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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 Mark Van Dyke, Mark Mazzella, Gamal Rashed, Nicole Evanek, Brent Slaker

In the past decade, there have been several incidents of massive ground collapses in underground stone mines in the eastern United States. Fortunately, these events have not resulted in any fatalities. However, three mine workers were injured and many others were at risk due to the hazard from the fall of ground and/or the airblast associated with these massive collapses. The National Institute for Occupational Safety and Health (NIOSH) initiated a project to better understand the causes of these massive ground collapses to help prevent their future occurrence. The main objective of this study is to investigate the main factor(s)that initiated one of these massive ground collapses. The authors relied on several tools including three-dimensional (3-D) LiDAR scans for the surface subsidence feature and the underground mine workings around the collapsed area, borescope data at multiple locations around the collapse, S-Pillar analysis for average and unfavorable conditions, and numerical models. The emphasis has been placed on the impact of the following limestone pillar stability parameters: pillar width-to-height (W/H) ratio, benching, the existence of karst features, weak roof, and geological discontinuities. 3-D LiDAR scans were utilized to show the shape and the extent of karst cavities observed within stable pillars around the perimeter of the collapsed area. Together, this coupling of field measurements and observations and modeled systems suggests the potential causes of the collapse.

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 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 Juan L. Yarmuch, Hyam Rubinstein

Open-pit mine planning is a key process in the definition of mineral reserves and their efficient removal. Ore in open-pit mines is extracted bymining sequential pits called pushbacks. Pushbacks need to satisfy complex geometrical constraints to ensure the efficient usage of equipment. To date, no algorithms that both account for those constraints and maximizeNPV, are commercially available. Therefore, pushback design is done by engineers relying on their expertise to incorporate practical features intothe output of commercial tools. In this article, we present a new algorithm that produces close to optimal practical pushbacks and we illustrate its performance in real mines.

Jun 25, 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 Yashar Pourrahimian, Magreth S. Dotto

Fragmentation by blasting is a critical process in hard rock mining. The blasting process and outcomes are highly influenced by rock mass properties and their response to higher stresses and loading rates. Each rock mass is unique, but some features affect the blast energy similarly, leading to similar outcomes. Having analyzed these features and their influence on blast results, they can be incorporated into the blast design to achieve better outcomes. This paper presents a numerical model validated by a case study from the actual mine blasts that studied the effect of rock mass properties on explosive energy production, attenuations, and overall blast outcomes. The results show that when the model parameters are calibrated based on real data, they can be used to simulate the fragmentation process and therefore be used to aid blast design.

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 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 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 Joseph Githiria, Khadija Omar Said

Mineable material is defined using cut-off grade either as geological or economic cut-off grade. Both geological and economic cut-off grade affects the output generated in an open-pit mining operation. It distinguishes the amount of ore sent to the mill for processing and waste to the dump. This consequently affects the overall profits generated from the operation hence the need for cut-off grade optimization. There are several mining parameters that are considered in a typical cut-off grade model. They include economic, geological, and operational parameters. They have a significant effect to influence the cut-off grade selected in mining operations at any given period. Some of the parameters are stochastic in nature by having a random probability distribution that may not be predicted precisely. When selecting cut-off grades in a mine operation, the mining engineers must consider the dynamic nature of the operational parameters in order to maximize the net present value of the project. Modifying Lane’s cut-off grade model which was developed in 1964 to incorporate the stochastic parameters in the approach helps improve the net present value as shown in the paper. The modified Lane’s approach is developed into a stochastic computer-aided application code-named NPVMining. Using NPVMining, selected stochastic parameters are considered in this exercise to show the effect on the output (net present value). The selected parameters (price and grade-tonnage) are varied by ± 10%. NPVMining has a graphical user interface that makes it easy to use and displays results in a user-friendly way. It gives the user the choice to manipulate the data depending on the study being undertaken or any other factor affecting the specific mine. This paper analyses the effect of each of the selected mining parameters on cut-off grade using NPVMining to show the significance of monitoring their variance.

Jun 25, 2023

By Silvia Misk, Cristina Araújo, João Felipe Costa, Roberto Rolo

Linear estimation methods are widely used for resource evaluation. Among the existing linear estimation methods, ordinary Kriging (OK) was accepted as the best estimator to determine the average ore grade of each block. However, OK is inadequate for modeling small blocks in relation to data spacing, resulting in over-smoothed models. In this study, the results of the recoverable resource from the short-term model are compared to estimated models generated by the traditional method, i.e., OK, and non-linear methods. The conclusion is that non-linear methods improve the accuracy and precision of the long-term model when compared to the model generated by OK.

