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By Taghi Sherizadeh, Kutay E. Karadeniz, Dogukan Guner, Samuel Nowak

Mine explosions are one of the most concerning risks related to underground coal mining, as a single gas explosion can cause injuries and fatalities among miners. Underground infrastructure seals and build-in-place (BIP) refuge alternatives (RAs) are employed to isolate an area from active workings to mitigate coal mining-related accident risks. They are designed to withstand blast pressures from a possible accidental detonation of the contained gas or a coal dust explosion. Crosscut seals and BIP RAs, typically consisting of steel-reinforced concrete, are prone to strong convergence and have a significant leakage potential due to high convergence damage near mined-out areas. The roof-to-floor convergence can change the structural integrity of these structures, and altered structural integrity can lead to less explosion resistance capability. Hence, a thorough analysis is needed to further understand their explosion response under various strata conditions.

Jul 1, 2023

By Chris Roos

The mining industry has made strides in planning for operations to be safe for the workforce and public, to protect the natural environment, and to maximize value for shareholders. However, many of the techniques used to accomplish these goals are based on estimated models of mineral content, production efficiency, and economic performance. The industry has begun to recognize the need for understanding the uncertainty in these models, how it affects their decisions, and what the potential impacts are for each project. The challenge has become how technical professionals can effectively communicate the impact of this uncertainty and its importance to both technical and non-technical audiences. This paper presents mining engineers (and other technical professionals) with traditional and modern principles of data visualization and technical communication that can assist with communicating the effects of uncertainty in the mine planning process.

Jun 25, 2023

By Yashar Pourrahimian, Magreth S. Dotto

Fragmentation by blasting is a critical process in hard rock mining. The blasting process and outcomes are highly influenced by rock mass properties and their response to higher stresses and loading rates. Each rock mass is unique, but some features affect the blast energy similarly, leading to similar outcomes. Having analyzed these features and their influence on blast results, they can be incorporated into the blast design to achieve better outcomes. This paper presents a numerical model validated by a case study from the actual mine blasts that studied the effect of rock mass properties on explosive energy production, attenuations, and overall blast outcomes. The results show that when the model parameters are calibrated based on real data, they can be used to simulate the fragmentation process and therefore be used to aid blast design.

Jun 25, 2023

By Alireza Kamrani, Yashar Pourrahimian, Hooman Askari-Nasab

In-pit crushing and conveying (IPCC) is an option to decrease the enormous operating costs that a truck shovel can introduce in an open-pit mine. Finding the best locations for the IPCC over the mine life will impose a new set of requirements for the mine planning. Through a two-step mathematical programming model alongside the road network, this method finds the optimal in-pit crusher locations and relocation times first and then optimizes the long-term mine schedule. The model is verified by a real mine. The results show that the truck travel time could be reduced by 500% and the discounted cash flow increased by 8% with an optimized schedule considering the crusher placement.

Jun 25, 2023

By Javad Sattarvand, Amin Moniri-Morad

Predicting equipment failure behavior plays a critical role in providing a safe operation. Although various reliability evaluation methods are available for estimating failure behavior profiles, these approaches are based on mathematical formulation, leading to actual operation simplification and improper estimation of failure trends. This study proposes a discrete-event simulation (DES) approach to model fleet failure behavior. This approach can model the maintenance strategies in multi-component systems with minimum simplifications. In this regard, the first step was to collect a historical failure time dataset (e.g., the time between failures). Then, the DES model is established to predict the severity of failure events and design maintenance strategy considering actual operation circumstances. Afterward, the maintainability analysis was performed to estimate equipment downtime and availability. The achieved results showed that the system availability was about 85%. Also, the findings demonstrated that the DES could efficiently predict the equipment failure behavior using various stochastic events.

Jun 25, 2023

By Cuthbert Musingwini, Adeodatus S. Nhleko, Mahlomola I. Mabala

Optimization of a three-dimensional (3D) underground stope layout is complex given the number of variables and constraints to consider, such as the stope size. Determination of the appropriate stope size in stope layout optimization problems is a complex process that must consider several factors including placement of infrastructure and development, production scheduling, and equipment selection. An inappropriate stope layout can result in unrealistic expectations for a production schedule, consequently negatively affecting the derived economic value. Therefore, this paper used a dual interchange algorithm (DIA) to evaluate the effect of stope size on the stope layout economic value (SLEV) for a synthetic Platreef mineral deposit planned for extraction using sublevel open-stoping. The results showed that generally as the stope size increases, the SLEV decreases. This aligns with practical mining because larger stope sizes have a higher chance of incurring more dilution which reduces economic value, while smaller stope sizes enable more flexible extraction where unprofitable blocks can be left unmined as waste, thus increasing the derived SLEV.

Jun 25, 2023

By Robert Bissonette, Samir Sbai

A critical component of implementing autonomous systems is the ability to use information from a robust sensory and information system to make sound, predictable, and repeatable decisions to assure safety. This means that any such system would need to evaluate what objects (people, equipment, boulders, etc.) of interest exist in that environment (with relevant data about identity, trajectory, characteristics, etc.), project a future continuum of those objects (including considerations for variabilities), and make control intervention choices to minimize risk due to exposures in the present operational area. The components of an overall system would include: a) sensor and data fusion, b) probability projection of future states, c) generation and evaluation of alternate futures, and d) implementation of best course of action to reduce risk. The National Institute for Occupational Safety and Health (NIOSH) Spokane Mining Research Division (SMRD) is studying this framework for applications for autonomous mining equipment. This paper focuses on using information from the sensor fusion engine to generate a continuous projection of future and alternate states (b and c above) incorporating the factors of confidence, accuracy, tolerance, and other variabilities. We refer to this as Machine Situational Awareness (MSA).

Jun 25, 2023

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 Javad Sattarvand, Frimpong Kwaku Asare

The safe evacuation of miners during an emergency in the shortest amount of time is one of the most essential features of a mining operation. Despite advancements in mine evacuation research, the usage of mine vehicles during evacuation has not been considered. The majority of recent studies have been done on miners’ evacuation on foot. Mathematical formulations including Minimum Cost Network Flow (MCNF) models, Dijkstra's algorithm, Floyd-Warshall algorithm, and Ant Colony Optimization models have been used to solve mine evacuation problems. These models, which concentrate on determining the shortest escape routes during the evacuation, have been found to be computationally expensive and may not offer the best solutions in some cases. The goal of this paper is to incorporate light vehicles in the evacuation process using a mixed-integer programming approach. The methodology aims to minimize the total time required to evacuate the mine, ensuring minimal exposure of the miners to the adverse conditions that triggered the evacuation.

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 Ehsan Moosavi, Kamyar Tolouei

In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets and uncertainties. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal and waste tonnages, average grades, and cash flows of each period. In this paper, to synthesize grade uncertainty into the strategic schedule, a stochastic integer programming (SIP) framework is presented to LTPSOP. The objective function of the SIP model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations is applied to synthesize grade uncertainty in the strategic schedule and produce a more profitable and riskbased production schedule. To solve the LTPSOP, the hybrid approach with the augmented Lagrangian relaxation method, the Harris Hawks optimization, the Random Forest method and Monte Carlo method was proposed. To indicate the applicability of the model, a case study on a gold mine was implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability .

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 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 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