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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 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 Aboagye, K Appau, W. Assibey-Bonsu, C. Muller

A case study based on two recoverable resource modelling techniques (indirect and direct or localised methods) have been derived using typical feasibility study or new mine drilling data configurations from the extensions of an existing mine’s massive production database. The database is from the Gold Fields Tarkwa Gold mine (~6Moz Mineral Reserves and 13-year Life-of mine). The corresponding recoverable resources from the various techniques are then analysed using the open pit Whittle optimisation process. The results were then compared based on the Whittle optimisation results to determine the efficiencies of the approaches and the validity of the recoverable mineral resource estimates for mine planning and financial forecasts (including Cash Flows etc). Based on historical production metal recovery, the paper also assesses the corresponding orebody and mine planning as well as financial risks with regards to the modelling techniques. The study shows that the localized direct and somewhat more straight forward approach if applied efficiently can provide an equally useful tool for computing recoverable resources for mine planning and financial forecasts, as compared to the indirect approach as presently in use.

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 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 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 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 Alessandro Navarra, Aldo Quelopana

For decades, open-pit mine planning algorithms have incorporated geological uncertainty and other elements or characteristics for increasingly realistic representations. Among these complexities is the detailing of downstream processes that occur inside processing plants. Previous studies have demonstrated that the strategic mine plan and mineral concentrator design are coupled, and should therefore be optimized simultaneously within the same quantitative framework. However, existing frameworks offer limited representation of operational modes. This work demonstrates an approach to resolving this gap, in an optimization based on a parallelized Variable Neighborhood Descent (VND). The resulting algorithm embeds mass-balancing linear programming (LP) using a DantzigWolfe decomposition. A case study of a Zn-Pb deposit is presented to show the performance reached.

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 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