this study, the experimental measurements of the motion of the grinding media are obtained using computer vision methods, and the numerical simulations are calibrated using the experimental results. A designed experiment approach is used to evaluate the performance of the mill drum under various operating conditions. The results of this study can be useful for the design and optimization of mill drums to achieve better performance and energy efficiency.

Jun 25, 2023

By Mario E. Rossi

Often geometallurgical models are prepared at various project stages from very few data and are assumed to be representative of very large tonnages. These may include recovery models; crushing and grinding parameters and models; models of concentrate grades; models of acid consumption; etc. These models commonly do not carry a measure of uncertainty or variability, and are taken at face value at the time of design and planning. This paper proposes a proactive approach to modelling variability, providing uncertainty estimates and thus allowing for risk analysis and mitigation of geometallurgical models. The variability model is built up by understanding the limitations of the pillars of a geometallurgical model: data uncertainty; geologic model uncertainty; domain definition uncertainty; and parameter estimation uncertainty. Of these factors, typically domain definition is the most significant contributor to geometallurgical model variability. First, the fundamental supports for these models are briefly discussed, with domain definition emphasized. Second, two methods for uncertainty modelling are proposed. The first, more applicable to conceptual or early-stage projects, is a simpler and more expedient alternative that provides initial assessments of variability. This is intended to aid management in decision-making related to budgeting, including further sampling and testing. The second, a block-by-block approach that allows a detailed assessment of risk for specific designs and mine plans, thus providing further opportunities for mitigation. In the example provided, emphasis is placed in communicating this variability to both management and technical personnel in the form of “measured-indicated-inferred” indicators and also block-by-block confidence intervals.

Jun 25, 2023

By Brendan Demich, Linda L. Chasko, Brendan Macdonald, Timothy J. Orr, Jessie J. Mechling, Cassandra Hoebbel, Jason Navoyski, William Helfrich, Jessica Cohen, Paul E. Schmidt, Jennica L. Bellanca

The National Institute for Occupational Safety and Health (NIOSH) in partnership with the Mine Safety and Health Administration (MSHA) developed VR Mine Rescue Training (VR-MRT). It is a training platform that allows mine rescue team members to explore virtual emergency scenarios collaboratively. Team members use a head-mounted display to visualize and interact together with equipment, hazards, and dynamic ventilation. All player actions are recorded and displayed for debriefing. Researchers at NIOSH demonstrated the training platform at various mine rescue competitions, including the International Mine Rescue Competition, and collected feedback. Participants’ responses were overwhelmingly positive. This paper describes the development of the software platform and the results of the usability surveys and interviews.

Jun 25, 2023

By Ademar Lopes, Victor Silva, Heitor Silva, Gustavo Usero, Roberto Rolo, Lucas Pereira, Gabriel Moreira, João Lague, Arthur Endlein, Matheus Feitosa

Grade control models are supposed to provide a higher resolution than long-term or interim models. However, available grids from diamond or reverse circulation drilling do not provide that required resolution. In this study, a geostatistical workflow is proposed to integrate available grade data from production blastholes with its operational parameters taken when drilling downhole. The step of geological logging can be replaced by machine learning models for classifying blastholes into lithologies and hardness classes. Next, indicator and ordinary kriging use the classified blastholes and previous data for estimating the model domains and the grades within each one. The workflow is applied in a world class iron ore mine in Brazil.

Jun 25, 2023

By Amin Mousavi, Soroush Khazaei, Yashar Pourrhimian

As a factory-like method, sublevel caving consists of development activities, stockpiling and production activities that are carried out on different levels independently. Therefore, considering the operation from the development phase to the plant will lead to more reliable production scheduling. This paper presents a mixed integer linear programming framework for the long-term production scheduling of sublevel caving that maximizes the net present value while satisfying development activities, mining, and processing capacities, continuous mining, the allowable number of active mining units, grade blending, and vertical and horizontal sequencing. The model is coded in Jupyter Notebook and is solved using the CPLEX solver.

