Intelligent Mining Research Articles

Research Status and Development Trend of Coal Spontaneous Combustion Fire and Prevention Technology in China: A Review.

Coal seam spontaneous combustion fire is not only one of the main forms of the five major mine disasters, but also the main cause of secondary disasters such as mine gas and coal dust explosions. In recent years, with the advancement of mechanization, automation, and intelligent mine construction, spontaneous coal fires in mines have presented a series of new characteristics, and the prevention and control of spontaneous coal fires are also facing new challenges. On the basis of literature research, this paper summarizes and discusses the basic theory of coal spontaneous combustion, monitoring and early warning methods, and prevention and control technology, summarizes the development process of coal spontaneous combustion theory, reviews the research progress of coal spontaneous combustion monitoring and early warning methods and prevention and control technologies, and discusses the future development direction. In terms of the basic theory of spontaneous combustion of coal, from the initial hypothesis of spontaneous combustion of multielement coal to the unified understanding of coal-oxygen composite theory, a complete set of theoretical systems have been established, and a lot of macro and micro studies have been carried out and analyzed from multiple perspectives. In terms of coal spontaneous combustion monitoring and early warning, from the initial single indicator gas early warning to the construction of gas index system, the hierarchical early warning system is studied, and gradually tends to be perfect. With the development of automation and intelligence technology, the monitoring of coal spontaneous combustion disasters has also formed a new monitoring technology with beam tube monitoring as the traditional method, distributed optical fiber, wireless AD hoc network temperature measurement, and a coal spontaneous combustion multiparameter wireless monitoring system. In terms of fire prevention and control, the traditional "prevention" and "treatment" have changed to the "prevention-control-extinction" technical system based on hierarchical early warning, and the focus has gradually shifted to "prevention", and a large number of antifire materials have been developed, including blocking materials and fire-fighting materials. However, the precise inhibition and control of coal spontaneous combustion disasters, the evolution model of coal spontaneous combustion under the conditions of multifactor coupling in the field, the reliability and stability of intelligent monitoring system, the noncontact detection method of fire source, and the collaborative adaptation of multiple prevention and control techniques are not yet clear. In the future development, the mechanism of spontaneous coal combustion and its evolution process and other basic theories should be deeply studied. On the basis of the mechanism optimization early warning method of spontaneous coal combustion process, flame retardant and fire prevention materials should be targeted and developed. On the basis of the spatiotemporal evolution of spontaneous coal combustion, monitoring and monitoring system equipment with high speed, high precision, and high stability should be developed, which should accelerate the realization of accurate dynamic sensing and intelligent early warning of coal spontaneous combustion, and form an active hierarchical collaborative prevention and control system based on the trinity of "prevention-control-extinction" of coal spontaneous combustion. The conclusions and prospects of this paper can be used for reference in the future research direction, and have a certain role in promoting the exchange of research results of coal science and technology workers.

Research on Large Language Model for Coal Mine Equipment Maintenance Based on Multi-Source Text

The efficient management and utilization of coal mine equipment maintenance knowledge is an indispensable foundation for advancing the establishment of intelligent mines. This knowledge has problems such as scattered, low sharing, and insufficient management, which restricts the development of coal mine intelligence. For the above-mentioned problems, a large language model for the maintenance of coal mine equipment based on multi-source text (XCoalChat) was proposed to better manage and utilize the existing massive knowledge of coal mine equipment maintenance. The dataset of coal mine equipment maintenance based on ReliableCEMK-Self-Instruction was constructed to obtain a wide and diverse amount of knowledge through sample generation. Aiming at the illusory problem of the large language model, a knowledge graph enhancement method based on the “Coal Mine Equipment Maintenance System—Full Life Cycle—Specification” was proposed to improve the knowledge density. A triple-LoRA fine-tuning mechanism and DPO direct preference optimization method were introduced into the top of the baseline model, which guarantees that XCoalChat can handle multiple Q&A and maintenance decision analysis tasks with limited computing power. Compared with ChatGLM, Bloom, and LLama, the comprehensive assessment of XCoalChat was performed by experiments including coal mine dialog consulting, coal mine professional consulting, and maintenance decision analysis. The results showed that XCoalChat achieved the best response accuracy in professional consulting and maintenance decision analysis; XCoalChat also took the least reasoning time on average. XCoalChat outperformed other mainstream large language models, which verify that XCoalChat is an effective large language model in the field of coal mine equipment maintenance.

