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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 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 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

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 Mauricio Brücher, Juan Camus

This article deals with open pit mine planning and a computer technology known as Direct Block Scheduling, or “DBS”. This is an emerging technology for modeling mine exploitation strategies using mixed-integer linear programming. This technique has proved to yield stronger economic results compared to the Lerchs-Grossmann (“LG”) algorithm currently used at the beginning of the mine planning process. The article briefly describes the shortcomings of current practice; then, this delves into the superior results obtained by DBS technology compared to current practice. Finally, the article argues that results achieve so far by DBS could be enhanced even further. To do this, the framework to apply this emerging technology, which is also used in current practice, should be modified to better exploit the capabilities of DBS technology. To better understand this change, an illustrated case study is presented at the end of the article.

Jun 25, 2023

By Jie Li, Longxiang Li, Wenbing Guo, Gaobo Zhao, Yuhang Hu

Surface subsidence caused by underground longwall mining is a very complicated process in time and space, influencing building and structures on the surface of the mining area, including houses, pipelines, and highway. The field monitoring data of surface subsidence are essential for protecting them. This study investigated the dynamic and residual subsidence characteristics of both the surface and the highway over a 730-day monitoring period on a longwall panel. Three monitoring lines (Lines 1–3) were established to monitor the subsidence process, and three stages were identified: initial stage of surface subsidence, active period of surface subsidence, and residual subsidence deformation stage. The maximum subsidence value on the highway (1,786 mm) was smaller than that on the surface (2,094 mm), and the maximum subsidence velocity on the highway (41.7–50.8 mm/day) was also lower than that on the surface (66.7 mm/day). During the residual subsidence deformation stage, both the surface and highway subsidence velocities decreased to below 0.6 mm/day. Furthermore, differences in subsidence characteristics between the surface and the highway were analyzed, with the subsidence value on the highway always being less than that on the surface, while the subsidence velocity during the residual deformation stage was not always lower. This could be attributed to the influence of dynamic loads from vehicles, which could induce additional residual subsidence on the highway. Further investigation into this phenomenon will be conducted in future research.

Jul 1, 2023

By Milka Madahana, John Ekoru

For efficient management of operations, mining companies are required to be able to predict potential scenarios for main market factors. Competitive advantage and value creation are established through managing the value chain well. The mining value chain is usually faced with challenges from various shifts in commodity markets as well as ongoing uncertainties. Therefore, there is a need for accurate analysis of data from the markets, for the prediction of commodities, for instance, Precious Group Metals (PGM). The main objective of this research work is to apply machine learning methodologies with the aim to identify patterns and trends in the Precious Group Metals, and thereafter analyze and predict the future trends of these markets. Numerous machine learning models are applied to a dataset consisting of 15 years of daily price for eight precious metals namely Platinum, Palladium, Rhodium, Ruthenium, Osmium, Indium, Silver, and Gold. The results reveal the patterns and trends within the data sets and that different machine learning methods could be suited for the prediction of market prices for each of the PGMs. The application of an amalgamation of various machine learning methods may result in more accurate predictions of the PGM markets. Accurate prediction of market prices for PGMs will possibly result in unlocking untapped sources of value in the mining industry.

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

By Robert Rennie, Taylor Davies, Ahsanul Rafi

Electric locomotives powered by extensive electrical cabling systems were prevalent in the mining industry in the early days of the 20th Century [1]. Since the movement of tethered electrical mining locomotives was limited, the advent of diesel-powered mobile mining equipment provided more flexibility. This led the mining industry to be dominated by diesel-powered locomotives for most of the 20th Century and the entire 21st Century, to date. However, diesel power has certain drawbacks. Diesel-powered vehicles produce tailpipe emissions such as diesel particulate matter, carbon dioxide, sulfur dioxide, and nitrogen oxide. Mine workers exposed to these emissions were, and continue to be, subject to health issues such as respiratory problems, increased risk of lung cancer, and cardiovascular disease. [2][3]. Extensive ventilation systems are required to dilute the diesel exhaust and dissipate the heat generated by these vehicles. Moreover, as mines are getting deeper due to depleting resources, ventilation costs in diesel-powered mines are becoming increasingly high. These factors, coupled with the trend towards greenhouse gas (GHG) reduction in mine operations, has allowed zero-emission electric locomotive systems to gain traction. As lithium-ion battery technology is becoming more advanced, battery-powered electric vehicles can now perform duty cycles comparable to those of diesel-powered vehicles. Although battery-electric vehicles (BEVs) are comparable to diesel-powered vehicles in terms of mobility, they have certain limitations. For instance, the amount of onboard energy is limited by the battery energy density and specific energy. The MT42 diesel haul truck from Epiroc, for example, has a fuel tank capacity of 580L, which is equivalent to around 1450 kWh of net electrical energy [4]. The electrified version of the MT42, on the other hand, has an onboard battery with 375 kWh of usable energy weighing around 4.9 metric tonnes [5]. Moreover, storing and refueling of diesel-powered vehicles is simple and quick, whereas battery energy storage and charging systems require extensive infrastructure development. Charging time is mostly limited by battery chemistry and design. Due to these limitations, the benefits of battery-powered mining vehicles are more likely to be realized with proper planning and optimization of the BEV fleet. This includes optimizing mining layouts for BEV use, charging infrastructure design, and management of onboard energy storage systems.

