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By Jan Luca Grunow, Ansgar Bendiek, Klaus Böde

According to the Markowitz portfolio theory the value of a diversified portfolio is higher than the sum of the parts of a portfolio, which is an important consideration for the correct valuation. This is valid for real estate portfolios as well as for mining assets (or enterprises). This paper analyses the application of the portfolio theory to a diversified portfolio of assets in the real estate industry and draws conclusions on further research work to be done in the minerals industry. The volume of real estate transactions is increasing over recent years. As such, the nature of those transactions is constantly changing and in some cases portfolios become more heterogeneous as they are composed of different property categories, e.g. locations and types of use. This makes their economic evaluation more difficult. This paper focuses on the acquisition of a fairly heterogeneous real estate property portfolio purchased by “Commerz Real” in Germany in Q4 2019. For economic evaluation the widely used Markowitz portfolio theory (Markowitz, 1952) was applied. It was investigated whether diversification effects could achieve added value in the form of a reduction in risk compared to the sum of their individual components. As a result the findings of this research could be applied to other heterogeneous real estate transactions and even to mine property portfolio or PPP1 project portfolio transactions.

Jun 25, 2023

By J. Sattarvand, P. C. Augustine

Despite significant safety improvements, the mining industry remains one of the most hazardous occupations globally and the evacuation of Miners, when an uncontrollable incident occurs in the mine, is the best bet to saving lives. However, Human factors/behaviors during an emergency are likely to influence the evacuation performance. Other industries have made a significant effort to determine these human factors that can impede evacuation performance, however, the current state-of-the-art in mine evacuation or self-rescue is that the evacuation route is predetermined, and a static sign is used to direct miners to the predetermined safe location. This method is limited in representing actual conditions that arise in a real emergency, neglecting the different behaviors displayed by humans. As optimization of the evacuation of a mine plays a fundamental role in emergencies and modeling evacuation behavior and movement of miners is a complex task, this research utilized Agent-Based simulations to simulate the evacuation behavior and performance. It was observed that people with the smart evacuation device made fewer mistakes and arrived at the safe place in less time compared to those have no access to the device. The Mathematical model used in optimizing the shortest path to a safe place is also explained in this paper.

Jun 25, 2023

By Amy McBrayer, Andrea Brickey

Limited research has been conducted related to the development of mathematical optimization models for production scheduling at large surface coal mines using the area, i.e., strip, mining method. Increased economic and technical challenges have led to a new interest in production schedule optimization models for these type of mining operations like those currently applied to model underground and open pit mines. While area mines share similar characteristics to both open pit and underground metal operations, neither an open pit nor underground production scheduling model adequately depicts an area mine mathematically. We present an integer programming model with blending constraints and discuss the challenges and opportunities related to the implementation for area mines as well as future research in area mine production scheduling.

Jun 25, 2023

By Błażej Doroszuk, Robert Król

Mill drums are essential equipment in various industries for the comminution of materials. To optimize their design and operation for efficient and cost-effective processing, it is necessary to understand the behavior of the mill drum under various operating conditions. This study investigates the behavior of a mill drum using a combination of experimental and numerical methods. The factors that can influence the behavior of the mill drum, such as the size and shape of the mill, grinding media, rotational speed, filling level, and material properties, are discussed. Experimental methods can be limited in obtaining measurements of internal processes, so numerical methods such as the Discrete Element Method (DEM) are also used to simulate the behavior of the mill drum. In this study, the experimental measurements of the motion of the grinding media are obtained using computer vision methods, and the numerical simulations are calibrated using the experimental results. A designed experiment approach is used to evaluate the performance of the mill drum under various operating conditions. The results of this study can be useful for the design and optimization of mill drums to achieve better performance and energy efficiency.

Jun 25, 2023

By Joseph Githiria, Khadija Omar Said

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

Jun 25, 2023

By W Medina, G Angulo, A Anani

A current challenge faced by the mining industry is solving the integrated transition mine problem (TMP) that maximizes the net present value (NPV). This study aims to develop an optimization framework capable of solving the TMP. The problem is NP-hard and therefore computationally intractable. Three ad hoc approaches – Exact solution, Benders’ decomposition, and Benders’ decomposition with Bienstock-Zuckerberg (BZ) algorithm – are compared for different scheduling periods. The TMP is formulated as a mixed integer linear programming model, implemented in Python, and solved with the Gurobi optimizer. The results showed that Bender's decomposition with the BZ algorithm outperforms the other approaches in execution time, computational cost, and feasibility to address large problems in exchange for a marginal cost in the quality of the NPV obtained. In addition, the probability of finding the optimal point of transition is higher regardless of the number of feasible crown pillar locations evaluated. This is achieved through the optimality cuts proposed in the algorithm.

