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By Xiaoting Li, Richard Adasiak, Benjamin Mirabile

Pumpable J-Crib stopping walls and Jennmar high-strength (HS) stopping panel walls are widely used for resisting high-ventilation air pressures and roof support in underground mines due to their installation efficiency, convergence allowance, and better performance than traditional concrete block stopping walls. Based on the different entry dimensions and required maximum ventilation air pressures of up to 36-in. water gauge, 24-, 27-, 30-, and 36-in.-diameter pumpable J-Crib stopping walls, or 18- to 30-in. thick Jennmar HS stopping panel walls can be designed, and their support capacities and convergence allowances are evaluated for better roof control. Pumpable J-Crib stopping walls consist of 0.187-in.-diameter steel wire reinforced, JCrib material-filled bags, and non-wire-reinforced filler bags in between. Jennmar HS stopping panel walls consist of two, 20-gauge (0.04-in.-thick), galvanized, cold-formed steel panels with J-Seal materials pumped in between. Based on the fundamental structural design principles and specifications of ACI 318-19(22), the compression and flexural strength, and shear resistance of the walls can be checked to be compliant with the codes and specifications following the load and resistance factor design (LRFD) method. Specifically, the 0.187- in.-diameter steel wires in the pumpable J-Crib walls and 20-gauge steel panels in the HS stopping walls are integral parts of the walls and are considered to effectively confine the J-Crib or J-Seal materials. These steel elements play a significant role in reinforcing and improving the strength or support capacity of the stopping walls from a structural design perspective. Because of the high ductility of low-density J-Crib/J-Seal materials and steel elements, these walls are good at accommodating or absorbing the roof convergence (≥5% entry height), which cannot be matched by a traditional concrete block stopping wall (Figure 1). Based on the successful design and application of these stopping walls in different mines for improving roof stability and avoiding the air leakage, the authors have formulated the design methodology and verified independently by the finite element analysis (FEA) simulation technique. This design methodology combines the fundamental structural design principles with specific wall dimensions and material properties, facilitating mine engineers and government agencies to understand the in-depth working mechanism of these stopping walls.

Jul 1, 2023

By Steven Keim, Steven Stansfield, Kevin Andrews

Geotechnical, geological, and hydrogeological fieldwork and data collection for the mining industry is often conducted by younger, less-experienced staff members. In many cases, fieldwork personnel are recent college graduates or are otherwise relatively new to the mining industry. Due to busy schedules, staffing shortages, project budgets, and in some cases, poor management, new fieldwork hires often do not receive thorough training. In the past and the present, many workers in the mining industry learn(ed) by “doing.” While this is still considered to be the most practical approach and the easiest way for a company to determine the skill set of an employee, there is always some risk involved in the “feet to the fire” method. The reality is that field personnel are often collecting data on which major operational decisions will be made or on which regulators will base enforcement decisions. Inadequately trained or inexperienced field personnel may collect inaccurate data or fail to recognize key conditions that may influence decisions made by regulators, design engineers, mine managers, and corporate leaders. Even the best engineers may come to incorrect conclusions if they are starting with poor input data. Decisions based on inaccurate data often result in delays, budget issues, unsafe conditions, or even worse consequences. This paper provides numerous mining industry examples of incidences where inaccurately collected field information did, or could have, resulted in major adverse consequences. The paper also offers some simple approaches to minimizing the potential for such problems.

Jul 1, 2023

By Xinghai Lei, Nan Wang, Xiangyang Jin, Yuqi Ren, Yang Li

The upward mining method, as an effective mining method, is widely employed in close-multiple coal seams to extend the service life of coal mines and improve the recovery rate of residual coal resources. However, the upward mining feasibility of the upper coal seam is related to the height of caved zone of interlayer strata after lower coal seam extraction. Therefore, it is extremely important to determine the height of the caved zone after the lower coal seam. The No. 9 coal seam (upper coal seam) and the No. 12- 1 coal seam (lower coal seam) in the Qianjiaying coal mine are selected as the research object. The characteristics of the interlayer strata structure field, fracture field, and subsidence field are studied using UDEC numerical modeling, in which the height of the caved zone is gotten after the No. 12- 1 coal seam extraction. Furthermore, the height of the caved zone is verified by onsite detection of GPR and BTVI. In the numerical modeling, the height of the caved zone is 19.6 m. And the height of caved zone is 14.7 m and 15.4–21.2 m, respectively, by using the onsite detection of GPR and BTVI. The heights of the caved zone onsite detection results are consistent with the numerical modeling result. The No. 9 coal seam is always located above the caved zone after the No. 12- 1 coal seam extraction. Consequently, the upward mining of the No. 9 coal seam is feasible.

