Smart Mining Research Articles

Resilience optimization analysis of smart mining cluster cyber-physical systems based on the NK model

This paper explores the challenges and opportunities presented by the digitization and intelligent clustering in the mining industry, focusing on the security and inter-system influences within Cyber-Physical Systems (CPS) of smart mining clusters. Utilizing the NK model and complex adaptive systems theory, we construct a dual-layered network and cascade failure model, alongside a dynamic repair strategy that adapts to environmental changes. By analyzing the critical parameters of node number (N) and relationship density (K), we assess the resilience of smart mining CPS under cascade failures with dynamic repairs. Our findings reveal that dynamic repair strategies significantly enhance network resilience against both random and deliberate attacks, with N and K acting as crucial regulators of repair effectiveness. This suggests that adapting N and K can improve repair outcomes and system resilience, despite increasing complexity. The paper discusses the practical implications of these strategies through simulation and real-world scenarios, highlighting the need for adaptive repair strategies in enhancing smart mining CPS resilience and process safety. Introducing the NK model to CPS resilience research offers new insights into the resilience of smart mining cyber-physical systems and provides a comprehensive risk analysis method for these systems in complex scenarios.

Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model

Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper.

Investment cost analysis in small hydropower plants in terms of efficiency and a case study in the Euphrates and Tigris basins

Flow rate is the basic parameter for calculating the energy capacity and investment costs of small hydropower plants. Because energy costs are directly related to energy capacity. While the energy capacity increases, the investment cost of the hydropower plant increases also. However, if the energy capacity is high, the energy production will be high and the investment cost will be amortized in a short time. In this study, small rivers in the Euphrates and Tigris Basins with previously determined hydropower capacities were selected, and the investment costs of small hydroelectric power plants to be designed on these rivers were analysed. For this analysis, 10 streams were selected from the Euphrates and Tigris Basins, and only Baskoy and Celebiyan streams have flow observation stations. The flow values of the remaining 8 streams were found by regression analysis using the basin and flow values of Baskoy and Celebiyan streams. Therefore, cost–benefit analyses were carried out separately for these flows in this study. In this study, because it includes the most effective hydrological parameters as well as many technical and economical parameters, the SMART Mini Hydro Tool Program was used.

Exploring microstructure and mechanical features of coupled cementitious tail-sand concrete by partial replacement of tungsten tailings

Tailings storage is not only harmful to the environment, but also poses a great challenge to mine safety production. We consider whether coarse-grained tailings can replace standard sand in concrete to rationalize tailings’ valorization/consumption. Hence, this paper aims practicability of employing tungsten tailings to prepare shotcrete for roadway support and to explore the impact of tailings replacement (TR) rate on strength features and fine structural characteristics of coupled cemented tailings-sand concrete (CCTSC). The tailings weight replacement rate was set at 0 %-100 % with a gradient interval of 20 % in order to carry out the macro-mechanical strength and micro-mechanisms of action studies of CCTSC. Results illustrate that uniaxial compressive strength (UCS) of CCTSCs takes a linear distribution link with tailings’ replacement rate. When TR is 0 % and 100 %, the corresponding UCS of CCTSC is 4.86 MPa and 9.63 MPa, respectively, representing an increase of 98.1 %. A mixture of tensile and tensile-shear damage mainly characterizes the damage modes of CCTSC. The hydration materials of CCTSC are ettringite (AFt) and calcium silicate hydrate (CSH) gel. Hydration products gradually transitioned from Aft to CSH with increasing TR. The conclusions of the present investigation will afford data support for preparation of shotcrete support constituents for tungsten mine tailings, and its practical implementation will also have a major impact on subsequent use of tailings resources as well as low-cost green and smart mining of underground mines.