Intelligent mining methodology of product field failure data by fusing deep learning and association rules for after-sales service text

The after-sales service text contains wealthy field failure data, which can be used to estimate the field reliability of product and its components. However, human-based data mining suffers from low efficiency and unstable quality. This study proposes an innovative intelligent mining methodology that combines the product failure dictionary (PFD), gate recurrent unit (GRU) and association rules (AR) to solve the problem. Firstly, the text is studied, and the segmented mining process is developed. Secondly, the PFD is edited according to the analysis results of a large product failure sample, and combined with GRU to construct the PFD-GRU efficient mining model. Thirdly, based on the failure determination criteria and the dependency syntax analysis results of the text, the AR-based mining method is formulated for re-mining the text that does not satisfy the determination threshold of the PFD-GRU model to establish the PFD-GRU-AR intelligent mining methodology. The case study results of the after-sales service text mining for four types of air-conditioner show that the segmented mining pattern can obtain better quality mining results than the one-stage mining pattern, and the P,R, and F1 of the PFD-GRU model have increased by at least 1.48%, 1.72% and 1.6% respectively compared to the corresponding indicator values of the GRU model. With the increase of the threshold value, the quality and robustness are improved and the mining results tend to be stable. As threshold value is 0.95, the PFD-GRU-AR methodology obtains minimum value for P,R, and F1 as 98.54%, 99.09% and 98.81% respectively.

Extracting unstructured roads for smart Open-Pit mines based on computer vision: Implications for intelligent mining

The mining industry is rapidly advancing towards automation and intelligence, with smart mines emerging as a future trend. Open-pit mining areas are semi-enclosed, and roads are essential for unmanned trucks to perceive the mining environment and execute various production tasks. The dynamic nature of open-pit mines, driven by production progress, leads to frequent alterations in roadways. As a consequence, roads become unstructured, with indistinct edges that easily blend into the surrounding mine environment. This poses a challenging operational environment for unmanned vehicles. To address this challenge in the realm of intelligent mining, this study establishes a dataset of mining roads based on different rock types and proposes an unstructured road segmentation method for mines by integrating residual networks, Contrast Limited Adaptive Histogram Equalization (CLAHE), and the Efficient Channel Attention (ECA) mechanism. This method is applied to four semantic segmentation networks: FCN, UNet, PSPNet, and DeepLab v3 +. The dataset and network model undergo validation using a specific hybrid loss function and relevant evaluation metrics. The results show that the established road dataset has good applicability, with an ablation experiment confirming the effectiveness of the added modules. This study introduces a new perspective for advancing unmanned driving in smart mines.

Automatic Control of Low Voltage Load in Power Systems Based on Deep Learning

Due to the interference of false data, there is a large error in the mining results of low voltage loads in the power system. In response to this problem, the author proposes a design of an intelligent mining system for low voltage loads in the power system based on deep learning. Using ARM+DSP dual CPU structure, initializing the adapter agent, and using dual arm spiral antennas, designing a low-voltage load monitor to detect partial discharge signals in the 500-1500 MHz frequency band and suppress noise interference; By transmitting monitoring information to the intelligent switch through CAN bus or 485 bus, remote monitoring can be achieved; Based on the contact points and current characteristics of the circuit breaker, a current transformer has been designed to reduce the range of induced voltage variation; Construct a continuous set of functions MMD in the space, adjust the original network structure, establish a deep learning mining model, initial network parameters, eliminate false data in the network, optimize the network using target domain data, and combine mining engines to achieve intelligent data mining. According to the experimental results, the maximum difference between the load of phase A of the data processing system based on numerical simulation and the actual data is 1000 kVA at a time of 6 seconds; When the load of phase B is 4 seconds, the maximum difference between it and the actual data is 2000 kVA; When the load of phase C is 8 seconds, the maximum difference between it and the actual data is 2000 kVA. It has been proven that the mining error of the system is 0, and it has a precise mining effect.