Jun 28, 2023

By Leping Liu, Li‑Ting Chen, Sachin Urade, Pengbo Chu

Mining industry requires constant adaptation to its practices in response to the dynamic change of the societal demand. Little information exists regarding how the US metal mining engineering education is prepared to meet with its industry needs due to the increase in critical minerals driven by the green energy transitions. This study aims to provide industrial perspectives on the current US metal mining engineering education and areas for improvement. An online survey with both closed- and open-ended questions were designed to seek opinions from the US mining industry. Findings indicated a shortage of qualified graduates from the current US metal mining engineering education system. The qualifications need to be improved include engineering sciences underlying mining methods, mining design experience, mining feasibility study, the connection between theory and practice, and understanding of the overall mining operation. The survey also identified future desired qualifications. The most desired ones in the next 5 years include an ability to acquire and apply new knowledge as needed and sufficient experience in the field. Respondents, regardless of the nature of their affiliated mining companies, unanimously recommended that collaborations between the industry and academia should be enhanced. The analysis of the results concluded four recommendations: (1) involve more multiple stakeholders in reforming mining education programs, (2) reinforce field experiences as a key part of mining engineering education programs, (3) enhance a closer collaboration between academia and industry, and (4) integrate emerging technologies (e.g., artificial intelligence/virtual reality) guided by pedagogical theories into new mining engineering curriculums.

Jun 26, 2023

By Chris Roos

The mining industry has made strides in planning for operations to be safe for the workforce and public, to protect the natural environment, and to maximize value for shareholders. However, many of the techniques used to accomplish these goals are based on estimated models of mineral content, production efficiency, and economic performance. The industry has begun to recognize the need for understanding the uncertainty in these models, how it affects their decisions, and what the potential impacts are for each project. The challenge has become how technical professionals can effectively communicate the impact of this uncertainty and its importance to both technical and non-technical audiences. This paper presents mining engineers (and other technical professionals) with traditional and modern principles of data visualization and technical communication that can assist with communicating the effects of uncertainty in the mine planning process.

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 Javad Sattarvand, Frimpong Kwaku Asare

The safe evacuation of miners during an emergency in the shortest amount of time is one of the most essential features of a mining operation. Despite advancements in mine evacuation research, the usage of mine vehicles during evacuation has not been considered. The majority of recent studies have been done on miners’ evacuation on foot. Mathematical formulations including Minimum Cost Network Flow (MCNF) models, Dijkstra's algorithm, Floyd-Warshall algorithm, and Ant Colony Optimization models have been used to solve mine evacuation problems. These models, which concentrate on determining the shortest escape routes during the evacuation, have been found to be computationally expensive and may not offer the best solutions in some cases. The goal of this paper is to incorporate light vehicles in the evacuation process using a mixed-integer programming approach. The methodology aims to minimize the total time required to evacuate the mine, ensuring minimal exposure of the miners to the adverse conditions that triggered the evacuation.

Jun 25, 2023

By Amy McBrayer, Andrea Brickey

Limited research has been conducted related to the development of mathematical optimization models for production scheduling at large surface coal mines using the area, i.e., strip, mining method. Increased economic and technical challenges have led to a new interest in production schedule optimization models for these type of mining operations like those currently applied to model underground and open pit mines. While area mines share similar characteristics to both open pit and underground metal operations, neither an open pit nor underground production scheduling model adequately depicts an area mine mathematically. We present an integer programming model with blending constraints and discuss the challenges and opportunities related to the implementation for area mines as well as future research in area mine production scheduling.