Predicting mine water inflow volumes using a decomposition-optimization algorithm-machine learning approach

Disasters caused by mine water inflows significantly threaten the safety of coal mining operations. Deep mining complicates the acquisition of hydrogeological parameters, the mechanics of water inrush, and the prediction of sudden changes in mine water inflow. Traditional models and singular machine learning approaches often fail to accurately forecast abrupt shifts in mine water inflows. This study introduces a novel coupled decomposition-optimization-deep learning model that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Northern Goshawk Optimization (NGO), and Long Short-Term Memory (LSTM) networks. We evaluate three types of mine water inflow forecasting methods: a singular time series prediction model, a decomposition-prediction coupled model, and a decomposition-optimization-prediction coupled model, assessing their ability to capture sudden changes in data trends and their prediction accuracy. Results show that the singular prediction model is optimal with a sliding input step of 3 and a maximum of 400 epochs. Compared to the CEEMDAN-LSTM model, the CEEMDAN-NGO-LSTM model demonstrates superior performance in predicting local extreme shifts in mine water inflow volumes. Specifically, the CEEMDAN-NGO-LSTM model achieves scores of 96.578 in MAE, 1.471% in MAPE, 122.143 in RMSE, and 0.958 in NSE, representing average performance improvements of 44.950% and 19.400% over the LSTM model and CEEMDAN-LSTM model, respectively. Additionally, this model provides the most accurate predictions of mine water inflow volumes over the next five days. Therefore, the decomposition-optimization-prediction coupled model presents a novel technical solution for the safety monitoring of smart mines, offering significant theoretical and practical value for ensuring safe mining operations.

Guardian Gear: Advancing Safely with Smart Mining Helmet

Guardian Gear: Advancing Safely with Smart Mining Helmet

A Digital Twin-Based Adaptive Height Control for a Shearer

The shearer is an important component of the smart mining workface, and its effective control is a key aspect that guarantees safe and high-efficient production in coal mines. To address the issue of autonomous height adjustment during the shearer’s cutting process, a self-adaptive speed control method driven by digital twin technology is proposed. A digital twin-based control architecture for the shearer is first established, which consists of physical and the corresponding virtual entities, as well as reality–virtual interaction between them. Based on the mathematic model formulated for height adjustment system of the shearer, an adaptive fuzzy sliding mode controller (AFSMC) with the displacement estimation is designed for the virtual entity, with the purpose of guiding the operation of the corresponding physical entity. Simulation experiments on MATLAB compares the control performance among the proposed method and four comparative ones, including PID controller, integral sliding mode controller (ISMC), feedback linearization controller (FLC), and fuzzy sliding mode controller (FSMC). The experimental results confirm the effectiveness of the proposed AFSMC. More specifically, its steady-state error is 0.024, the maximum absolute control input is 8.43, and the settling time is 1.74 s. This also proves that the digital twin-based control method enables the precise adaptive height adjustment of the shearer, providing potential reference for the intelligent development of a smart mining workface.

Application of deep learning classification model for regional evaluation of roof pressure support evolution effects over time in coal mining face

Hydraulic support leg pressure serves as a crucial indicator for assessing work face support quality. Current evaluation methods for support quality primarily concentrate on static analyses-like inadequate initial support force, pressure overrun, and uneven bracket force-while neglecting dynamic column pressure changes. This paper introduces a model for assessing hydraulic support quality using deep learning techniques. Real-time data is preprocessed into a spatio-temporal pressure sub-matrix sample, which is then inputted into the model. This process assesses the support quality type and characterizes its dynamic evolution within the area. The model facilitates the identification of dynamic support quality effects in the working face area, aiding operators in making targeted adjustments to hydraulic support status. Experimental results revealed that the optimized LeNet-5 network-adjusting parameters like convolutional layer count, kernel size, and ReLU activation function-achieved the highest classification accuracy of 85.25 % for support quality, surpassed other networks. Furthermore, the improved LeNet-5 network outperformed other networks in both F1 score and recall. Additionally, the improved LeNet-5 network achieved faster convergence to the optimal solution, accelerated training speed. This highlighted its advantages in evaluating the spatio-temporal support quality of hydraulic supports in smart mining operations.

