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By John J. Simmons

There is considerable interest in the iron and steel industry in the development of technologies that can reduce carbon dioxide emissions, as the iron and steel industry is the third largest producer of carbon emissions in the world, after automobiles and the power generation industry. The E-Iron Iron Nugget Making Process, invented by Dr. Komar Kawatra and Dr. Tim Eisele, provided a major breakthrough in the iron making process as the process uses biomass in the form of hard wood or soft wood as the reductant in the place of coal. Dr. Kawatra recognized that biomass is rich in both carbon monoxide and hydrogen, two important reducing agents. This resulted in initial experiments conducted by Dr. Kawatra and Dr. Eisele in the laboratories of the Chemical Engineering Department of Michigan Technological University (Michigan Tech) that prepared self-reducing pellets with wheat flour as the reductant and limestone/dolomite as the flux. Initial results were encouraging so they extended their work to include hardwood, which is in huge supply in the forests of Michigan and Minnesota, as the reductant. Dr. Kawatra has also applied the E-Iron Technology to other iron bearing minerals to produce pig iron grade iron nuggets. He used the E-Iron Process to convert red mud, a high iron waste product of the aluminum industry, into pig iron nuggets with the rare earth elements reporting to the slag. This work resulted in a patent with Dr. Kawatra and Dr. Michael Archambeau named as the inventors. Dr. Kawatra then extracted the rare earth elements as a separate high value bi-product using oxalic acid, which he produced from carbon dioxide. Dr. Kawatra also conducted a test program that produced pig iron nuggets from New Zealand beach sands. The vanadium in the beach sands was retained in the iron nuggets, recoverable as a valuable bi- product in a separate process. The titanium dioxide in the beach sands reported to the slag. This demonstrates the potential use of the E-Iron Process to convert the large ilmenite reserves in Northern Minnesota into pig iron and titanium dioxide. Another use of the E-Iron Process discovered by Dr. Kawatra was its ability to produce iron nuggets from manganiferous ores. Dr. Kawatra was able to direct the manganese into either the metal or slag by adjusting process temperature. The above discoveries are included in several patents that were assigned to Carbontec Energy. This presentation will only address the use of the E-Iron Process to produce pig iron nuggets from steel mill waste and un-pelletized, mid-grade iron ore. Carbontec Energy Corporation agreed to sponsor additional research directed at improving this new technology that would later be trade named, The E-Iron Process. Carbontec Energy conducted further work that proved that the E-Iron technology could also convert steel mill waste into metallurgically acceptable iron nuggets. Carbontec also collaborated with the Lawrence Livermore National Laboratory and Purdue University Northwest to conduct a Department of Energy (DOE) sponsored program that would use their high performance computer capability to both verify and model the E-Iron Process. Purdue University Northwest reported that the E-Iron Process can produce pig iron nuggets with 85% less carbon emissions than a blast furnace. The E-Iron Process is similar to the ITmK3 process, developed by Kobe Steel, that also produces iron nuggets but uses coal as the reductant.

Feb 1, 2025

By Devin Burns, Saima Ghazal, Michael Owusu-Tweneboah, Kwame Awuah-Offei

Trust in AI systems and the nature and amount of information about the system can influence decision-making. It is, therefore, crucial to understand how AI-powered monitoring systems (e.g., gas monitoring systems) influence miners’ trust and decision-making in emergency evacuation. This work focused on measuring the differences between how participants respond to a simulated underground mine emergency evacuation situation when warnings are represented as coming from a human or AI-based monitoring system. We also manipulated the amount of information participants received about the system, yielding a 2×2 between-participants survey design. The participants received an alert message about rising gas levels in a mine and provided a response on how they would react and reported their perceived safety. We also asked questions to assess their trust in, preference for AI over human-based gas monitoring systems, and whether they are willing to delegate the duties of underground gas monitoring to AI systems. The experiment results show the amount of information had a significant influence on miners’ trust and decision-making. The safety perception of participants based on age, number of children and ethnicity was significantly different from one category to another. This work provides valuable insights as the mining industry deploys AI systems to aid mine safety.

