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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

By Liam Findlay

Optimizing a production schedule with semi-mobile in-pit crushing & conveyance (IPCC) requires integrating extraction sequence, destination policy, crusher relocation, conveyor layout, and truck fleet investment decisions. An integrated stochastic optimization framework is proposed to produce long-term schedules for mining complexes using multiple semi-mobile IPCC systems. The optimizer selects the crusher locations and designs the conveyor routes from anywhere in the mines. The stochastic framework considers simulated multi-element orebody realizations and manages associated risk. A hybrid metaheuristic based on simulated annealing and evolutionary algorithm is implemented for the optimization. The method is demonstrated using an iron ore mining complex.

Jun 25, 2023

By Enrique Jelvez, Pierre Nancel-Penard

The traditional way of generating open-pit mine production schedules has several limitations. This work addresses the design and production-scheduling problem by considering minimum operational space requirements under a Direct Block Scheduling approach to generate extraction sequences that satisfy the operational space required. For this, traditional formulations were extended by including a new set of constraints that generate operating spaces for mining equipment to move and extract material. Then, a heuristic algorithm produces close to optimal practical pushbacks. This proposal was applied in a case study, and the results show its ability to generate practical production schedules without sacrificing expected return, obtaining differences in terms of Net Present Value (NPV) lower than 1% when compared to the traditional approach.

Jun 25, 2023

By Biswajit Samanta, Rahul Kumar, Pranjal Pathak

The Open Pit Mine Production Scheduling Problem (OPMPSP) is a complex optimization problem that seeks to determine the best sequence for extracting mineralized material from the ground in order to maximize the Net Present Value (NPV). This process must take into account various capacity constraints, such as mining rates, processing limits, stockpiling capacities, blending requirements, and precedence constraints, which dictate the order in which blocks can be extracted. Traditionally, the OPMPSP has been formulated as a mixed integer programming problem, employing binary and continuous variables to represent the extraction of each block. However, this approach has limitations, particularly when it comes to addressing the stockpiling issue. In response, some researchers have proposed mathematical models, which offer a more accurate representation of the problem but come with their own set of challenges. In this study, an alternative approach to tackle the OPMPSP with stockpiling by employing constraint programming (CP) is explored. The method was carefully tested on the Newman1 dataset from the well-known Minelib library. The results show that the CP-based model obtained a production schedule having about 2.69 % higher NPV than the NPV of the solution, which is recorded in the Minelib Library.

Jun 25, 2023

By Carlos Fonseca, Gustavo Usero, Roberto Mentzingen Rolo, Gabriel Moreira, Octavio Rosa de Almeida Guimarães

Geological domain modeling is an important step in mineral resources evaluation. The procedure can be laborious and time-consuming, especially in multivariate settings. However, estimates are significantly improved when carefully limited by geological variables. This paper proposes a workflow for geological domain modeling suited for stratigraphic deposits. The workflow consists in automatically defining the domains by using clustering algorithms, generating the surfaces that represent the contacts by interpolation and subsequent triangulation, and simulating the surfaces. The proposed workflow produces consistent and realistic results and allows the user to test different domain configurations and assess volumetric uncertainty automatically and quickly.

Jun 25, 2023

By Clinton Birch

The multiple-criteria decision analysis is a technique that helps to find an optimal solution by evaluating conflicting criteria. In this study, an approach to perform multiple-criteria decision analysis for cut-off grade optimization, which considers the diverse stakeholder groups prevalent in the mining industry, is presented. The optimization model developed in this approach links the resource block model to the financial model, enabling the process to either maximize profit, net present value, revenue to the State, or extend the mine's life. To identify the optimal cut-off grade in each case, the Excel Solver function is utilized. Although it has limitations, the Solver function can still be modified to handle models with thousands of block values for determining input variables based on the target value. Following stakeholder engagement and dialogue, a single weighted average cut-off grade can then be determined. This approach is demonstrated for nine South African Witwatersrand gold mines.

Jun 25, 2023

By George Biro, Christoph Mueller

A number autonomous operations is in place in several, mainly in fenced areas, where safety is assured by locking off the entire area for other traffic. However autonomy can be brought much further if functional safety is assured and a real mixed traffic of autonomous and manually operated vehicles is possible. MobileTronics has created a modular software and control framework for autonomous machines. The paper covers application examples in which the deterministic object recognition in combination with probabilistic algorithms and sensors is used as a basis for functional safe certification of mixed traffic autonomy.

Jun 25, 2023

By João F. C. L. Costa, Vanessa C. Koppe, Clayton V. Deutsch, Marcel A. A. Bassani, Pedro H. A. Campos

Blending inevitably occurs to some degree during mining and mineral processing operations. Traditional mine scheduling assumes that all variables average linearly, which allows the calculation of the blended material properties as a linear weighted average. However, metallurgical properties such as metal recovery and Bond Work Index (BWI) are known to average nonlinearly. The nonlinear properties of the blended material can be calculated using nonlinear blending models. A simulated annealing algorithm is developed and applied to a short‐term mining face optimization problem to maximize the total metal recovered considering nonlinear blending. Compared to a standard schedule plan, the optimized one shows relevant improvements in synergistic and antagonistic blending behaviors.