Jun 25, 2023

By Miguel Carrera, Jose Arnal, Julian Ortiz, Maarten Haest, Fouad Faraj

ShovelSense is a complete solution for bulk ore sorting at the mine face that combines robust shovel bucket mounted X-ray fluorescence sensors, with real-time material classification, fleet management integration and supporting cloud-based data infrastructure, to ensure each truck always goes to the right destination. Correct truck destinations are a challenge in many current mining settings because the material classification of a truck, which is the key input into the truck destination, is often based on estimates at block resolution (~20x20x15m), with limited adjustment for blast movement and mixing. In this contribution, we present a geostatistical, data driven approach to estimate the potential value of bulk ore sorting at the mine-face for seven open pit mines. The approach is premised on the assumption that truck loads within blocks follow a simple gamma distribution. Standard ore control data from the seven mines have been used to estimate the potential bulk sorting value at these operations and ShovelSense-integrated dispatch datasets have been used to validate these estimates for specific benches on which ShovelSense operated. ShovelSense results validate the ore control data-based value predictions and show how at mine-face bulk ore sorting value varies between distinct deposit types. In general, deposit types that have higher bulk ore grade contrasts, display more potential ShovelSense value. The manuscript concludes with the limitations and opportunities of applying the proposed value estimation approach to evaluate at mine-face bulk ore sorting opportunities for open pit operations.

Jun 25, 2023

By Javad Sattarvand, Milad Ghahramanieisalou

As new technologies are introduced to the mining industry, the challenges related to their safe assimilation and the potential changes they bring to mining techniques necessitate adjustments to the current operations. Predictive simulations are critical to understanding unforeseen scenarios and shifting costly changes from the operational stage to the design. The Digital Twin methodology has gained a lot of attention in recent years. Many industries, from manufacturing to engineering and even social sciences, are adopting this approach to understand better the complex systems they are working with. By considering the enormous expenses and risks related to a pilot project, DT could replace that by acting as a prototype in which realistic tests and simulations are carried out with close to zero cost. DT includes a data hub, simulation and analysis tools, and visualization platforms to enable appropriate designs and monitoring plans focusing on unknown areas. A few examples of DT's application in mining include fleet management, Mine-to-Mill optimization (especially D&B), geotechnical digital twin (gDT), etc. When developing regulations, testing the efficiency of the mine layout, design, and magnitude of the restricted area and its access points, human-machine interactions, risk identification, and management processes, examining the efficacy of control systems, and monitoring, DT could help speed up (or completely replace) the process in the physical world.

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 Kadri Dagdelen, Lee J. Zamalloa

Modern surface mining operations are always seeking for minimizing costs while increasing production rates, among the optimization of other specific tasks that have an impact on the economics of the mine. Specifically, the cost related to the production fleet operation (shovels and trucks), the maximization of the overall performance of the equipment, and the uncertainty to meet the goals at the end of the operational time horizon are always topics of discussion with no apparent simple answer. On the other hand, truck-shovel dispatch systems based on Linear/Non-Linear Programming Algorithms have been in operation since mid-1990’s with different levels of success depending on the correct arrangement of operational resources and the complexity of the mine, but having in most cases some important shortcomings related to the way these systems deal with the decision making of the operational objectives, being necessary to have a human agent (dispatcher) to guide the system when the operational parameters change, creating the possibility of suboptimal decisions due to the human factor. This paper discusses the implementation of a novel methodology based on AI techniques to assist in the decision making of the best goal selection when operating a Fleet Management System, taking into consideration time periods as variables and integrating them as part of the analysis, providing a better overview of the forecast plan completion with respect to previous production numbers and evaluating best alternative to handle future events to minimize losses or maximize profits, resulting in better truck assignments, minimum shovel idle times and overall productivity improvements of the operation at the end of the time horizon.

Jun 25, 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 V. C. Koppe, J. F. C. L. Costa, D. M. Marques, A. Amaral

Errors associated with estimating the grade of an attribute of interest in a deposit can compromise planning, sequencing, and mineral beneficiation decisions. Several factors are associated with estimation errors. Among them, we can highlight the factors associated with sample spacing, the size of production volumes, and the variability of the attribute considered in the estimation. The access to estimation error can be obtained by some geostatistical methods, for example, by stochastic simulation. However, this is a process that in some situations is computer intensive and takes time. Previous works have proposed a simplified method to access the Maximum Estimation Error (MEE) associated with different types of grade variables, depending on the sample spacing, at small production volumes and considering specific coefficients of variation ranges. This paper presents an approach to assess the MEE associated with long-term volumes. Considering the methodology proposed, it is possible to approximate if a specific drilling pattern used in a deposit provides an acceptable pre-determined estimation error for Resource Classification. Results and considerations on the impact of the geometry of larger production volumes on the MEE calculation are also presented.

Jun 25, 2023

By Angelina Anani, Nathalie Risso, Carlos Olmos de Aguilera

The traditional approach to safety clearance monitoring and proper PPE usage in underground mines is time-consuming, costly, and susceptible to negligence. However, automated real-time monitoring can be implemented using computer vision-based approaches. This study aims to develop a system that provides timely information based on the automatic detection of proper PPE use and safety clearance in underground mines. Deep learning and a decentralized computing structure between the cloud and sensing devices (fog technology) are used. We hypothesize that a camera-based system can provide near real-time assessment of proper PPE use and verified clearance to access restricted areas in underground mines.

Jun 25, 2023