Jun 25, 2023

By Artur Skoczylas, Paweł Stefaniak, Sergii Anufriiev, Wioletta Koperska, Maria Stachowiak

In many underground mines the excavated ore is produced during the blasting process. Drilling machines that are used to drill the blastholes, where later the explosives will be mounted, are crucial for this task. In order to optimize the currently used techniques, great emphasis is placed on increasing the efficiency and safety of blasthole drilling operations. The natural consequence is the development of monitoring systems and algorithms for processing operational and historical data. In this article, we present the result of the analysis of drilling data carried out as part of the IlluMINEation project. The paper proposes an algorithm for work cycle detection for drilling machines, together with the estimation of general operating parameters and diagnosing the drilling process on the basis of long-term data. Based on the available data, selected components of the drilling cycles have been indicated. Several approaches based on the parameters of the defined cycles are proposed. These approaches can be used in the future to create a full-fledged system for examining the effectiveness and safety of mining drilling rigs.

Jun 25, 2023

By Jose A. Gomez, Javad Sattarvand

Failures of tailings dams have been happening lately. Due to the lack of laws on particular design criteria and stability requirements related monitoring during construction and maintenance, they are thought to be more fragile than hydraulic dams. Monitoring the dam is therefore necessary to understand its current condition and guarantee its safety. The physical condition of the dam could be evaluated with the early identification of seepage. Additionally, due to their adaptability and capacity for high-resolution data collecting, UAVs are an excellent choice for efficiently covering the tailings dam site. UAVs may capture high-quality photos when equipped with a high-resolution RGB camera, thermal sensors, or multispectral sensors. When these sensors are paired with image processing and machine learning algorithms, the result is a reliable estimate of the dam condition.

Jun 25, 2023

By Cesar Carrasco, Heath Arvidson, Mark Forster

Block models built to represent the geology and rock characteristics of mineral deposits represent huge complex data sets, which form the basis of strategic and business planning, and public reporting of Mineral Resources and Mineral Reserves. Recent advances in cloud computing and database technology developed for the Internet of Things offer a different way of working with these critical data sets. Enabling a cloud based, open data structure with high performance data analytics capability presents a compelling improvement in governance and a democratization of ore body knowledge that can lead to automation use cases that support the digitalization of mining.

Jun 25, 2023

By Paweł Stefaniak, Natalia Duda-Mróz, Sergii Anufriiev, Wioletta Koperska

Due to the high environmental risks and negative impact of a failure, tailings storage facilities (TSFs) need constant monitoring. Advanced mathematical models have been developed in the past to predict the behavior of TSFs and raise alerts if needed. To be precise and reliable, such models need a spatial distribution of soil types within the dam as an input. Getting this data from laboratory measurements is time and costconsuming. In this article, we propose an ANN-powered algorithm, which allows us to accurately estimate the soil distribution based on a cone penetration test (CPT).

Jun 25, 2023

By Christoph Mueller, Grzegorz Tabak

Basing on five years of experience, this paper discusses modern e-drive systems used in underground transport. This covers Energy distribution and cabling principles, inverters and motors as well as auxiliary aggregates. Also an overview of different regimes for battery charging and the energy management and the method of control of the traction are lined out: This means that throttle and brake are not handled separately, but integrated into “longitudinal control” algorithms which assure that a commanded speed is maintained while synchronizing the multi motor systems and taking into account the constraints imposed by e.g. slope grade and battery parameters.

Jun 25, 2023

By George Biro, Christoph Mueller

A number autonomous operations is in place in several, mainly in fenced areas, where safety is assured by locking off the entire area for other traffic. However autonomy can be brought much further if functional safety is assured and a real mixed traffic of autonomous and manually operated vehicles is possible. MobileTronics has created a modular software and control framework for autonomous machines. The paper covers application examples in which the deterministic object recognition in combination with probabilistic algorithms and sensors is used as a basis for functional safe certification of mixed traffic autonomy.

Jun 25, 2023