Jun 25, 2023

By Savita Schlesinger, Mike Hanna, Nathan Scalla, James O’Shaughnessy, Steve Miller

Loudoun Water’s Quarry A (Milestone Reservoir) project involves conversion of a rock quarry to a billion-gallon raw water reservoir. The project includes a new 40 MGD pump station, deep intake shaft, and three drill and blast intake tunnels for accessing the reservoir. Due to concerns regarding constructability and construction risks, the project design was paused while a Construction Manager At-Risk (CMAR) contractor was procured. The CMAR contractor was engaged collaboratively with the design team to provide risk and constructability input for refining the design. Aspects of the original design, design modifications, and benefits of the CMAR collaboration will be presented.

Jun 13, 2023

By Julia Herhold

The demand for sustainable and profitable/efficient projects within the tunneling and other underground engineering sectors grows rapidly. Construction tasks play a key role within the project conduction, whether as primary (tunneling), secondary (e.g., mine securing) or tertiary processes (e.g., workshops). The construction tasks of primary processes create the main structures, such as tunnels, which resemble the main project goal. For the secondary and tertiary processes, the purpose of the task shifts. The main goal is to provide structures, such as the securing of the cavity and workshops, that enable the project and enhance the efficiency of the primary conduction underground. To plan and optimize the conduction of construction tasks underground, whether they are identified as primary, secondary or tertiary tasks, the work preparation can be used. This method, known from the above ground construction, can be adapted and used underground. The approach of a digital work preparation tool combines, connects and adapts the main tasks “choice of procedure,” “logistics” and “site planning.” In addition, the digital tool is connected with digital models (construction, site, surroundings). With the adaption and forward projection of the work preparation, underground constructions (e.g., tunneling) can provide a more sustainable and efficient outcome.

Jun 13, 2023

By Tristan Joubert, Fabio Panella, LiLing Chen, Yung Loo, Michael Devriendt, Nasir Qureshi, Ahmad Ali

Tunnel inspections have traditionally been carried out manually by inspectors writing up observations and taking photos of defects. The results from the inspection and the defects observed are dependent upon the rigor of the inspectors and may be subject to repeatability and consistency issues and is often time consuming with elevated health and safety risks. This paper will discuss work that is being carried out in developing and implementing an innovative hardware and software solution integrating machine learning to automate the process of capturing objective tunnel condition information, offering cost and programme savings as well as health and safety improvements

Jun 13, 2023

By Ehsan Alavi, Eren Kusdogan

The City of Toronto’s Coxwell Bypass Tunnel is currently being constructed by North Tunnel Constructors ULC, a joint venture of Jay Dee Canada, C&M McNally Tunnel Constructors and Michels Canada. This project includes construction of approximately 10.5 km of 6.3 meter finished diameter rock tunnel, five 20 meter diameter storage shafts and eleven tunnel connection drop shafts, along with associated deaeration and adit tunnels. This paper describes progress on the project to date and reviews the risk mitigation measures utilized on the project to move the project forward through tough geological conditions during shaft excavation and tunneling phase of the project.

Jun 13, 2023

By Verya Nasri, Agustin Rey

Once completed, the Montreal Réseau Express Métropolitain (REM) will be the fourth largest automated transportation system in the world. The REM represents construction costs of approximately 7.0 billion Canadian dollars. The project consists of 67 km of twin tracks over four branches connected to downtown Montreal. The project includes 26 stations with 3 underground stations in downtown Montreal. One of the underground stations was built using the NATM method with thin permanent shotcrete initial and final liners separated by a sprayed on waterproofing membrane. The 2 other underground stations were built with the cut and cover approach including one using permanent secant pile wall with permanent walers and struts as the support of excavation and the final perimeter wall of the station. The project also includes the rehabilitation and enlargement of the Mont Royal Tunnel. This 100-year-old double track tunnel is about 5 km long. The REM also consists of 3.6 km new TBM tunnel connecting downtown to the Montreal International Airport through saturated soft ground and karstic rock. By the time of RETC, the construction of REM underground structures will be completed. This paper presents the design and construction aspects of the underground structures of this mega project.

Jun 13, 2023

By Randall C. Divito, Craig R. Eckdahl, Bruce D. Wagener, Joseph Klejwa

The existing Central City Tunnel was built primarily in the 1930s with a small portion as old as the 1880s. It conveys stormwater in downtown Minneapolis. It is a 4,400-ft long tunnel located 80 feet underground. The Central City Tunnel has experienced surcharging and surface overflows during heavy rainfall events. This paper presents the details of tunnel and shaft design for the new Central City Parallel Tunnel to mitigate the overflows. The design considered hydraulics and geotechnical engineering for the structural geologic conditions including the weak Saint Peter Sandstone and overlying strong Platteville Limestone. Ground conditions resulted in tunnel designs including large flat-back box tunnels and unique cathedral-shaped tunnel sections.

Jun 13, 2023