Feb 1, 2025

By Monica Campos, Jose Mujica, Maria Campos

The extraction and processing of metals and other minerals have played an important role in economic, political, and social development worldwide, from the early stages of prehistory to the present day (Gosner, 2020). In other words, mining has been a fundamental part of human civilization, providing the raw materials necessary for technological, infrastructure, and industrial advances. As society progresses, the importance of mining continues to grow, supporting the development of everything from the smallest electronic devices to the tallest skyscrapers (The Importance Of Mining In Modern Society, 2024). The mining industry is closely linked to a country’s economy and activities related to natural resources. The economy of a nation is connected to its extractive industries (Moreira & McMahon, 2014), which have seen dramatic growth in recent years (Pietrobelli et al., 2018). In a context of increasing demand for natural resources, driven by the development of emerging technologies, the expansion of infrastructure, and the foundation of engineering achievements for the coming decades (Importance Of Mining - Minerals Education Coalition, 2024), mining should focus efforts on global development, requiring a comprehensive approach to sustainability that addresses the planet’s needs and protects the well-being, primarily, of the communities and environments where mining operations take place (Figari, 2024). In these areas, diversity and inclusion should be promoted, thus fostering equitable and sustainable development. Speaking of sustainable development, diversity, and inclusion brings us to an interesting topic: the Sustainable Development Goals (SDGs), also known as the Global Goals, which could serve as a key framework for fostering a strong social management culture in mining. The SDGs were adopted by the United Nations in 2015 as a universal call to end poverty, protect the planet, and ensure that all people enjoy peace and prosperity by 2030 (Sustainable Development Goals, n.d.). Companies have compelling reasons to align their actions with the SDGs, as their long-term success depends on healthy communities. This is a major challenge and requires collaborative efforts (Singh & Rahman, 2021). As Fraser (2021) mentions, achieving more sustainable outcomes may involve mining companies and communities working together collaboratively. This research proposes an innovative approach: Inclusive Mining for Sustainable Development, which aims not only to maximize economic profitability but also to promote social and environmental well-being by integrating the United Nations’ Sustainable Development Goals (SDGs), primarily focused on diversity and inclusion. This can be achieved through the implementation of a key value-added initiative for mining companies: the Creation and operation of a corporate volunteering program aimed at developing and executing inclusive practices to promote gender equality, the inclusion of local communities and indigenous peoples, and accessibility for people with disabilities. This proposal will educate employees and stakeholders about sustainability and promote concrete actions through corporate volunteering to strengthen the mining company’s commitment to community development, improve social relations, and advance toward achieving the United Nations’ Sustainable Development Goals, establishing the company as a global change agent.

Feb 1, 2025

By Boyd Eisenbraun, Dennis Murr, Milton Rojas

Approximately since 1925, the phenomena of grinding and classification of minerals with screens have been studied. In the U.S. iron ore mining operations located in the so-called “Mesabi Iron range,” magnetite processing plants have made progress in the application of high-frequency screens that have allowed them to process low-grade Taconite ores and successfully achieve the economic benefit of these minerals. In the particular case of magnetite minerals disseminated in the siliceous matrix, the main objective is to reduce the generation of particles called “middlings” with high silica contents. Commonly the primary challenge of the metallurgical task is to achieve high recoveries and iron concentrates with low impurities. To achieve this, several studies and industrial applications have shown success in this regard and not only in iron ores, but also in other minerals around the world. The study described in this paper attempts to describe some key aspects that helped develop an effective solution and list the benefits associated with this modern process design by using industry-proven technologies for minerals in Chile that have similar characteristics to the minerals of the iron belt in the United States.