Jun 25, 2023

By Kate Willa Brown Requist, Eric Lutz, Moe Momayez

As air quality sensor networks become increasingly popular in underground coal mines, it is important to generate paradigms for the application of the collected data. To date, air quality sensing has been primarily used as an early warning system for hazardous air conditions. Using data collected from a network of sensors in a US underground coal mine, we have created multiple visualization methods to show the interactions of carbon monoxide evolution with ventilation airflow. These visualization methods can allow for further analysis of the source of contaminant, as well as better data resolution across the area of concern within the mine. By utilizing univariate spatial interpolations, we present methods for identifying the movement of carbon monoxide at one-minute intervals. The resulting visualizations display the evolution of carbon monoxide concentration across a sizeable study area over a period of 14 minutes.

Jun 25, 2023

By M. Shahsavar, J. Sattarvand, J. Gomez

Mining can be categorized as a hazardous activity, considering some factors such as environmental conditions with a considerable presence of humidity, suspended particles, or falling rocks have affected the severity and number of accidents compared with other economic sectors. The industry analyzes incident reports to narrow the rate of severe injuries and fatalities, conducting root cause analysis and identifying leading indicators. As the International Council on Mining and Metals noted, the vast trove of incident data is not analyzed as much as possible due to a lack of analytics expertise at mine sites. However, machine learning could solve the problem of analyzing all the incident data in a no-timeconsuming way, considering the abundant data, and without using expert personnel in data science. Thus, a Convolutional Neural Network and a Naïve Bayes model were introduced to perform classification in the MSHA database. The database from 2020 consists of 60 fields to describe safety incidents; these fields include mine I.D., accident date, subunit (mill, surface), material extracted, and other metadata.

Jun 25, 2023

By J. P. De Carvalho, R. Dimitrakopoulos

An actor-critic reinforcement learning approach is presented to improve the production of an industrial mining complex by defining shovel allocation and adapting the short-term plan given grade-control decisions. Blasthole data updates the simulated orebody models, which are input into a stochastic grade-control method based on a spatially constrained clustering approach that minimizes the profit-loss function. These aspects are embedded in a discrete-event simulator that defines the material flow from faces to processors. A case study at a copper mining complex shows the methods’ ability to adapt to new information, improving the quality of the material feed and increasing cash flow by 23% compared to a baseline case.

Jun 25, 2023

By Milka Madahana, John Ekoru

Particulate matter is one of the leading pollutants in the mines and in the communities in the areas near or surrounding the mines. The availability of large Clinical and environmental data sets obtained from the mines has brought about the need to use better approaches to analyze the collected data. Prediction and control of particulate matter concentration in the mining environment is essential in minimizing air pollution and protecting the respiratory health of mine workers. The main objective of this research work is to analyze datasets obtained from a mining area using machine learning techniques which will then be followed by the real-time estimation of dust particulate in the mines. Datasets collected over 8 years in an hourly sampling rate from an open cast mine is cleaned and analyzed for trends and patterns. Artificial Neural Networks (ANNs), Support Vector Machines and decision trees techniques are applied to analyze and predict the particulate concentrate. The estimation of particulate concentration using Artificial Intelligence will significantly contribute toward the development of quantitative tools to be used in correlating of cause and effect of dust concentration in mines. The results obtained in this work will form a sub-unit of a respiratory health monitoring system for mine workers. The system is essential in the collection, analysis, and tracking of mine workers’ respiratory health, thus it forms part of an early intervention system for mine workers. From the existing theory, we expect the Artificial Neural Network will perform much better than previously used classical models.