Jun 25, 2023

By Kate Willa Brown Requist, Eric Lutz, Moe Momayez

As air quality sensor networks become increasingly popular in underground coal mines, it is important to generate paradigms for the application of the collected data. To date, air quality sensing has been primarily used as an early warning system for hazardous air conditions. Using data collected from a network of sensors in a US underground coal mine, we have created multiple visualization methods to show the interactions of carbon monoxide evolution with ventilation airflow. These visualization methods can allow for further analysis of the source of contaminant, as well as better data resolution across the area of concern within the mine. By utilizing univariate spatial interpolations, we present methods for identifying the movement of carbon monoxide at one-minute intervals. The resulting visualizations display the evolution of carbon monoxide concentration across a sizeable study area over a period of 14 minutes.

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 M. Shahsavar, J. Sattarvand, J. Gomez

Mining can be categorized as a hazardous activity, considering some factors such as environmental conditions with a considerable presence of humidity, suspended particles, or falling rocks have affected the severity and number of accidents compared with other economic sectors. The industry analyzes incident reports to narrow the rate of severe injuries and fatalities, conducting root cause analysis and identifying leading indicators. As the International Council on Mining and Metals noted, the vast trove of incident data is not analyzed as much as possible due to a lack of analytics expertise at mine sites. However, machine learning could solve the problem of analyzing all the incident data in a no-timeconsuming way, considering the abundant data, and without using expert personnel in data science. Thus, a Convolutional Neural Network and a Naïve Bayes model were introduced to perform classification in the MSHA database. The database from 2020 consists of 60 fields to describe safety incidents; these fields include mine I.D., accident date, subunit (mill, surface), material extracted, and other metadata.

Jun 25, 2023

By Milka Madahana, John Ekoru

Particulate matter is one of the leading pollutants in the mines and in the communities in the areas near or surrounding the mines. The availability of large Clinical and environmental data sets obtained from the mines has brought about the need to use better approaches to analyze the collected data. Prediction and control of particulate matter concentration in the mining environment is essential in minimizing air pollution and protecting the respiratory health of mine workers. The main objective of this research work is to analyze datasets obtained from a mining area using machine learning techniques which will then be followed by the real-time estimation of dust particulate in the mines. Datasets collected over 8 years in an hourly sampling rate from an open cast mine is cleaned and analyzed for trends and patterns. Artificial Neural Networks (ANNs), Support Vector Machines and decision trees techniques are applied to analyze and predict the particulate concentrate. The estimation of particulate concentration using Artificial Intelligence will significantly contribute toward the development of quantitative tools to be used in correlating of cause and effect of dust concentration in mines. The results obtained in this work will form a sub-unit of a respiratory health monitoring system for mine workers. The system is essential in the collection, analysis, and tracking of mine workers’ respiratory health, thus it forms part of an early intervention system for mine workers. From the existing theory, we expect the Artificial Neural Network will perform much better than previously used classical models.

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 Josiah Pohl, Christopher Utter, Kyle Everly, Akshay Chowdu

Battery Electric Vehicles offer increased energy efficiency for open-pit load & haul operations at a potentially lower overall carbon cost. Electrifying a mine’s fleet, however, presents a unique set of operational challenges. In this study, we explore the effects of electrification on productivity at real operations using a Discrete Event Simulator. The model can simulate multiple shifts, a mixed fleet of diesel and electric trucks, charging and refueling stations, trolley systems, and accurate shovel-truck loading cycles. This study helps determine the trade-offs of adopting electric fleets and how they interact with the layout of today's mines.

Jun 25, 2023

By Alessandro Navarra, Aldo Quelopana

For decades, open-pit mine planning algorithms have incorporated geological uncertainty and other elements or characteristics for increasingly realistic representations. Among these complexities is the detailing of downstream processes that occur inside processing plants. Previous studies have demonstrated that the strategic mine plan and mineral concentrator design are coupled, and should therefore be optimized simultaneously within the same quantitative framework. However, existing frameworks offer limited representation of operational modes. This work demonstrates an approach to resolving this gap, in an optimization based on a parallelized Variable Neighborhood Descent (VND). The resulting algorithm embeds mass-balancing linear programming (LP) using a DantzigWolfe decomposition. A case study of a Zn-Pb deposit is presented to show the performance reached.