Преимущества интеграции автоматизированных систем управления технологическим процессом угольных шахт с системами определения местоположения

Coal mining companies, especially those involved in underground operations, are currently at different stages on their way to digitalization and face a number of challenges, which are described in the article. One group of such problems is the difficulties concerned with the coal mine dispatching control. The paper shows how a properly selected approach, i.e. the integration of various automated process control systems with positioning systems, provides a solution to these problems and offers significant advantages both regarding the industrial safety and maximizing the economic effect. Examples of such integration are given for the conveyor transport control system, for the air and mine gas control systems as well as a CCTV systems with positioning systems within the Smart Mine® complex. An assessment of the current technical basis for the implementation of this approach is given, and the advantages of using a single platform for subsystems of the multi-functional safety system and production management systems are shown.

Conditional generative model with skip-connection structure for low-light image enhancement

Vision-guided coal mine robots often encounter challenges in low-light environments, capturing images marked by poor visibility and significant loss of detail, which complicates the advancement of smart mine safety production. Despite various advancements, achieving high visibility and detailed images through robotic vision sensors remains a formidable task. This paper introduces a conditional generative model incorporating a skip-connection structure to address these issues. We present a novel approach utilizing conditional Generative Adversarial Networks (GNAs) aimed at improving the visibility of image content. This involves an encoder-decoder framework with skip-connections functioning as the generator, and the discriminator, both tailored to enhance image detail. In addition, we implement loss functions integrated with boundary equilibrium constraints to counter the issue of model collapse in conditional GANs, thereby enhancing training stability and image fidelity. Our experimental findings demonstrate that our model delivers competitive results against other state-of-the-art low-light image enhancement models, alongside a standardized training regimen that ensures rapid and consistent convergence.

Sensor Technologies for Safety Monitoring in Mine Tailings Storage Facilities: Solutions in the Industry 4.0 Era

The recent tailings storage facility (TSF) dam failures recorded around the world have concerned society in general, forcing the mining industry to improve its operating standards, invest greater economic resources, and implement the best available technologies (BATs) to control TSFs for safety purposes and avoid spills, accidents, and collapses. In this context, and as the era of digitalization and Industry 4.0 continues, monitoring technologies based on sensors have become increasingly common in the mining industry. This article studies the state of the art of implementing sensor technologies to monitor structural health and safety management issues in TSFs, highlighting advances and experiences through a review of the scientific literature on the topic. The methodology applied in this article adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and utilizes scientific maps for data visualization. To do so, three steps were implemented: (i) a quantitative bibliometric analysis, (ii) a qualitative systematic review of the literature, and (iii) a mixed review to integrate the findings from (i) and (ii). As a result, this article presents the main advances, gaps, and future trends regarding the main characteristics of the sensor technologies applied to monitor TSF structural health and safety management in the era of digitalization. According to the results, the existing research predominantly investigates certain TSF sensor technologies, such as wireless real-time monitoring, remote sensors (RS), unmanned aerial vehicles (UAVs), unmanned survey vessels (USVs), artificial intelligence (AI), cloud computing (CC), and Internet of Things (IoT) approaches, among others. These technologies stand out for their potential to improve the safety management monitoring of mine tailings, which is particularly significant in the context of climate change-related hazards, and to reduce the risk of TSF failures. They are recognized as emerging smart mining solutions with reliable, simple, scalable, secure, and competitive characteristics.

A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process

In the face of the increasing complexity of risk factors in the coal mining transportation system (CMTS) during the process of intelligent transformation, this study proposes a method for analyzing accidents in CMTS based on fault tree analysis (FTA) combined with Bayesian networks (BN) and preliminary hazard analysis (PHA). Firstly, the fault tree model of CMTS was transformed into a risk Bayesian network, and the inference results of the fault tree and Bayesian network were integrated to identify the key risk factors in the transportation system. Subsequently, based on the preliminary hazard analysis of these key risk factors, corresponding rectification measures and a risk control system construction plan are proposed. Finally, a case study was carried out on the X coal mine as a pilot mine to verify the feasibility of the method. The application of this method effectively identifies and evaluates potential risk factors in CMTS, providing a scientific basis for accident prevention. This research holds significant importance for the safety management and decision making of coal mine enterprises during the process of intelligent transformation and is expected to provide strong support for enhancing the safety and reliability of CMTS.