Intelligent mine safety risk based on knowledge graph: hotspots and frontiers.

The safety of mining has always been a concern. The occurrence of safety accidents not only endangers human health, but also causes serious damage to the ecological environment. With the continuous upgrade and improvement of mining technology, most mines are undergoing intelligent construction and transformation. In order to analyze security risks that should be focused on the construction of intelligent mines and the technical challenges that will be faced, we used the Web of Science (WOS) Core Collection to identify 283 publications on the field of security risks in intelligent mines from 2013 to 2022. We combined the Vosviewer, CiteSpace, and Bibliometrix R software packages to conduct an in-depth analysis and exquisite visualization of the literature, including the authors, journals, countries, hot topics, and research frontiers. This paper can help scholars comprehensively and quickly understand the research status and hotspots in the field of intelligent mine safety and risk, and it provides theoretical support for further research and exploration in the future.

Research on intelligent identification algorithm of coal and rock strata based on Hilbert transform and amplitude stacking

AbstractThe high precision identification of coal–rock layers is a significant challenge in intelligent mining. There is a large amount of electromagnetic noise and metal reflector signals in the full space detection environment of mining roadway, which makes it hard to distinguish the reflected waves at interface from a set of echo signals generated by the interface due to the similar amplitudes among them. So the method of identifying layers solely based on amplitude characteristics has poor stability and accuracy in coal mining environments. This paper proposes a method for identifying coal–rock layers based on Hilbert transform and tracking–scanning–stacking technology. There are two steps to achieve the recognition of air–coal–rock interfaces. First, by analysing the instantaneous amplitude spectrum obtained from the Hilbert transform, the first extreme point that is always the maximum value within a wavelength range is determined as the rough position of the air–coal interface. To solve the problem of recognition errors caused by noise and energy dispersion, the density difference method is used to remove discrete points. Second, the precise position of the air–coal interface is determined by tracking the extreme points within the 1.5 wavelength range around the rough position, and using the amplitude stacking method to quantitatively analyse and compare the degree of energy concentration. The data between zero time and the reflected waves at the air–coal interface is removed to avoid the impact of them on the recognition of the coal–rock interface. Results of physical model experiments and actual coal mine experiments show that this method yields better results and has high stability compared to conventional recognition method. Moreover, the average relative thickness errors are 4.5% for air layer and 4.2% for coal layer.

Real-Time Monitoring of Underground Miners’ Status Based on Mine IoT System

Real-time monitoring and timely risk warnings for the safety, health, and fatigue of underground miners are essential for establishing intelligent mines, enhancing the safety of production, and safeguarding the well-being of miners. This concerns the collection, transmission, and processing of relevant data. To minimize physical strain on miners, data collection functions are consolidated into two wearable terminals: an electronic bracelet equipped with reliable, low-power components for gathering vital sign data and transmitting them via Bluetooth and a miner lamp that integrates multi-gas detection, personnel positioning, and wireless communication capabilities. The gas sensors within the miner lamp undergo regular calibration to maintain accuracy, while the positioning tag supports round-trip polling to ensure a deviation of less than 0.3 m. Data transmission is facilitated through the co-deployment of 5G communication and UWB positioning base stations, with distributed MIMO networking to minimize frequent cell handovers and ensure a low latency of no more than 20 ms. In terms of data processing, a backpropagation mapping model was developed to estimate miners’ fatigue, leveraging the strong correlation between saliva pH and fatigue, with vital signs as the input layer and saliva pH as the output layer. Furthermore, a unified visualization platform was established to facilitate the management of all miners’ states and enable prompt emergency response. Through these optimizations, a monitoring system for underground miners’ status based on mine IoT technology can be constructed, meeting the requirements of practical operations.

