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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 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 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 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 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

By George Biro, Christoph Mueller

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

Jun 25, 2023

By João F. C. L. Costa, Vanessa C. Koppe, Clayton V. Deutsch, Marcel A. A. Bassani, Pedro H. A. Campos

Blending inevitably occurs to some degree during mining and mineral processing operations. Traditional mine scheduling assumes that all variables average linearly, which allows the calculation of the blended material properties as a linear weighted average. However, metallurgical properties such as metal recovery and Bond Work Index (BWI) are known to average nonlinearly. The nonlinear properties of the blended material can be calculated using nonlinear blending models. A simulated annealing algorithm is developed and applied to a short‐term mining face optimization problem to maximize the total metal recovered considering nonlinear blending. Compared to a standard schedule plan, the optimized one shows relevant improvements in synergistic and antagonistic blending behaviors.

Jun 25, 2023

By Konstantinos Komnitsas, Evangelos Petrakis, Emmanouil Varouchakis

During the modeling of grinding systems, population balance modeling (PBM) which considers a constant breakage rate has been widely used over the past years. However, in some cases, PBM exhibited some limitations, and time-dependent approaches have been developed. Recently, a non-linear framework which considers the traditional linear theory of the PBM as a partial case was introduced, thus allowing the estimation of product particle size distribution in relation to grinding time or the specific energy input to the mill. In the proposed model the simplified form of the fundamental batch grinding equation was transformed into the well-known Rosin–Rammler (RR) distribution. Besides, the adaptability and reliability of the prediction model are among others dependent upon the operating conditions of the mill and the adjustment of the RR distribution to the experimental data. In this study, a series of grinding tests were performed using marble as test material, and the adaptability of the non-linear model was investigated using three loads of single size media, i.e., 40, 25.4, and 12.7 mm. The results indicate that the proposed model enables a more accurate analysis of grinding, compared to PBM, for different operating conditions.

Jun 24, 2023

By Mustafa Fener, Masoud Rostami, Sair Kahraman, Behnaz Dibavar

Roadheaders are commonly used in coal mines for roadway excavations. The prediction of roadheader performance is necessary for the planning of roadway projects and the estimation of costs. Recently, a new rock mass cuttability classification (RMCC) system has been introduced for roadheaders used in coal mines. In this study, the validity of the RMCC system was investigated for different types of roadheaders and different coal mines. Four different coal mines were visited, and the performance measurements of different axial and transverse roadheaders were carried out during the excavation of roadways. Uniaxial compressive strength, volumetric joint count, the strike and dip of joints, joint aperture, the Cerchar abrasivity index, and water ingress were determined in field and laboratory studies to calculate the RMCC index. The analysis of the data demonstrated that the correlation coefficients between the net cutting rates and the RMCC indices were higher than 0.94, suggesting that the RMCC system was valid. It was concluded that the RMCC system could be reliably used for the evaluation of roadheader performance in coal mines.

Jun 21, 2023

By Jorge Armstrong, Sean Warren, JOSEF BOURGEOIS

The application of statistical analysis software programs has proven useful for the investigation of rockfall runout distance along a designed slope. Rockfall modeling programs are continually being upgraded with more sophisticated analysis tools, such as the use of the rigid body versus lump mass models. Engineers at mine sites utilizing the software may have varied experience related to these models, their associated input parameters, and how to interpret the generated results. To address this concern, researchers at the Spokane Mining Research Division (SMRD) of the US National Institute for Occupational Safety and Health (NIOSH) investigated the influence of slope height, slope angle, slope material, and rock size for both rigid body and lump mass models in a 2-D statistical analysis program. Based on a literature search and industry input, specific ranges common to that of an open pit mining environment were chosen for each of the input parameters to determine 90% rock runout distance as well as their sensitivity to change. Data collected from this numerical analysis and simulation will be compared to empirical rockfall data gathered through the duration of the Highwall Safety project conducted by NIOSH from 2022–2026.

Jun 18, 2023

By A. Dell’Era, E. Ciro, C. Lupi

The indium recovery via electrowinning from sulfate baths has recently gained significant attention as it does not present toxic emissions, hermetically sealed system requirements, human health, and environmental hazards. Following previous works related to the optimization process for AISI 316L and Ni cathode, it was observed significant importance of the metal supports on the input and output parameters of the indium electrowinning process from sulfate solutions. Thus, comparing input and output parameters in both cases seems very interesting. Particularly, regarding the input parameters, the attention has been focused on the electrolyte composition, current density, and temperature, while productivity, morphology, and structure have been considered regarding the output parameters. Considering the productivity as the optimal output, the findings for Ni cathode were better than that for AISI 316L in most of the selected conditions studied. In the case of morphology, indium grains appear rounded and dendritic on the AISI 316L, while those on the Ni cathode show stratified lamellar grains. Anyway, the indium deposit obtained on Ni cathode shows bigger grains independently from the used operative conditions with respect to that on AISI 316L. Regarding the structure, it is clearly tetragonal, but preferential orientation, crystallinity, and deformation of the structure strongly depend on the metal support.

Jun 14, 2023

By Rainer Antretter

The city of Garmisch-Partenkirchen in southern Germany is located at a main transit road to Tyrol in Austria and suffers of a very heavy traffic load. Therefore, the Kramertunnel as a by-pass tunnel is built to release/mitigate the traffic through the city centre. The 3,600 m long single tube road tunnel is designed with a parallel but smaller emergency tunnel and with cross passages, fitted with single- and doublesided emergency bays, a vent shaft, and an intermediate ceiling to separate fresh air supply from the road traffic. The tunnel, concrete lined along its entire length provided the challenge to find smart formwork solutions for quick set-up, repositioning, setup and pouring. Three particularly specific sections are discussed in this paper.

Jun 13, 2023