Mining Technology: Transactions of the Institutions of Mining and Metallurgy

Preliminary truck dispatching involves directing trucks to appropriate destinations before addressing specific optimization objectives such as maximizing ore production or minimizing waiting times. While rule-based systems are commonly used for preliminary dispatching, they lack adaptability to unforeseen scenarios. This study presents an intelligent rule-based system that integrates reinforcement learning to generate labeled data and supervised learning to train a deep neural network on the collected data. A modified Q-learning algorithm, RapidQ, was introduced to expedite the data collection process. The system was implemented in a simulated open-pit mine case study, which incorporated a broader range of dispatching features that are underexplored but essential compared to previous studies. The simulation was designed to handle uncertainties such as weather conditions, blasting needs, truck and shovel failures, and maintenance schedules. When evaluated against a conventional rule-based system, the proposed intelligent dispatching system achieved 10 times fewer incorrect dispatches, 4% fuel savings, a 10% reduction in queuing time, and a 14% increase in ore production. The developed system can be positioned as a potential upper-stage solution in future multi-stage intelligent dispatching systems, complemented by specific dispatching algorithms at the lower stage.

Arman Hazrathosseini

Ali Moradi Afrapoli

Mining Technology Transactions of the Institutions of Mining and Metallurgy

An intelligent rule-based decision-making system for preliminary truck dispatching within open-pit mines

基于智能规则的露天矿卡车预调度决策系统

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.

G Balakrishna Reddy

Alok Kumar Samanta

K Ratna Kumar

Amit Kumar Singh

Reduction of production delay cost in underground coal mines: An FMEA-based method study approach

地下煤矿生产延误成本的降低：基于FMEA方法的研究途径

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.

Morteza Paricheh

Morteza Osanloo

Mehdi Rahmanpour

An analytical approach for evaluation of conveyor exit strategies in open-pit mines

露天矿带式输送机出口策略评估的分析方法

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.

Redouane Oubah

Latifa Ouadif

Youssef Zerradi

Amine Soufi

Anas Driouch

Strip mining in thick overburden context: A review

厚覆盖层条件下露天开采综述

ABSTRACT We present findings from a blast movement monitoring research conducted as a part of mine to mill campaign at the Geita Gold Mine—and additional data from blast monitoring practices at North Mara Gold Mine. These include measurement of the blast-induced rock movement and perimeter translation after blast derived from a blast shot at Nyankanga pit (Geita Gold Mine) and three other shots at the Nyabirama pit (North Mara Gold Mine). Using Blast Movement Technology, 34 movement-monitoring balls installed in 13 blast-monitoring boreholes made possible estimation of the average vertical and horizontal ore movements in the studied blast shots. Recorded ore movement estimates average horizontal and vertical movements of 3.69 and 1.56 m, respectively, at the Nyabirama pit and average horizontal and vertical movements of 4.5 and 2.48 m, respectively, at the Nyankanga pit. Based on the estimated material movements, zones of ore losses and dilution are established and new dig lines are proposed from successful ore block translation in all blast shots.

Stephen Peter Magohe

Nice Shoo

Emilia Mapogha

Crispin Kinabo

Conrad Chambulikazi

Monitoring rock movement and—controlling ore loss and dilution associated with blasting at Geita and North Mara Gold mines, Tanzania

监测坦桑尼亚Geita和North Mara金矿爆破相关的岩石移动及控制矿石损失与稀释

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.

Prosper Chimunhu

Erkan Topal

Mohammad Waqar Ali Asad

Roohollah Shirani Faradonbeh

Ajak Duany Ajak

The future of underground mine planning in the era of machine learning: Opportunities for engineering robustness and flexibility

机器学习时代地下矿山规划的未来：工程鲁棒性与灵活性的机遇

短名
Journal Impact	1.87
国际分区	MINING & MINERAL PROCESSING(Q3)

ISSN	2572-6668, 2572-6676
h-index	35

涉及主题	工程类地质学采矿工程计算机科学化学物理地理数学材料科学生物考古历史有机化学冶金环境科学经济废物管理岩土工程热力学结构工程量子力学煤
出版信息	出版商: SAGE Publications Inc.，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2018，原创研究文献占比: 94.12%，自引率:5.60%， Gold OA占比: 10.17%

Mining Technology: Transactions of the Institutions of Mining and Metallurgy

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An intelligent rule-based decision-making system for preliminary truck dispatching within open-pit mines

Reduction of production delay cost in underground coal mines: An FMEA-based method study approach

An analytical approach for evaluation of conveyor exit strategies in open-pit mines

Strip mining in thick overburden context: A review

Monitoring rock movement and—controlling ore loss and dilution associated with blasting at Geita and North Mara Gold mines, Tanzania

An intelligent rule-based decision-making system for preliminary truck dispatching within open-pit mines

An analytical approach for evaluation of conveyor exit strategies in open-pit mines

Reduction of production delay cost in underground coal mines: An FMEA-based method study approach

Strip mining in thick overburden context: A review

The future of underground mine planning in the era of machine learning: Opportunities for engineering robustness and flexibility