Jun 25, 2023

By Brendan Demich, Linda L. Chasko, Brendan Macdonald, Timothy J. Orr, Jessie J. Mechling, Cassandra Hoebbel, Jason Navoyski, William Helfrich, Jessica Cohen, Paul E. Schmidt, Jennica L. Bellanca

The National Institute for Occupational Safety and Health (NIOSH) in partnership with the Mine Safety and Health Administration (MSHA) developed VR Mine Rescue Training (VR-MRT). It is a training platform that allows mine rescue team members to explore virtual emergency scenarios collaboratively. Team members use a head-mounted display to visualize and interact together with equipment, hazards, and dynamic ventilation. All player actions are recorded and displayed for debriefing. Researchers at NIOSH demonstrated the training platform at various mine rescue competitions, including the International Mine Rescue Competition, and collected feedback. Participants’ responses were overwhelmingly positive. This paper describes the development of the software platform and the results of the usability surveys and interviews.

Jun 25, 2023

By Josiah Pohl, Christopher Utter, Kyle Everly, Akshay Chowdu

Battery Electric Vehicles offer increased energy efficiency for open-pit load & haul operations at a potentially lower overall carbon cost. Electrifying a mine’s fleet, however, presents a unique set of operational challenges. In this study, we explore the effects of electrification on productivity at real operations using a Discrete Event Simulator. The model can simulate multiple shifts, a mixed fleet of diesel and electric trucks, charging and refueling stations, trolley systems, and accurate shovel-truck loading cycles. This study helps determine the trade-offs of adopting electric fleets and how they interact with the layout of today's mines.

Jun 25, 2023

By Augusto Andres Torres Toledo, João Felipe Coimbra Leite Costa, Luciano Nunes Capponi, Diego Machado Marques

Short-term mine planning to open pit requires defining diglines to orientate the excavation geometry. In deposits with multiple chemical variables, it is necessary to have more rigorous control over the feeding of the processing plant. This study analyzes the impact of incorporating multiple chemical variables, the risks generated by geological uncertainty, and the number and starting point locations of shovels in the sequential design of diglines. The objective is to maintain and preserve the low-grade variability, required tonnage, and continuity in sequentially selecting blocks. To achieve this, the probability distribution function of each chemical variable is used while attempting to maintain the ore grades as constant as possible. As it is difficult to maintain multiple elements with low variability simultaneously, the relevance of each chemical variable in the economic benefit is identified. Weights are assigned to signal the importance of each element based on its significance in the recovery process. Several diglines, consisting of sequential blocks, were generated on the same seed point to meet the weekly ore demand. Optimizing the stationarity of multiple chemical variables, accessing and reducing the geological uncertainty performed through geostatistical simulation of grades and transfers functions, considering the number and location of shovels, while meeting the operational restrictions of the selected blocks. The optimized digline with the multiple grades as stationary as possible is retained. The process is illustrated in a phosphate mine. The results show that the proposed method is viable and allows for a quick solution.

Jun 25, 2023

By Amin Mousavi, Soroush Khazaei, Yashar Pourrhimian

As a factory-like method, sublevel caving consists of development activities, stockpiling and production activities that are carried out on different levels independently. Therefore, considering the operation from the development phase to the plant will lead to more reliable production scheduling. This paper presents a mixed integer linear programming framework for the long-term production scheduling of sublevel caving that maximizes the net present value while satisfying development activities, mining, and processing capacities, continuous mining, the allowable number of active mining units, grade blending, and vertical and horizontal sequencing. The model is coded in Jupyter Notebook and is solved using the CPLEX solver.

Jun 25, 2023

By Theophilus Mensah, Kwame Awuah-Offei

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

Jun 25, 2023

By Artur Skoczylas, Paweł Stefaniak, Sergii Anufriiev, Wioletta Koperska, Maria Stachowiak

In many underground mines the excavated ore is produced during the blasting process. Drilling machines that are used to drill the blastholes, where later the explosives will be mounted, are crucial for this task. In order to optimize the currently used techniques, great emphasis is placed on increasing the efficiency and safety of blasthole drilling operations. The natural consequence is the development of monitoring systems and algorithms for processing operational and historical data. In this article, we present the result of the analysis of drilling data carried out as part of the IlluMINEation project. The paper proposes an algorithm for work cycle detection for drilling machines, together with the estimation of general operating parameters and diagnosing the drilling process on the basis of long-term data. Based on the available data, selected components of the drilling cycles have been indicated. Several approaches based on the parameters of the defined cycles are proposed. These approaches can be used in the future to create a full-fledged system for examining the effectiveness and safety of mining drilling rigs.

Jun 25, 2023

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

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

Jun 25, 2023

By 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 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