Jun 25, 2023

By Sebastian Avalos, Julian M. Ortiz

The long-term mine scheduling is the optimal decision on how to extract and process the mining reserves. The resulting plan is mainly driven by economic, metallurgical, and geological factors. Accounting for their uncertainties leads to a robust solution and the feasibility for economic-risk quantification. In this work, we model the spatial uncertainty of grade distributions of multiple correlated elements, by using a multivariate morphing transformation. The result is a set of multivariate geostatistical realizations of the grades, which honor the conditioning data, the direct and cross variograms, and the multivariate statistical distribution, inferred from a set of samples. These models are the input in a reinforcement learning framework, adopted to train a deep neural network that considers the grade uncertainty and leads to an optimum mine plan after training using deep Q-Learning. Mining and processing capacities, as well as economic parameters and metallurgical constraints, are incorporated as the reinforcement learning environment rules. The method is validated on a real deposit, providing insights for future applications.

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 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 Theophilus Mensah, Kwame Awuah-Offei

Engineers face significant challenges when determining what geometry provides the most profitable and safe stope for extraction. Several techniques and optimization algorithms have been developed in recent years, but most fail to find optimal solutions because they are heuristic or LP-based without efficient geometric constraints. This paper presents a two-dimensional binary linear programming (BLP) model for determining the optimal combination of blocks in a stope that maximizes the economic value of the layout of stopes for a sublevel deposit. The work draws from Queyranne and Wolsey’s (2017 & 2018) formulations of tight constraints for bounded up/down times in production planning problems to formulate novel and efficient geometric constraints along with geotechnical and grade constraints for the BLP stope layout optimization problem. Results from the model indicate that it is possible to formulate efficient shape constraints in LP-based approaches. The model used for the numerical example contained 144 valuable blocks out of 774 blocks, which translates into an economic value of $53.21M. The BLP model selected 60 valuable blocks and 13 waste blocks that met all constraints translating into a maximum economic value of $34.4M in 1.83 hours within a gap tolerance of 0.00%.

Jun 25, 2023

By Nasib Al Habib, Hooman Askari-Nasab, EUGENE BEN-AWUAH

In-pit crushing and conveying (IPCC) is getting popular in deeper open pit mines as a suitable alternative to truck haulage because it offers a lower operating cost due to shorter haulage distance and less truck requirement. Semi-mobile in-pit crusher, currently the most popular IPCC system, is relocated every two to five years, and the short-term plan needs to be updated accordingly. To the best of our knowledge, short-term planning with IPCC is an area of research that has yet to be explored extensively, and hardly any model can generate a shortterm extraction sequence considering an IPCC. This research work proposes a mixed integer programming model to develop a short-term production plan within a time horizon of 12 months. The model's objective is to optimally allocate shovels to minimize the cost of material handling and maximize revenue subject to meeting plant requirements, maximum allowable tonnage variation and IPCC location constraints to achieve production and NPV targets set by the strategic plan. The proposed model will be implemented in an iron ore mine case study for verification. The model is developed and solved using MATLAB. The comparison of results between scenarios with and without IPCC justifies the use of IPCC in large open pit mines from a short-term perspective.

Jun 25, 2023

By Jorge Amaya, Nelson Morales Varela, Francisco Saavedra, Gonzalo Nelis, René Gómez

Precedence constraints are broadly used in mathematical models and algorithms for mine planning to model, for example, slope angles in open pit operations or connectivity in underground mines. Unfortunately, these constraints do not capture relevant geometrical aspects for the design of the mine like minimum bottom space of pits or smoothness of the economic envelope in block caves. In this work, we present different approaches that utilize precedence constraints generating economic envelops that are more suitable for design purposes. The numerical experiences show that the proposed techniques produce high value envelops with better geometrical properties and that the methods proposed can be computed efficiently.

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 Kadri Dağdelen, Matthew Deutsch, Thys Johnson

Nested pit analysis in open-pit long range mine planning is used to define approximate ultimate pit limits and an initial high-level schedule in the early stages of a project. Modern ultimate pit optimizers can solve for dozens, or even hundreds, of nested pits however often a smaller subset of pits which are evenly spaced with respect to contained tonnage are desired for downstream analysis. In situations where that pushback selection process must be automated, such as when performing sensitivity analysis, or when the best subset of pushbacks is required; an optimization procedure is warranted. This paper describes a dynamic programming algorithm for selecting pushbacks following nested pit analysis, which is performant and addresses a long-standing inconvenience faced by long range open-pit mine planners.

Jun 25, 2023