Feb 1, 2025

By Duah Philip, Awuah-Offei Kwame

Regulatory pressure to mitigate the cement industry’s environmental impact has intensified recently, spurring innovation in low-carbon cement technologies. In the United States, cement production, consumption, and carbon emissions are accelerating. Despite this trend, the adoption of environmentally friendly dry clinker production technology with combined preheater-precalciner systems has surged from 53.3% in 2002 to 88.5% in 2019. Concurrently, reliance on wet technologies, known for their higher environmental impact, has dropped from 16.5% to 0.6% within the same period. This paper presents the results of our comprehensive study, utilizing over 100 years of industry data, to analyze these technological shifts and their environmental implications.

Feb 1, 2025

By Liming Yuan, Wei Tang, RICHARD THOMAS, DAVOOD BAHRAMI

The location and orientation of fire suppression nozzles is critical in suppressing mobile mine equipment fires. To effectively suppress such fires, optimization needs to be considered for the suppression nozzle locations and discharge angle. Detailed experiments were conducted to study the effectiveness of fire suppression agents with different nozzle configurations. A variety of fire suppression systems including dry chemical, wet chemical, dual agent (dry and wet chemical), carbon dioxide, and water mist were used. Six types of nozzle locations were studied, and their fire suppression effectiveness were compared. Results from this work highlight potential considerations when determining effective fire suppression designs.

Feb 1, 2025

By Sean Hunter

Artificial Intelligence has a long history but is currently coming strongly into focus with new advancements. This leads to questions many people are asking: what exactly is AI, how can it be used effectively in mining operations, and what foundation is required to make AI implementations successful? In mining, many AI applications exist, from drill pattern optimization to predictive maintenance. However, many implementations struggle due to data integration challenges. Mining operations generate large amounts of data from many sources, such as fleet management systems, drill sensors, process control systems, and laboratory results. Unfortunately, this data often exists in silos where it’s difficult to get it out, making AI applications unable to leverage it. Worse still, tools that use AI don’t always provide their data back to the system, creating even further data silos. All these problems make it hard to take advantage of the data at the mine, slowing down and reducing the effectiveness of AI. This paper presents a comprehensive approach to organizing mining data using an AI-ready knowledge graph system. We will first explore the evolution of AI in mining, from early expert systems to modern machine learning applications. Then, we will detail how a knowledge graph can integrate business context, operational data, and AI models into a cohesive system that enables more effective AI implementations across mining operations. The proposed method has been developed considering both the unique data challenges faced by the mining industry, while also considering best practices learned from the AI field at large. By following this approach, it’s possible to better utilize existing data, getting higher quality and more complete results faster, and in a way that’s reusable and composable.

Feb 1, 2025

By Philani Larrance Ngwenyama, Ronald CW Webber-Youngman

Through-the-Earth (TTE) communication systems were developed to provide post-accident communication in underground mines. Unlike primary tracking systems, they do not require any major infrastructure installations underground. Instead, they use the Earth’s overburden as the medium or channel for the transmission of electromagnetic waves between surface and underground. Under ideal conditions, TTE communication systems should be able to provide full-duplex, two-way voice, text and data communication. However, this will depend on the design and optimization of the different types of TTE communication systems (that is, antenna and electrodes), and understanding of the rock properties and their influence on the transmission of signals. This can help simulate and model the transmission efficiencies and predict the possible range and data rates. Being able to optimize the performances of these systems can enhance rescue missions, improving the chances of saving trapped miners, and ultimately reduce fatality rates across the mining industry.

Feb 1, 2025

By R. Chandramohan, Landon Jackson, C. Meinke

Understanding constraints within the raw battery material supply chain is essential for making informed decisions that will ensure the battery industry’s future success. The primary limiting factor for long-term mass production of batteries is mineral extraction constraints. These constraints are highlighted in a first fill analysis which showed significant risks if lithium-ion batteries are used to fully support vehicle electrification and intermittent energy storage. Nickel, lithium, cobalt and graphite reserves risk 100 percent depletion with significant consumption of known resources. Furthermore, more than 700 new critical mineral mines will need to be developed to meet the required production rates for decarbonization by 2050. Demand for critical minerals will outpace mine development timelines even as improvements are made to battery energy density and compositions. Governments and the private sector need to align themselves on decarbonization goals to establish cooperative agreements on the critical mineral supply chain by reducing the barriers to entry and increasing exploration efforts. Additional measures must also be taken to reduce the demand for critical minerals. Policy such as incentivizing public transportation and biking infrastructure can be exploited to drastically reduce the mineral demand placed on the mining industry.