Research on Coal and Gangue Recognition Based on the Improved YOLOv7-Tiny Target Detection Algorithm.

The recognition technology of coal and gangue is one of the key technologies of intelligent mine construction. Aiming at the problems of the low accuracy of coal and gangue recognition models and the difficult recognition of small-target coal and gangue caused by low-illumination and high-dust environments in the coal mine working face, a coal and gangue recognition model based on the improved YOLOv7-tiny target detection algorithm is proposed. This paper proposes three model improvement methods. The coordinate attention mechanism is introduced to improve the feature expression ability of the model. The contextual transformer module is added after the spatial pyramid pooling structure to improve the feature extraction ability of the model. Based on the idea of the weighted bidirectional feature pyramid, the four branch modules in the high-efficiency layer aggregation network are weighted and cascaded to improve the recognition ability of the model for useful features. The experimental results show that the average precision mean of the improved YOLOv7-tiny model is 97.54%, and the FPS is 24.73 f·s-1. Compared with the Faster-RCNN, YOLOv3, YOLOv4, YOLOv4-VGG, YOLOv5s, YOLOv7, and YOLOv7-tiny models, the improved YOLOv7-tiny model has the highest recognition rate and the fastest recognition speed. Finally, the improved YOLOv7-tiny model is verified by field tests in coal mines, which provides an effective technical means for the accurate identification of coal and gangue.

Coal–Rock Data Recognition Method Based on Spectral Dimension Transform and CBAM-VIT

Coal–gangue sorting is a vital component of intelligent mine construction. As intelligent manufacturing continued to advance, data-driven coal–gangue recognition emerged as a prominent research topic. However, conventional data-driven methods for coal–gangue recognition heavily rely on expert-extracted features. The process of feature extraction is labor-intensive and significantly impacts the final outcome. Deep learning (DL) offers an effective approach to automatically extract features from raw data. Among the various DL techniques, convolutional neural networks (CNNs) have proven to be particularly effective. In this paper, we propose an intelligent method for recognizing coal–rock by fusing multiple preprocessing techniques applied to near-infrared spectra and employing dual attention. Initially, a signal-to-RGB image conversion method is applied to fuse three types of preprocessing data, namely first-order differential, second-order differential, and standard normal transform, into an RGB image representation. Subsequently, we propose a neural network model (CBAM-VIT) that integrates the convolutional block attention mechanism (CBAM) and Vision Transformer (VIT). When evaluated on the coal–rock dataset, this model achieves an accuracy of 98.5%, surpassing the performance of VIT (95.3%), VGG-16 (89%), and AlexNet (82%). The comparative results clearly demonstrate that the proposed coal–gangue recognition method yields significant improvements in classification outcomes.

Decision support system for cost planning and implementation of production tasks of LW “Bogdanka” S.A. – the concept of integrating IT solutions towards the idea of an intelligent mine

The article presents the concept and specific activities undertaken by LW “Bogdanka” S.A. to build an IT system that integrates the current dispatching solutions of the mine with IT systems supporting the management of basic underground infrastructure facilities. This concept forms the substantive foundation of a solution that supports the decision-making system for conducting production activities using IT solutions and production monitoring, implemented under the slogan “Intelligent Mine”. The Intelligent Mine, as understood by LW “Bogdanka” S.A., involves a series of innovative technical solutions aimed at increasing mining effectiveness while ensuring the safety of underground workers and minimizing the environmental impact. The article addresses several key issues, including the practical experience of LW “Bogdanka” S.A. in innovative deposit management, scheduling access, preparatory and exploitation works, production resources management, and monitoring of production processes. Additionally, the article explores how these integrated IT systems contribute to improving operational efficiency, enhancing safety protocols, and promoting sustainable mining practices. By providing a comprehensive overview of these initiatives, the article highlights the significant advancements and practical implementations that LW “Bogdanka” S.A. has achieved in its journey toward becoming an Intelligent Mine.

