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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 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 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 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 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 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 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

By Katherine Hynard, Colin Eustace

Development quarterly mine plans is generally time-consuming and requires consideration for many operational constraints and competing objectives. With lengthy turnaround times, there is often limited opportunity to adjust the quarterly plan details to account for differences between actual state of the mine operation at the time of execution and expected state at the time that the plan was developed. Actual mining task completion times can vary significantly from the expected task completion times, due to many factors. In the absence of revision to account for differences between the planned and actual state, resource allocations and mining sequences, embodied in the quarterly mine plan may no longer be the best approach at the time of execution to achieve an efficient mining operation. Simulation and optimisation tools can be used to support rapid refinements for development and execution of weekly mine plans. When initialized with an operations data feed to establish the actual state of the mine operation, optimisation models can be used to facilitate both automation and revision of the quarterly plan to produce a weekly or daily plan for execution. This approach can remove much of the manual burden of revision if the existing quarterly plan no longer represents the best approach to meeting production objectives. Following initialisation with mining actuals, simulation models can provide a representation of the range of likely mine operations outcomes. The models can be used to accelerate the planning process by providing visibility of future performance and identifying likely operational issues. They can also be used to test the robustness of the plan with respect to performance variability and response to disturbances. Following identification of issues, simulation models can also be used for evaluating mitigation measures, through automated testing of alternative plans. Integration of mine data feeds with simulation and optimisation tools enables automated optimal responses to variations between the quarterly mine plan and actuals to be tested prior to release of and execution plan and implementation. The use of simulation and optimisation in weekly mine planning will be discussed in this paper with reference to surface mining examples.

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

By Miguel Carrera, Jose Arnal, Julian Ortiz, Maarten Haest, Fouad Faraj

ShovelSense is a complete solution for bulk ore sorting at the mine face that combines robust shovel bucket mounted X-ray fluorescence sensors, with real-time material classification, fleet management integration and supporting cloud-based data infrastructure, to ensure each truck always goes to the right destination. Correct truck destinations are a challenge in many current mining settings because the material classification of a truck, which is the key input into the truck destination, is often based on estimates at block resolution (~20x20x15m), with limited adjustment for blast movement and mixing. In this contribution, we present a geostatistical, data driven approach to estimate the potential value of bulk ore sorting at the mine-face for seven open pit mines. The approach is premised on the assumption that truck loads within blocks follow a simple gamma distribution. Standard ore control data from the seven mines have been used to estimate the potential bulk sorting value at these operations and ShovelSense-integrated dispatch datasets have been used to validate these estimates for specific benches on which ShovelSense operated. ShovelSense results validate the ore control data-based value predictions and show how at mine-face bulk ore sorting value varies between distinct deposit types. In general, deposit types that have higher bulk ore grade contrasts, display more potential ShovelSense value. The manuscript concludes with the limitations and opportunities of applying the proposed value estimation approach to evaluate at mine-face bulk ore sorting opportunities for open pit operations.

Jun 25, 2023

By Javad Sattarvand, Milad Ghahramanieisalou

As new technologies are introduced to the mining industry, the challenges related to their safe assimilation and the potential changes they bring to mining techniques necessitate adjustments to the current operations. Predictive simulations are critical to understanding unforeseen scenarios and shifting costly changes from the operational stage to the design. The Digital Twin methodology has gained a lot of attention in recent years. Many industries, from manufacturing to engineering and even social sciences, are adopting this approach to understand better the complex systems they are working with. By considering the enormous expenses and risks related to a pilot project, DT could replace that by acting as a prototype in which realistic tests and simulations are carried out with close to zero cost. DT includes a data hub, simulation and analysis tools, and visualization platforms to enable appropriate designs and monitoring plans focusing on unknown areas. A few examples of DT's application in mining include fleet management, Mine-to-Mill optimization (especially D&B), geotechnical digital twin (gDT), etc. When developing regulations, testing the efficiency of the mine layout, design, and magnitude of the restricted area and its access points, human-machine interactions, risk identification, and management processes, examining the efficacy of control systems, and monitoring, DT could help speed up (or completely replace) the process in the physical world.

Jun 25, 2023

By Paweł Stefaniak, Natalia Duda-Mróz, Sergii Anufriiev, Wioletta Koperska

Due to the high environmental risks and negative impact of a failure, tailings storage facilities (TSFs) need constant monitoring. Advanced mathematical models have been developed in the past to predict the behavior of TSFs and raise alerts if needed. To be precise and reliable, such models need a spatial distribution of soil types within the dam as an input. Getting this data from laboratory measurements is time and costconsuming. In this article, we propose an ANN-powered algorithm, which allows us to accurately estimate the soil distribution based on a cone penetration test (CPT).

Jun 25, 2023

By Kadri Dagdelen, Lee J. Zamalloa

Modern surface mining operations are always seeking for minimizing costs while increasing production rates, among the optimization of other specific tasks that have an impact on the economics of the mine. Specifically, the cost related to the production fleet operation (shovels and trucks), the maximization of the overall performance of the equipment, and the uncertainty to meet the goals at the end of the operational time horizon are always topics of discussion with no apparent simple answer. On the other hand, truck-shovel dispatch systems based on Linear/Non-Linear Programming Algorithms have been in operation since mid-1990’s with different levels of success depending on the correct arrangement of operational resources and the complexity of the mine, but having in most cases some important shortcomings related to the way these systems deal with the decision making of the operational objectives, being necessary to have a human agent (dispatcher) to guide the system when the operational parameters change, creating the possibility of suboptimal decisions due to the human factor. This paper discusses the implementation of a novel methodology based on AI techniques to assist in the decision making of the best goal selection when operating a Fleet Management System, taking into consideration time periods as variables and integrating them as part of the analysis, providing a better overview of the forecast plan completion with respect to previous production numbers and evaluating best alternative to handle future events to minimize losses or maximize profits, resulting in better truck assignments, minimum shovel idle times and overall productivity improvements of the operation at the end of the time horizon.

Jun 25, 2023

By Angelina Anani, Nathalie Risso, Carlos Olmos de Aguilera

The traditional approach to safety clearance monitoring and proper PPE usage in underground mines is time-consuming, costly, and susceptible to negligence. However, automated real-time monitoring can be implemented using computer vision-based approaches. This study aims to develop a system that provides timely information based on the automatic detection of proper PPE use and safety clearance in underground mines. Deep learning and a decentralized computing structure between the cloud and sensing devices (fog technology) are used. We hypothesize that a camera-based system can provide near real-time assessment of proper PPE use and verified clearance to access restricted areas in underground mines.

Jun 25, 2023