Feb 1, 2025

By Hilal Soydan, H. Sebnem Düzgün, Jurgen Brune

In this study, with the primary goal of capturing ongoing digital transformation and automation impacts on the mining industry and its workforce, we conduct several interviews with mining industry experts in the United States and analyze our survey reports qualitatively and quantitatively through exploratory analysis. After the interpretation of the insights of industry experts, we proceed to generate a personalized and customized data analysis through a novel metric based on skills, knowledge, competencies and occupational requirements, which quantifies the job similarities for occupations in the mining industry based on the publicly available database of the U.S. Department of Labor. We use text analytics to tokenize and classify the interviews to capture a better understanding of major response categories. The temporal analysis shows that the critical competency needs in the data science and autonomy category increases from 28 percent in current demands to 43 percent. In defining our metric, we also calculate Kullback–Leibler (KL) divergence for each job profile that enables determining whether and to what extent that job is transitionary in our test set based on the mean, standard deviation and kurtosis of each job of interest. Our analysis reveals that the in-group job transitions are significantly easier than the between-group transitions, proving our initial assumptions and common sense. The generated heat maps provide the opportunity to present the gap between the current job and desired job profiles that provide feasible career change options, among others, offering individualized career paths for job seekers and promoting potential job transitions. Through the collection of industry-specific individual employee data, the AI system is envisaged to continue to learn as end users engage with the system, thus creating a central data hub specifically for the future workforce in the mining industry. Although the study has limitations on generalizability for qualitative assessments, it presents itself as a valuable application of how qualitative and quantitative approaches could be of value for future worker training in the mining sector.

Feb 1, 2025

By Isabel F. Barton, Eytan Bos Orent, Mark D. Barton

A new examination of tabular uranium (U)-vanadium (V) deposits within the La Sal district places mineralization in the context of the complex fluid flow history that has occurred in the Paradox Basin and Colorado Plateau. As the only currently producing district of this deposit type in the region, the La Sal district provides excellent opportunities to study deposit- and district-scale features that can serve as exploration guides where exposure is limited. The absence of major structures along a narrow and elongate mineral trend that runs E-W across the southern part of the Utah-Colorado border indicates a regional lithologic control. The trend was likely influenced by Jurassic drainage patterns, as indicated by paleocurrent directions shown in Fig. 1. Fluvial sandstone host rocks in the Jurassic Morrison Formation contain significant evidence of multiple superimposed fluid systems that have progressively changed local mineralogy. Apart from the presence of ore minerals, remnants of fluid systems are preserved as cements including quartz overgrowths, compositionally distinct carbonates, and sheet silicates. New geochronology methods provide constraints on the timing of initial mineralization and later remobilization. U-Pb ages of calcite associated with ore minerals provide evidence for Early Cretaceous mineralization, whereas textures and K-Ar ages show that early-formed V-minerals were replaced at the expense of V-bearing micas and clays during the Eocene. The paragenesis of minerals in U(-V) deposits of the La Sal district reflects the complexity of a sedimentary

Feb 1, 2025

By Amy McBrayer, Vladislav Kecojevic, Juan David Valencia Quiceno, Dragan Bogunovic

The mining industry’s commitment to innovation reflects a history of adopting technological advancements to enhance workplace safety, environmental sustainability and vocational training. The objective of this research was to develop an augmented- reality (AR) system for heavy-equipment operators (HEOs) in surface mining. The developed system has the potential to enhance mine safety, training and data-driven decision- making, which presents a significant step toward a more sustainable, effective and technologically driven mining training, contributing to the industry’s evolution and growth. The AR training system leverages Microsoft’s Power Platform and HoloLens 2 capacities to provide operators with detailed and immersive training guides for three mining equipment, including bulldozers, motor graders and haul trucks. AR guides combine speech controls, 3D objects, informative images and explicative videos in the real working conditions to enhance the operator’s learning process. The system has the potential to modify the training structure based on user performance and provides an efficient approach to data collection during training. This project was developed and applied through a case study in a surface mine in the Southern United States.