Non-explosive mechanized and intelligent mining/heading in underground mine

The non-explosive mechanized mining and heading is an alternative approach to traditional drilling and blasting method for excavating the deep hard rock, the intellectualized upgrade of which includes smart perception, wise decision and intelligent control. Based on the comprehensive analyses for two key problems (rock breakage and rock stability) and three key factors (environmental conditions, rock properties and excavation parameters), the process of non-explosive mechanized and intelligent mining and heading was established, which included in-situ monitoring, cuttability improvement, intelligent control and performance evaluation to achieve mechanization, automation and unmanned fully-automation for equipment, continuation, refinement and collaboration for process, and informatization, digitization and smartization for management. In addition, the PDCA (Plan−Do−Check−Adjust) cycle and its associated sub-cycles were established to manage the process of non-explosive mechanized and intelligent mining and heading. Through the cyclic managements for deep high stress induced utilization and energy regulation, mutagenesis modification for risk reduction and cuttability improvement of hard rock, and multi-source rock breakage, the safe and efficient mining and heading in underground mine can be achieved.

Computer detection intelligent mining algorithm for software defined networks

The network security (NS) issues of software defined network (SDN) are becoming increasingly prominent. Traditional computer detection (for convenience, computer detection is abbreviated to CD) methods are usually based on rules and features, and cannot cope with new threats and unknown attacks. The purpose of this article is to analyze a CD intelligent mining algorithm (for convenience, intelligent mining algorithm is abbreviated to IMA) suitable for SDNs, and to optimize and improve the data acquisition difficulty, large data volume, dynamism, timeliness, and security issues of SDNs. On the basis of data preprocessing on the KDD (Data Mining and Knowledge Discovery) Cup 1999 dataset, this article constructed an architecture for CD IMAs. In the data intelligence layer, a combination of k-means algorithm and XGBoost (eXtreme Gradient Boosting) algorithm was used for computer network attack detection based on stream classification. Based on the preprocessed dataset, the k-means algorithm and its combination were trained and simulated. The effectiveness and performance optimization of the CD IMA analyzed in this article were verified by comparing it with traditional computer attack detection algorithms in terms of detection accuracy, CPU (Central Processing Unit) occupancy, detection time, and false detection rate. The experimental results showed that the accuracy of the CD IMAs analyzed in this article was over 80 %, and some even reached over 90 %. However, the accuracy of traditional computer attack detection algorithms has not reached 90 %. The detection time of traditional computer attack detection algorithms was all above 2 s, but some of the detection time of the algorithm in this article was within 2 s. The detection rate of the algorithms analyzed in this article was above 90 %, and the detection results were more accurate than traditional algorithms. This proved that the algorithm analyzed in this article can more effectively select optimized feature subsets, has good flow classification ability, and has high detection accuracy for different types of attacks. The CD IMA for SDNs has great potential and application prospects.

Sustainable development of the mining industry in the context of digital transformation

The article considers the processes and prospects of the mining industry sustainable development. The main results of the study include the substantiation of the introduction of digital transformation processes as the main global trend of the mining enterprises sustainable development, along with the use of high-tech innovative approaches. The presented study considers the impact of the digital technological revolution “Industry 4.0” on the prospects for the of the mining industry development. The comprehensive analysis of the state, problems, advantages and disadvantages of the mining enterprises functioning in the context of constantly changing external and internal factors of economic and foreign policy development of modern Russia has been carried out. The factors determining the relevance of the digital transformation of mining enterprises, promising directions for the development of geotechnology in the 21st century, are considered, using the example of the introduction of intelligent Mining 4.0. (Mining 4.0.) and the construction of intelligent digital mines. The mechanisms and digital solutions, which implementation will solve thef key tasks, are considered. The practical application of the research results will allow mining enterprises to reach a new sustainable level of the development as well as to increase the competitiveness in the global market.

