Transportation is the most expensive task in deep open-pit mining operations. An In-Pit Crushing and Conveying (IPCC) system, as an alternative to the conventional shovel-truck system, requires precise planning to avoid wasting high capital expenditures on trucks. The decision to fit a conveyor system into the pit design is challenging and needs to be addressed in the literature. Further optimisation of the IPCC system requires choosing an appropriate conveyor exit strategy. Conveyor exit schemes include a Dedicated Ramp Slot (DRS), an Existing Ramping System, an inclined tunnel and High-Angle Conveyors (HAC). This paper provides an analytical approach to compare the cost of different conveyor exit strategies in open-pit mines. The results indicate that for a depth up to 100 m, the Present Value of Cost (PVC) for the DRS is the least. For the cases deeper than 300 m, the HAC has the least PVC. Sensitivity analysis showed that for the depths between 100 and 300 m, a more accurate and detailed definition of parameters is needed. Besides, in most cases, the HAC provides the lowest payback period and the highest internal rate of return among the alternatives.
Coal is the backbone of the Indian economy, essential for electricity, steel, cement, and other vital items. Although India aims to be carbon emission-free by 2070, it must rely on coal for a few more decades. In the fiscal year 2021-22, India produced 96 percent of its coal from open-cast (OC) mines, whereas 70 percent of India's total coal reserve is in deep undergrounds. Despite underground (UG) coal mining being eco-friendly and yielding richer coal, its production cost is nearly four times higher than OC mining. Therefore, further research on UG mines is necessary to address these challenges. This study analyzed an underground coal mine production system in India and identified production delays as a major cost driver. Causes of delays were categorized by their association with machine, material, and man. An innovative technique, the Machine Criticality Score (MCS), has been developed to quantify production delay costs and guide management in mitigating them. Additional methods like failure mode and effects analysis (FMEA), standard operating procedure (SOP), inventory management tools, and method study were also used in this study to reduce production delay costs.
In cave mines, wet inrushes occur when there is an uncontrolled inflow of fine, wet material from drawpoints. Currently, uncertainty exists regarding the spatial-temporal pattern and severity of inrush incidents. This uncertainty arises from the limited understanding of wet inrush mechanisms within the complex conditions of a cave mine. In this study, the existing gaps in knowledge around the spatial and temporal patterns of inrush incidents were addressed using machine learning techniques. A random forest (RF) model was employed to analyse the inrush database collected at the Deep Ore Zone mine over several years. The conceptual understanding of inrush mechanisms and triggers, along with historical evidence, was employed to establish an initial set of key inrush variables to be used in the RF model. The developed RF model demonstrated promising performance with an accuracy of 85%. The feature importance results indicated that previous inrush history, fragment size, draw rate (short term and long term), differential draw index (short term and long term) and history of inrush at neighbouring drawpoints had the highest impact on inrush susceptibility. The insights gained provide an improved assessment of inrush susceptibility, thereby improving the strategies employed to mitigate inrush risk.
Among surface mining methods, strip mining is a widely used technique for extracting mineral resources. This method is suitable for most near-surface stratiform sedimentary deposits and has historically been linked to surface mining of deposits such as coal, phosphates, and other natural substances. Given the significant economic interest that strip mining represents, it is in the best interest of mining companies to exploit as much of the reserves as possible using this technique, as it offers many advantages compared to open-pit or underground methods, particularly regarding safety and ore recovery rates. Today, the exploited deposits have become deeper, making the strip mining process more complex. Hence, there is a need for certain adaptations and the introduction of new techniques to address these geological conditions that threaten the feasibility of this method. This article explores the challenges that strip mining faces in contexts of thick overburden and subsequently presents the various techniques proposed by researchers worldwide aimed at overcoming these difficulties. The paper is based on an in-depth review of existing literature that deals with case studies of open-cast mines in different contexts. The examination of various approaches and techniques used for optimizing exploitation under thick overburden conditions by strip mining, along with their limitations, is presented.
Machine learning (ML) applications are increasing their footprint in underground mine planning, enabled by the gradual enrichment of research methods. Indeed, improvements in prediction results have been accelerated in areas such as mining dilution, stope stability, ore grade, and equipment availability, among others. In addition, the increasing deployment of equipment with digital technologies and rapid information retrieval sensor networks is resulting in the production of immense quantities of operational data. However, despite these favourable developments, optimisation studies on key input activities are still siloed, with minimal or no synergies towards the primary objective of optimising the production schedule. As such, the full potential of ML benefits is not realised. To explore the potential benefits, this study outlines primary input areas in production scheduling for reference and limits the scope to six key areas, covering dilution prediction, ore grade variability, geotechnical stability, ventilation, mineral commodity prices and data management. The study then delves into the literature of each before examining the limitations of existing common applications, including ML. Finally, conclusions with recommendations/solutions to enhance resilience, global optimality, and reliability of the production schedule through synergistic nexus with function-specific optimised input models are presented.