Feb 1, 2025

By Tim Lundquist

Disa Technologies Inc. provides innovative, sustainable solutions for mineral processing and remediation through its patented High-Pressure Slurry Ablation (HPSA) technology. HPSA processes solid feed material in slurry form, propelled by high pressure pumps through opposing nozzles that generate impinging jets within a collision housing. This process induces particle-to-particle collisions in a high energy zone, creating intergranular fractures along grain boundaries. In our batch scale and continuous operating testing, we have found that controlling key processing variables such as nozzle velocity, slurry solids percentage, and the number of passes can allow for controlled size reduction and maximum mineral liberation. Since 2018, Disa Technologies has demonstrated the versatility of HPSA by testing it on diverse materials, including phosphate, copper, uranium, vanadium, zinc, rare earth elements, lithium, nickel, mineral sands, gold tailings, and more. The intent of this paper is to present an overview of our test results describing in detail impacts on size reduction, recovery, and grade with a focus on HPSA's adaptability across ore types. Looking forward, it explores the potential to process materials such as DR-grade iron, clay, PGMs, bauxite, and various tailings. Additionally, it examines planned advancements in HPSA technology, including optimized nozzle spacing, higher capacity models, improved tonnage-to-footprint ratios, enhanced maintenance features, and expanded nozzle configurations.

Jan 1, 2025

By Clayton Vernon Deutsch, Marcel Antônio Arcari Bassani, João Felipe Coimbra Leite Costa, Vanessa Cerqueira Koppe, Pedro Henrique Alves Campos

Traditional linear approaches oversimplify the complexities of ore blending. Adopting nonlinear blending models in mining and mineral processing is critical to enhancing prediction accuracy and allowing optimization regarding nonadditive variables, such as metallurgical recovery. This study implements a simulated annealing algorithm in short-term mine planning that seeks to optimize the metal recovery by considering how to blend the ore in the mine better. Two nonlinear metallurgical recovery models are used as inputs to the algorithm to represent synergistic and antagonistic blending. The results of the case study demonstrate that the optimized schedule exhibits improvements in both blending behaviors when contrasted with conventional linear-based scheduling plans.

Jan 1, 2025

By Ebelia Manda, Peter RK Chileshe, Sydney Chinyanta, Abdul Samson, Victor Mwango Bowa

Buttressing is a widely employed control measure for mitigating slope failure in various settings such as openpit mines, road sections, dams, and hilly or rocky terrains. This paper presents a study on the effects of buttressing, specifically concerning slide-head toppling failure. Drawing insights from a case study of slide-head toppling in Zambia, our investigation employs both analytical (limit equilibrium) and numerical (3DEC software) modeling techniques. The analytical and numerical models exhibit notable similarities in their schematics and overall results, revealing a subtle effect of buttressing in controlling slide-head toppling failure. This study contributes valuable insights into the nuanced role of buttressing in slope stability, with implications for optimizing buttress design parameters. While the overall findings suggest a modest influence of buttressing on slide-head toppling failure, further discussions delve into the specific implications and potential refinements for practical applications.

Jan 1, 2025

By Michael Forbes, Rick Jeuken

The challenge of managing geological uncertainty in mine planning is central to optimizing resource extraction while minimizing risks and costs. This paper introduces a novel approach to coal mine planning by integrating chance-constrained programming to address the uncertainty inherent in mining operations. The main takeaway from this work is the demonstration that focusing on reducing the variance of high-quality coal blocks, rather than low-quality ones, can lead to better blending outcomes, higher profitability and more efficient resource utilization. The study presents a case study of a coal mining operation, using chance-constrained programming to manage the uncertainty surrounding coal property predictions, specifically the predicted coke strength after reaction (CSR).