Research on the Coordinated Development of Intelligent Mining Technology and Green Ecological Restoration in Open-Pit Coal Mines

Research on the Coordinated Development of Intelligent Mining Technology and Green Ecological Restoration in Open-Pit Coal Mines

Key technologies for intelligent mining of difficult-to-mine coal seams

Key technologies for intelligent mining of difficult-to-mine coal seams

Overview of the faults and diagnosis methods of the hydraulic system of modern coal mining machines

The high power, high efficiency, low failure rate, and intelligence of coal mining equipment are important directions for the development of coal mining machines both domestically and internationally. The implementation of efficient and low failure coal mine equipment is an important prerequisite for ensuring the smooth deployment of intelligent mines. At present, coal mining equipment generally consists of three parts: mechanical, electrical, and hydraulic. However, hydraulic pressure is a necessary system for coal mining equipment to complete high-power and intelligent operations. This article will combine the author's years of work experience to summarize the current local faults and diagnostic methods of hydraulic systems in coal mine equipment.

IMAPeM: A New Paradigm for Implementing Intelligent Mining With Humans in the Loop

iMAPeM: A New Paradigm for Implementing Intelligent Mining With Humans in the Loop

Current state and prospects for digitalization of the Russian coal industry

Background. Innovative robotic systems for mineral extraction and processing are increasingly changing the core operational principles of mining enterprises, thus making the creation of an intelligent mining enterprise a reality.Aim. To develop a set of proposals for promoting the digitalization of the coal industry in Russia by carrying out an analysis of the current state of implementation of innovative equipment and digital technologies within the Industry 4.0 state initiative in Russian coal companies.Materials and methods. The research was conducted using the methods of systems approach, value chain theory, expert evaluation, technological foresight, logical and comparative analysis, economic and statistical processing of information, including its tabular interpretation. The research materials included the data of coal companies, JSC “Rosinformugol”, Internet sources, and open publications.Results. The proposal is made to develop five groups of key technological digital industry platforms(TP), as follows: 1) TP “Prospecting and exploration works”; 2) TP “Underground mining technologies”; 3) TP “Open-pit mining technologies”; 4) TP “Technologies for processing coal and industrial waste”; 5) TP “Technologies for transporting rock mass”. It is proposed to implement the innovative digital potential of the coal industry in two stages, thereby completing the structural and digital transformation of the coal industry.Conclusion. The proposed TPs of the Russian coal industry should solve the following tasks:determination of the basic requirements and functional properties of the Russian coal industry based on the Industry 4.0 state initiative, along with the principles of their implementation;determination of the main technological directions for the main sectors (processes) of technological development of the coal industry, including prospecting and exploration; underground and open-pit coal mining; upgrading and processing of coal and industrial waste; transportation of coal;identification of the main components, technologies, information and management solutions in all the above processes;diversification of the activities of coal mining companies in the creation of products with high added value;overcoming the technological gap and import dependence by implementing phased import substitution and innovative development of the Russian coal industry and mining engineering with the achievement of technological sovereignty.The research results can be used when creating a unified network of scientific and educational centers on the principles of public-private partnership for the development and industrial implementation of innovative technologies in the coal, mining, and exploration industries, as well as in the training of highly-qualified personnel on the basis of Sergo Ordzhonikidze Russian State University for Geological Prospecting.

Social Intelligence Mining: Unlocking Insights from X

Social trend mining, situated at the confluence of data science and social research, provides a novel lens through which to examine societal dynamics and emerging trends. This paper explores the intricate landscape of social trend mining, with a specific emphasis on discerning leading and lagging trends. Within this context, our study employs social trend mining techniques to scrutinize X (formerly Twitter) data pertaining to risk management, earthquakes, and disasters. A comprehensive comprehension of how individuals perceive the significance of these pivotal facets within disaster risk management is essential for shaping policies that garner public acceptance. This paper sheds light on the intricacies of public sentiment and provides valuable insights for policymakers and researchers alike.