A mining complex or mineral value chain is an integrated system composed of mines, stockpiles, waste disposal and tailings facilities, processing destinations and transportation, that leads to generating sellable products delivered to customers and/or the spot market. To deal with such a system, conventional approaches optimise the related components independently and sequentially, while ignoring the related uncertainties. This article extends the simultaneous stochastic optimisation of mining complexes, so as to incorporate equipment uncertainties in addition to supply uncertainty. The inclusion of multiple components and different sources of uncertainty empowers the optimisation to capitalise on the synergies between the different components of a mining complex, while also managing the related technical risk and maximising the net present value. An application at a copper mining complex demonstrates the applied aspects of the proposed approach that jointly considers supply and equipment uncertainty to generate life-of-asset production schedules with a 2% higher net present value, when compared to the results considering only supply uncertainty.
In the realm of heavy-duty machinery, excavators hold pivotal roles in construction, mining, and various large-scale projects. The excavator's efficacy relies significantly on its bucket teeth, crafted from robust materials like steel, crucial for soil and rock excavation. However, inadequacies in tooth shape and composition can lead to wear which in turn leads to diminished productivity. Employing finite element analysis, the present research delves into the influence of cantilever profile on TATA Hitachi Ex70 and JCB JS81 bucket teeth, using AISI 4340 and AISI 4140 materials, respectively. Analysing von Mises stress and deformation through Altair HyperMesh, subsequent Altair OptiStruct facilitates topology optimisation aims to reduce tooth mass. It appears that Tooth I (TATA Hitachi Ex70) experienced a decrease in mass, yet an elevation in von Mises stress compared to its initial mass of 2.81 kg and stress level of 1.74 E + 02 MPa. Similarly, Tooth II (JCB JS81) showcased a reduction in mass and a rise in von Mises stress from its original mass of 2.03 kg and stress of 1.038 E + 02 MPa. Validation of optimised designs through the graphs depicting the Factor of Safety (FOS) for both teeth ensures compliance with specified requirements, confirming that optimised excavator bucket teeth designs are safe.
Mine planning and pit design involve making the best decisions and recognising good practices for the profitable exploitation of mineral resources. A pit design project begins with the delimitation of the ore body in the form of blocks, then going through pit optimisation, then pit design and sequencing and ends with the economic evaluation. Several techno-economic indicators are used in these stages and have a direct impact on mine planning. The research methodology proposes an in-depth study of the geotechnical parameters of the pit to be conducted in mine planning in a more reliable and assertive way, with the aid of 3D geotechnical modelling. From a geotechnical point of view, an initial validation of the methodology was performed in an example of application in an iron mine, where a change in the planned slope angles of a pit over a 5-year period was suggested. The suggested new pit guarantees safety factors that suit the minimum stability requirements and proposes a 1.49% higher ore availability and a 0.90% increase in the net present value.
A comprehensive approach for material measurement, tracking, and reconciliation of actual and planned is required for a successful metal accounting system. For ore and metal accounting, the mining industry has mostly relied on spreadsheets to track material flow. The broad use of spreadsheets in metal accounting and mining operations is investigated in this paper. The paper also provides a comparative review of four commonly used software packages, namely (a) Deswik, (b) MineRP, (c) Snowden, and (d) Mapteks’ MRT systems. The capacity to track materials in real-time from the pit or underground to the surface is a standout feature for all the applications. Although there are limitations in dealing with discrepancies in production variables such as mass, grade, fragmentation, density, spatial coordinates, and moisture content, a conceptual data verification and reconciliation process with an improvement strategy aligned with best practices for effective ore tracking at mines is proposed.
The mathematical methods developed so far for addressing truck dispatching problems in fleet management systems (FMSs) of open-pit mines fail to capture the autonomy and dynamicity demanded by Mining 4.0, having led to the popularity of reinforcement learning (RL) methods capable of capturing real-time operational changes. Nonetheless, this nascent field feels the absence of a comprehensive study to elicit the shortfalls of previous studies in favour of more mature future works. To fill the gap, the present study attempts to critically review previously published articles in RL-based mine FMSs through both developing a five-feature-class scale embedded with 29 widely used dispatching features and an insightful review of basics and trends in RL. Results show that 60% of those features were neglected in previous works and that the underlying algorithms have many potentials for improvement. This study also laid out future research directions, pertinent challenges and possible solutions.