Jan 1, 2025

By John Thella, Edward McNew, Christina Thella, Mends Emmanuel, Pengbo Chu

Ore sorting is an emerging technology in mineral processing that can produce a significant impact on the total mining and processing economics. The technology is capable of preconcentrating an ore based on differences in the mineralogical characteristics, which can include color, brightness, transparency, size, shape, density or near-infrared response. The current practice of conducting an ore sorting test typically requires large amounts of materials to be sent to a commercial testing facility, which is expensive and time-consuming. Here, we present a novel method to simulate commercial ore sorting on a laboratory scale using a rare earth ore.

Jan 1, 2025

By Isabel Barton

This article describes and analyzes the history of research into the origins of the uranium-vanadium(-radium) deposits of the Paradox Basin. For several decades after their discovery in 1881, research was desultory and almost entirely descriptive, fluctuating with demand for vanadium and radium. Systematic geologic descriptions, accumulated between 1910 and 1950, supported rapid progress during a government-backed surge of research in the 1950s, when uranium was the chief commodity of interest. Most of the major theoretical innovations date from this decade, including the use of concepts from solution chemistry to identify the conditions and constrain the processes of deposit formation and alteration. Research from this time was highly influential in geology at large, providing the model for sandstone-hosted uranium deposits worldwide and numerous new minerals and mineral structures. After about 1960, mine production remained high but research dwindled, and most new advances were to the details of the hydrothermal model of ore formation rather than its core concepts. By the end of the 1960s, the deposits were understood to have formed at the mixing interface of an oxidized, metal-bearing water with a reducing fluid of debated origin. Research was desultory over the ensuing decades; the source of the metals and the nature of the chemicals constituting the trap were not clearly established. They remain questions today, as do factors like the relationship between ore mineralization and the geologic history of the basin. (More in the full paper.)

Jan 1, 2025

By Mirza Muhammad Zaid, Nana A. Amoah, Ping Chang, Guang Xu, Ashish R. Kumar

The key takeaway of this study is the significant improvement in dust protection for roof bolter operators in underground coal mines. The optimized canopy air curtain (CAC), with a redesigned two-level manifold system, provides uniform airflow distribution that effectively reduces dust exposure. By enhancing the protection of operators from harmful respirable coal and silica dust, this technology can reduce the risk of diseases such as coal workers’ pneumoconiosis (CWP) and silicosis. The application of this optimized CAC design in real-life coal mining operations can dramatically improve worker health and safety by minimizing dust-related exposure, ensuring compliance with industry regulations and prolonging the lifespan of mining equipment by reducing dust accumulation.

Dec 1, 2024

By Connor Meinke, Shaihroz Khan, Kevin Erwin, Rajiv Chandramohan, Muhammad Usman Siddiqui

A geometallurgy study aims to link metallurgy and geology to reduce technical risk and enhance the economic performance of a mineral-processing plant. It does so by accounting for variability in a deposit to develop cash-flow models with variable throughput rates. High-quality sample selection for metallurgical test work that is representative of the deposit is an essential component of a geometallurgy study, but the large multidimensional data set makes sample selection a daunting task, as classifying the data set while respecting its heterogeneity is difficult. This paper presents a streamlined approach for sample selection, using statistical analysis techniques in Python. It cuts down time to select samples from around 1,200 s per drillhole to about 60 s for data classification and from 12 h to 8 h for handpicking samples from the classified data set, translating to cost savings. The cumulative sum method and k-means clustering method are used in the methodology to elegantly classify the data and select representative samples. The effectiveness of the methodology is demonstrated by presenting data from a prefeasibility study of a copper-iron mine in which 40 samples were selected for flotation test work.

Dec 1, 2024