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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 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

By Liam Findlay

Optimizing a production schedule with semi-mobile in-pit crushing & conveyance (IPCC) requires integrating extraction sequence, destination policy, crusher relocation, conveyor layout, and truck fleet investment decisions. An integrated stochastic optimization framework is proposed to produce long-term schedules for mining complexes using multiple semi-mobile IPCC systems. The optimizer selects the crusher locations and designs the conveyor routes from anywhere in the mines. The stochastic framework considers simulated multi-element orebody realizations and manages associated risk. A hybrid metaheuristic based on simulated annealing and evolutionary algorithm is implemented for the optimization. The method is demonstrated using an iron ore mining complex.

Jun 25, 2023

By Enrique Jelvez, Pierre Nancel-Penard

The traditional way of generating open-pit mine production schedules has several limitations. This work addresses the design and production-scheduling problem by considering minimum operational space requirements under a Direct Block Scheduling approach to generate extraction sequences that satisfy the operational space required. For this, traditional formulations were extended by including a new set of constraints that generate operating spaces for mining equipment to move and extract material. Then, a heuristic algorithm produces close to optimal practical pushbacks. This proposal was applied in a case study, and the results show its ability to generate practical production schedules without sacrificing expected return, obtaining differences in terms of Net Present Value (NPV) lower than 1% when compared to the traditional approach.

Jun 25, 2023

By Biswajit Samanta, Rahul Kumar, Pranjal Pathak

The Open Pit Mine Production Scheduling Problem (OPMPSP) is a complex optimization problem that seeks to determine the best sequence for extracting mineralized material from the ground in order to maximize the Net Present Value (NPV). This process must take into account various capacity constraints, such as mining rates, processing limits, stockpiling capacities, blending requirements, and precedence constraints, which dictate the order in which blocks can be extracted. Traditionally, the OPMPSP has been formulated as a mixed integer programming problem, employing binary and continuous variables to represent the extraction of each block. However, this approach has limitations, particularly when it comes to addressing the stockpiling issue. In response, some researchers have proposed mathematical models, which offer a more accurate representation of the problem but come with their own set of challenges. In this study, an alternative approach to tackle the OPMPSP with stockpiling by employing constraint programming (CP) is explored. The method was carefully tested on the Newman1 dataset from the well-known Minelib library. The results show that the CP-based model obtained a production schedule having about 2.69 % higher NPV than the NPV of the solution, which is recorded in the Minelib Library.

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 Rodrigo Peroni, Vitor Campos

Carajás is one of the largest open pit mines in the world. The mine is comprised by a haul road network more than 100 km long, where the need for good roads is paramount and determining of the truck performances. This work integrates, regarding the ultimate pit design, an approach to consider an economic value to an originally unfit material, assuming it has the appropriate characteristics for haul road construction, which sometimes lies over economic iron ore. This measure demonstrated an improvement in the utilization of resources and guarantee the necessary material to the haul road design and Mining Pads maintenance.

Jun 25, 2023

By Erik Marsh, Frederick C. Harris Jr., Javad Sattarvand, Joshua Dahl, Alireza Kamran Pishhesari

Based on the mine safety and health administration (MSHA)reports, mining truck accidents are among the significant causes of fatalities in the mining industry. The major causes of haulage accidents can be divided into non-driving and driving factors, such as truck-related sudden failures and operator performance. Numerous parameters affect trucks' safe operation, but situational awareness plays a significant role. Due to the distinct structure of mining trucks, making them efficient for mining purposes, the operator has a very limited field of view (FOV), making them prone to accidents resulting in losing lives. The cabin's position blocks the truck's right and rear sides, creating several blind points around it. Aiming to test/train the operator's awareness of the surrounding, a proximity detection system is developed through a multiplayer virtual reality (VR) application to investigate the human factor in equipment collisions. The operators were examined and monitored when asked to conduct what exactly happened in the Marigold mine truck accident without being aware of the scenario. After receiving sound, vibration, and visual alarm warnings, their reaction and performance were analyzed. The platform is developed using the Unity game engine for HTC VIVE virtual reality goggles. An Omni-direction treadmill and a motion simulator platform are utilized for the player and dump truck operators. Fifteen students were monitored when driving the dump truck receiving/not receiving proximity detection alarms. None of them repeated the accident when receiving proximity alarms. Also, they found the visual alarm the most comfortable while driving the truck.

Jun 25, 2023

By Pratama Mochamad Rizki, Andika Satya

High-risk area in open-pit mining should always be monitored by the operational supervisor because of its nature. One of this type is the soft soil dump area. Due to the unstable bearing capacity of the soil, it’s highly possible for us to find cracks in the area. This paper aims to build computer vision which able to detect truck’s distance from safe limit allowed during dumping activity. The system will warn the truck’s driver if the truck has crossed that predetermined safe limit.

Jun 25, 2023

By Carlos Fonseca, Gustavo Usero, Roberto Mentzingen Rolo, Gabriel Moreira, Octavio Rosa de Almeida Guimarães

Geological domain modeling is an important step in mineral resources evaluation. The procedure can be laborious and time-consuming, especially in multivariate settings. However, estimates are significantly improved when carefully limited by geological variables. This paper proposes a workflow for geological domain modeling suited for stratigraphic deposits. The workflow consists in automatically defining the domains by using clustering algorithms, generating the surfaces that represent the contacts by interpolation and subsequent triangulation, and simulating the surfaces. The proposed workflow produces consistent and realistic results and allows the user to test different domain configurations and assess volumetric uncertainty automatically and quickly.

Jun 25, 2023

By Rodrigo Peroni, Euler Victoria

Knowing the depth of contacts between weathering zones in phosphate mines is determinant to define the stripping ratios and the mineralized zones. Contacts are gradational and difficult to define even in the drillhole samples, so the combination with geostatistical simulation techniques helps to define the contact zone and the grade distribution, providing a way to assess the probability of the occurrence and the positioning of contacts between the barren waste and the ore zones. These inputs are used in the scheduling, anticipating risks associated with mine planning scenarios. The results from an application to a case study showed a good improvement when compared to traditional techniques.

Jun 25, 2023

By Clinton Birch

The multiple-criteria decision analysis is a technique that helps to find an optimal solution by evaluating conflicting criteria. In this study, an approach to perform multiple-criteria decision analysis for cut-off grade optimization, which considers the diverse stakeholder groups prevalent in the mining industry, is presented. The optimization model developed in this approach links the resource block model to the financial model, enabling the process to either maximize profit, net present value, revenue to the State, or extend the mine's life. To identify the optimal cut-off grade in each case, the Excel Solver function is utilized. Although it has limitations, the Solver function can still be modified to handle models with thousands of block values for determining input variables based on the target value. Following stakeholder engagement and dialogue, a single weighted average cut-off grade can then be determined. This approach is demonstrated for nine South African Witwatersrand gold mines.

Jun 25, 2023