Autonomous and smart mines are predicted to become more prevalent. Automation has undeniable benefits in the mining industry, especially in terms of safety. However, automation has also led to unforeseen implications for individuals, organisations and communities. This study undertakes a systematic review of research on the impacts of automation in the mining context. A total of 94 documents that dealt with issues related to humans, safety and communities were found. Documents were analysed using both manual and natural language processing techniques. The review revealed the main concerns the industry must face for the successful implementation of automation, with interoperability and inadequate wireless networks identified as the most significant challenges. Key themes for individuals were workload, cognitive load, communication, acceptance of automation and trust. Task changes and culture were the most predominant issues at the organisational level. Impacts on employment and indigenous communities were highlighted at the community level. The emergence of advanced technologies and interoperability issues have implications for implementing of smart or intelligent mining. Human factors, precisely situation awareness and workload, have far-reaching consequences for safety and productivity because automation is becoming more complex. Moreover, not quantifying community impacts affects how companies can meet their corporate social responsibility commitments. Keywords Automation impacts , mining industry , human factors , natural language processing , autonomous , safety
Mining machinery constitutes essential assets for a mining corporation. Due to economies of scale, technological innovations and stringent quality and safety requirements, the size, complexity, functionality and diversity of industrial machinery have expanded markedly over the last two decades. This growth has increased sensitivity to machine availability and reliability. Mining operations install comprehensive maintenance units tasked with inspection, repair, replacement and inventory management for the machines in use. Leveraging the proliferation of sensor technologies integrated within the machines, maintenance units obtain rich data streams synchronously disclosing machine health and performance metrics, which enables a predictive maintenance programme. This programme performs prognostic detections of anomalies and permits timely intervention to avert catastrophic breakdowns. However, such sensor-driven predictive maintenance scheme for machinery in the mining sector is limited. The present paper utilises the Gaussian process, a powerful predictive modelling technique, to show its potential in addressing this challenge. The efficacy of this approach is validated through three case studies. Each case study is equipped with sensor data and represents a typical predictive maintenance task for mining assets. The developed Gaussian process models successfully capture meaningful temporal patterns in sensor data and generate credible predictions across all three tasks: temporal prediction of sensor data degradation trends, remaining useful lifespan prediction and simultaneous monitoring and prediction of multiple machine conditions. Furthermore, the models offer uncertainty estimates to the prediction outcomes, potentially facilitating maintenance decision-making process.
Optimization of mining projects is often aimed at maximizing the net present value (NPV). Cut-off grade along with production rate determines the quantity and destination of material that is mined and processed. Thus, the cash flows and the NPV of a mining project are directly affected by the cut-off grade, the mineable reserve and the production rate. In order to achieve the maximum NPV, these factors must be evaluated. Block caving is a non-selective mass mining method. In block caving method, as the cut-off grade changes, the amount of mineable reserve, and the correlated mining envelope changes consequently. Determining the optimum cut-off grade and production rate for block cave mining is a complex task, therefore, artificial neural network (ANN) and response surface method (RSM) approaches are utilized in this paper. According to the results, a combination of RSM and ANN models is able to determine the best configuration of cut-off grade and production rate that leads to the maximum NPV.
The method described for production scheduling in this study is a simultaneous use of a clustering algorithm with a genetic algorithm (GA). The aggregating algorithm presented in this study aims to control the concentration of operations and the cluster size, which is evaluated using the Silhouette criterion. The fitness function and the chromosome length in the GA have differences from the usual one. The results showed the number of binary variables in a mixed-integer linear programming model was reduced by 78.5% based on the created clusters. Although the aggregated model's net present value (NPV) is decreased by 7%, the solution time significantly dropped from 3 h to 43.1 s. Also, compared to the non-clustering block model, the aggregated block model's NPV, obtained by GA, was improved.
This study focuses on the thermal behavior of tunnel boring machines (TBMs) through an in-depth investigation into the temperature distribution of their disc cutters. Utilizing the differential quadrature method (DQM), the research conducts a comprehensive numerical analysis to assess the impact of excavation and geological parameters on disc cutter temperature and wear. The accuracy of the DQM model is validated against the finite difference method (FDM), demonstrating comparative results with reduced computational requirements. The findings indicate a significant correlation between disc cutter temperature and various factors, such as rotational speed, spacing, geological conditions, and material strength. Notably, increased spacing or cutter speed leads to higher temperatures and accelerated cutter wear. Moreover, geological factors, particularly rock strength, influence friction coefficients, affecting disc cutter temperatures significantly. For instance, even a slight increase in cutter spacing results in a substantial 65% rise in cutter consumption, underscoring the relevance of these findings for life cycle assessment (LCA) evaluations across diverse geological and environmental conditions in TBM operations.
