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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 Enrique Jelvez, Nelson Morales Varela, Christian Ihle, Joaquin Silva

All mineral beneficiation processes require water for its execution. Therefore, the availability and adequate management of water are key to the existence of any mine operation. However, many mines’ operations are affected by water scarcity and risk an increase of operational costs or even production in some cases. This paper proposes a model to minimize the losses of water due to evaporation and re-absorption by adjusting the active and inactive periods of the discharge in different points of the tailing. When applied, the model determines optimal or nearly optimal discharge operation in all the scenarios that were evaluated.

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 Bartłomiej Bursa, Paweł Stefanek, Paweł Stefaniak, Sergii Anufriiev, Wioletta Koperska, Maria Stachowiak

The article presents the main functionality of the DSS class system for monitoring and stability assessment of tailings storage facilities. The presented demonstrator was deployed on the example of the Żelazny Most reservoir - the largest active facility of this type in Europe and the second one in the world. Considered a high-risk structure, it demands a system capable of online analysis of big data from various sources using machine learning, 3D modeling, or GIS mapping with the functions of interactive time-spatial analysis. In this paper, we present a complex calculation module with an integrated user interface.

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 Mario E. Rossi

Often geometallurgical models are prepared at various project stages from very few data and are assumed to be representative of very large tonnages. These may include recovery models; crushing and grinding parameters and models; models of concentrate grades; models of acid consumption; etc. These models commonly do not carry a measure of uncertainty or variability, and are taken at face value at the time of design and planning. This paper proposes a proactive approach to modelling variability, providing uncertainty estimates and thus allowing for risk analysis and mitigation of geometallurgical models. The variability model is built up by understanding the limitations of the pillars of a geometallurgical model: data uncertainty; geologic model uncertainty; domain definition uncertainty; and parameter estimation uncertainty. Of these factors, typically domain definition is the most significant contributor to geometallurgical model variability. First, the fundamental supports for these models are briefly discussed, with domain definition emphasized. Second, two methods for uncertainty modelling are proposed. The first, more applicable to conceptual or early-stage projects, is a simpler and more expedient alternative that provides initial assessments of variability. This is intended to aid management in decision-making related to budgeting, including further sampling and testing. The second, a block-by-block approach that allows a detailed assessment of risk for specific designs and mine plans, thus providing further opportunities for mitigation. In the example provided, emphasis is placed in communicating this variability to both management and technical personnel in the form of “measured-indicated-inferred” indicators and also block-by-block confidence intervals.

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 Silvia Misk, Cristina Araújo, João Felipe Costa, Roberto Rolo

Linear estimation methods are widely used for resource evaluation. Among the existing linear estimation methods, ordinary Kriging (OK) was accepted as the best estimator to determine the average ore grade of each block. However, OK is inadequate for modeling small blocks in relation to data spacing, resulting in over-smoothed models. In this study, the results of the recoverable resource from the short-term model are compared to estimated models generated by the traditional method, i.e., OK, and non-linear methods. The conclusion is that non-linear methods improve the accuracy and precision of the long-term model when compared to the model generated by OK.

Jun 25, 2023

By Sebastian Avalos, Julian M. Ortiz

The long-term mine scheduling is the optimal decision on how to extract and process the mining reserves. The resulting plan is mainly driven by economic, metallurgical, and geological factors. Accounting for their uncertainties leads to a robust solution and the feasibility for economic-risk quantification. In this work, we model the spatial uncertainty of grade distributions of multiple correlated elements, by using a multivariate morphing transformation. The result is a set of multivariate geostatistical realizations of the grades, which honor the conditioning data, the direct and cross variograms, and the multivariate statistical distribution, inferred from a set of samples. These models are the input in a reinforcement learning framework, adopted to train a deep neural network that considers the grade uncertainty and leads to an optimum mine plan after training using deep Q-Learning. Mining and processing capacities, as well as economic parameters and metallurgical constraints, are incorporated as the reinforcement learning environment rules. The method is validated on a real deposit, providing insights for future applications.

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 Richard Minnitt, Philippe Renard, Christina Dohm, Winfred Assibey-Bonsu, Matthew Aboagye, Julien Straubhaar, Mahlomola I. Mabala

The application of multiple point statistics (MPS) in oil studies has gained traction over the years; however, the use of MPS in mineral resource estimation and classification is still limited. This is despite MPS’ ability to deal with complex and heterogeneous geology better than the commonly applied two-point based methods. The availability and choice of a representative training image (TI) are often cited as the main challenge holding back MPS’ application in mining geostatistical studies. On the other hand, the general approach used in mining whereby early focus is on higher grade areas and lower grades are planned to be mined later presents a massive opportunity for the development of representative TIs. Mined-out areas provide large amounts of historical data that can be used to develop training images or training datasets that can be used to estimate in mining extension areas (often lower in grade), provided low grade areas are underpinned by similar geology analogue. This approach will enable more robust estimates in mine/pit extension areas for both high- and low-grade extensions than with traditional two-point geostatistical methods.

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 Ademar Lopes, Victor Silva, Heitor Silva, Gustavo Usero, Roberto Rolo, Lucas Pereira, Gabriel Moreira, João Lague, Arthur Endlein, Matheus Feitosa

Grade control models are supposed to provide a higher resolution than long-term or interim models. However, available grids from diamond or reverse circulation drilling do not provide that required resolution. In this study, a geostatistical workflow is proposed to integrate available grade data from production blastholes with its operational parameters taken when drilling downhole. The step of geological logging can be replaced by machine learning models for classifying blastholes into lithologies and hardness classes. Next, indicator and ordinary kriging use the classified blastholes and previous data for estimating the model domains and the grades within each one. The workflow is applied in a world class iron ore mine in Brazil.

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 Nasib Al Habib, Hooman Askari-Nasab, EUGENE BEN-AWUAH

In-pit crushing and conveying (IPCC) is getting popular in deeper open pit mines as a suitable alternative to truck haulage because it offers a lower operating cost due to shorter haulage distance and less truck requirement. Semi-mobile in-pit crusher, currently the most popular IPCC system, is relocated every two to five years, and the short-term plan needs to be updated accordingly. To the best of our knowledge, short-term planning with IPCC is an area of research that has yet to be explored extensively, and hardly any model can generate a shortterm extraction sequence considering an IPCC. This research work proposes a mixed integer programming model to develop a short-term production plan within a time horizon of 12 months. The model's objective is to optimally allocate shovels to minimize the cost of material handling and maximize revenue subject to meeting plant requirements, maximum allowable tonnage variation and IPCC location constraints to achieve production and NPV targets set by the strategic plan. The proposed model will be implemented in an iron ore mine case study for verification. The model is developed and solved using MATLAB. The comparison of results between scenarios with and without IPCC justifies the use of IPCC in large open pit mines from a short-term perspective.

Jun 25, 2023

By 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