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.