Jun 25, 2023

By Augusto Andres Torres Toledo, João Felipe Coimbra Leite Costa, Luciano Nunes Capponi, Diego Machado Marques

Short-term mine planning to open pit requires defining diglines to orientate the excavation geometry. In deposits with multiple chemical variables, it is necessary to have more rigorous control over the feeding of the processing plant. This study analyzes the impact of incorporating multiple chemical variables, the risks generated by geological uncertainty, and the number and starting point locations of shovels in the sequential design of diglines. The objective is to maintain and preserve the low-grade variability, required tonnage, and continuity in sequentially selecting blocks. To achieve this, the probability distribution function of each chemical variable is used while attempting to maintain the ore grades as constant as possible. As it is difficult to maintain multiple elements with low variability simultaneously, the relevance of each chemical variable in the economic benefit is identified. Weights are assigned to signal the importance of each element based on its significance in the recovery process. Several diglines, consisting of sequential blocks, were generated on the same seed point to meet the weekly ore demand. Optimizing the stationarity of multiple chemical variables, accessing and reducing the geological uncertainty performed through geostatistical simulation of grades and transfers functions, considering the number and location of shovels, while meeting the operational restrictions of the selected blocks. The optimized digline with the multiple grades as stationary as possible is retained. The process is illustrated in a phosphate mine. The results show that the proposed method is viable and allows for a quick solution.

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 Katherine Hynard, Colin Eustace

Development quarterly mine plans is generally time-consuming and requires consideration for many operational constraints and competing objectives. With lengthy turnaround times, there is often limited opportunity to adjust the quarterly plan details to account for differences between actual state of the mine operation at the time of execution and expected state at the time that the plan was developed. Actual mining task completion times can vary significantly from the expected task completion times, due to many factors. In the absence of revision to account for differences between the planned and actual state, resource allocations and mining sequences, embodied in the quarterly mine plan may no longer be the best approach at the time of execution to achieve an efficient mining operation. Simulation and optimisation tools can be used to support rapid refinements for development and execution of weekly mine plans. When initialized with an operations data feed to establish the actual state of the mine operation, optimisation models can be used to facilitate both automation and revision of the quarterly plan to produce a weekly or daily plan for execution. This approach can remove much of the manual burden of revision if the existing quarterly plan no longer represents the best approach to meeting production objectives. Following initialisation with mining actuals, simulation models can provide a representation of the range of likely mine operations outcomes. The models can be used to accelerate the planning process by providing visibility of future performance and identifying likely operational issues. They can also be used to test the robustness of the plan with respect to performance variability and response to disturbances. Following identification of issues, simulation models can also be used for evaluating mitigation measures, through automated testing of alternative plans. Integration of mine data feeds with simulation and optimisation tools enables automated optimal responses to variations between the quarterly mine plan and actuals to be tested prior to release of and execution plan and implementation. The use of simulation and optimisation in weekly mine planning will be discussed in this paper with reference to surface mining examples.

Jun 25, 2023

By Jay Nopola, Christopher Johnson, Matthew Minnick, Erik Hemstad, Erik Walega

Mining data collection, storage, and interpretation methods have progressively changed over time. Data collection, for the most part, has transitioned from handwritten notes and logs based on manual gauge readings to real-time data streaming through instrumentation capable of directly communicating with site-based data historians or cloud storage. Data collection and storage have had to adjust commensurately with the volume of data, leading to more complex database architecture. Geoscience interpretation methods have also changed; however, the first goals of modeling and understandings from first principles remain the same and are typically deeply rooted in historical academic work. Machine learning methods are becoming more common, available, and implemented by industry and practitioners. Despite the wide accessibility of machine learning methods, the population of geoscience subject matter experts in the underlying theory of this analysis type is limited. This form of analysis, therefore, must be conducted carefully and in conjunction with subject matter experts, on both machine learning theory and the field it is being applied to. This paper aims to provide background history on data analytics and machine learning, current methods and practices, and high-level geoscience case study examples, as well as take aways regarding the mining industry, potential pitfalls specific to geosciences, and possible perspectives on the direction of artificial intelligence in the future.

Jun 25, 2023