The stability of the dump slope depends on many parameters, mainly the shear strength of the dump material. Obtaining strength parameters of dump material is vital in dump stability analysis. Dump material consisting of particles of varying sizes, as large as 1000 mm, is typical. It is challenging to obtain the material strength properties of such large particles in the laboratory. Extrapolation of the material strength properties of the modelled samples prepared by parallel gradation technique is used to calculate the dump material strength properties in this study. A closer agreement is found between the predicted and calculated values of the angle of internal friction. Numerical modelling has been carried out with the obtained material properties using RS 2 V9.0, a finite element package, to analyse the stability of a dump slope. It is observed from the results that FOS (factor of safety) increase with an increase in d max (maximum particle size), keeping the shape of the PSD (particle size distribution) curve constant. The paper develops a slope stability analysis methodology considering particle size's effect on slope stability. The proposed method can be used for accuracy and reliable results for slope stability analysis.
Digital twins (DTs) are transforming business operations across industries through accurate replication of physical entities using the Internet of Things and big data analytics. Despite booming progress in the manufacturing, aerospace and buildings sectors, the adoption of DTs in the minerals industry has been slow, and integration with efficient visualisation and user interactions has not been fully optimised to achieve maximum fidelity and usability. One promising avenue for enhancing DT capabilities is the utilisation of extended reality (XR) technologies, which also hold great potential for realising an industrial metaverse where real-world business activities can be conducted in a virtual space. This article proposes a cost-effective and scalable approach to developing a DT with real-time monitoring and control capabilities for a ball mill operation, a widely used processing equipment in the minerals industry. The case study showcases two approaches with different levels of system integration by leveraging serious game development platforms, toolkits and workflows.
The three main optimisation components of sublevel stoping methods are stope layout, production schedule (or stope sequencing) and access networks. The joint optimisation of these components could further add value to an underground mining project. This potential has not been considered in the literature due to computational difficulties, and the problem was solved sequentially. This paper proposes a new joint optimisation model to integrate these components. In addition, the proposed optimisation model incorporates stochastic simulations to capture uncertainty and variability associated with the grades of the related mineral deposits mined. The optimisation model is based on a two-stage stochastic integer programming (SIP) formulation that maximises the project's net present value (NPV) and minimises the planned dilution. Applying the proposed method at a small copper deposit shows that the SIP outperforms the results obtained from mixed integer programming. For a seven-year mine life, the SIP model generated ∼20% more NPV, demonstrating the importance of developing a joint optimisation formulation and accounting for grade uncertainty and variability.
Simultaneous stochastic optimisation frameworks provide a method for optimising long-term production schedules in mining complexes that aim to maximise net present value and manage risk related to supply uncertainty. The uncertainty and local variability related to the quality and quantity of material in the mineral deposits are modelled with a set of stochastic orebody simulations, an input into the simultaneous stochastic optimisation framework. Infill drilling provides opportunities to collect additional information associated with the mineral deposits, which can inform future production scheduling decisions. A framework is developed for optimising infill drilling locations with a criterion that seeks areas that directly affect long-term planning decisions and requires the use of geostatistical simulations. Actor-critic reinforcement learning is applied to identify infill drilling locations in a copper mining complex using this criterion. The case study demonstrates that adapting production scheduling decisions given additional information has the potential to improve the associated production and financial forecasts and identifies a stable area for infill drilling.
Access to persistent computer-generated virtual worlds may provide a powerful tool for conceptualising the mining cycle and managing the domains of exploration, feasibility, planning, design, construction, operations, rehabilitation, decommissioning and closure. Each domain presents a significant challenge to mine operations. Realisation of persistent virtual worlds that can be accessed by many simultaneously may be possible by leveraging Metaverse technologies to produce an ‘always on’ Mining Metaverse based on International Standards and industry collaboration. The realisation of a Mining Metaverse is a complex task because the Metaverse itself has many components and domains that must be managed effectively for it to be sustainable. This article introduces the complexity of the Metaverse components as a taxonomy and was inspired from collaborative work completed by the Standards Australia IT-031 Modelling and Simulation Committee and International Standards Organisation ISO/IEC JTC 1/SC 24 Committee. It is intended as a starting point for the mining industry towards understanding what the Mining Metaverse may be, and effectively embracing and managing this complex emerging technology in the future.