Sora for Smart Mining: Towards Sustainability With Imaginative Intelligence and Parallel Intelligence

Sora for Smart Mining: Towards Sustainability With Imaginative Intelligence and Parallel Intelligence

Algorithm for Point Cloud Dust Filtering of LiDAR for Autonomous Vehicles in Mining Area

With the continuous development of the transformation of the “smart mine” in the mineral industry, the use of sensors in autonomous trucks has become very common. However, the driving of trucks causes the point cloud collected by through Light Detection and Ranging (LiDAR) to contain dust points, leading to a significant decline in its detection performance, which makes it easy for vehicles to have failures at the perceptual level. In order to solve this problem, this study proposes a LiDAR point cloud denoising method for the quantitative analysis of laser reflection intensity and spatial structure. This method uses laser reflectivity as the benchmark template, constructs the initial confidence level template and initially screens out the sparse dust point cloud. The results are analyzed through the Euclidean distance of adjacent points, and the confidence level in the corresponding template is reduced for rescreening. The experimental results show that our method can significantly filter dust point cloud particles while retaining the rich environmental information of data. The computational load caused by filtering is far lower than that of other methods, and the overall operation efficiency of the system has no significant delay.

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.

Smart Mining With Autonomous Driving in Industry 5.0: Architectures, Platforms, Operating Systems, Foundation Models, and Applications

Smart Mining With Autonomous Driving in Industry 5.0: Architectures, Platforms, Operating Systems, Foundation Models, and Applications

The impact of identity conflicts among Chinese knowledge-based miners on unsafe behavior: A moderated chain mediation model.

In the context of smart mine construction, coal mine safety management is imposing stringent requirements on the safety competence of miners. To meet these demands, coal mine enterprises have initiated proactive measures to recruit a new generation of knowledge-based miners who possess high qualifications and specialized backgrounds. This study aims to explore the underlying mechanisms of identity conflict (IC) and its influence on unsafe behavior (USB) among KBMs within the context of China's smart mining initiatives, as well as to identify the role of potential mediating and moderating variables in this relationship. An empirical study was conducted on a sample of 304 KBMs with a college degree or above, selected from multiple coal mines in Shanxi Province, China. Hierarchical regression analysis and the Bootstrap method were employed for data analysis. This model incorporated IC as the antecedent variable and meticulously investigated the mediating effects of role breadth self-efficacy (RBSE) and psychosocial resources (PSR), along with the moderating effect of positive explanatory bias (PEB). The findings indicate a significant positive correlation between IC and USB among KBMs. RBSE and PSR were found to mediate the relationship between IC and USB. Moreover, this mediating effect further influenced the relationship between IC and USB through a chain mediating effect. Additionally, PEB strengthened the positive impact of KBMs' RBSE on PSR. These results provide a broader perspective on the antecedent variables associated with KBMs' USB and provide valuable insights and practical management strategies enhancing the safety management practices within coal mining enterprise.

Monitoring and Controlling System of Smart Mini Greenhouse Based on Internet of Things (IoT) for Spinach Plant (Amaranthus sp.)

Greenhouses, often composed of plastic or glass structures, play a fundamental role in optimizing the environmental conditions essential for successful plant cultivation, ultimately resulting in higher-quality crop yields. The study aims to develop a real-time monitoring and control system for a smart mini greenhouse utilizing the Internet of Things (IoT), with a specific focus on cultivating spinach. The monitoring system is constructed around an ESP32 microcontroller, complemented by a DHT22 sensor for accurate air temperature and humidity measurements, and an RTC DS3231 timer for scheduling tasks. The DHT22 sensor's set point values trigger the fan and misting system operations. The fan activates when the air temperature reaches 32°C, while the misting system turns on when humidity levels (RH) reach 55%. The ESP32 is the central processing unit, enabling internet connectivity through Wi-Fi for real-time data monitoring via the Blynk application. Sensor calibration confirms the system's precision, with regression analyses producing impressive R2 values of 0.989 and 0.952. The shading net control system operates four hours daily, from 11:00 to 15:00 WIB (Western Indonesian Time), optimizing the greenhouse environment. This well-executed system leads to the robust growth of spinach plants, reaching a height of 23.02 cm, sprouting nine leaves, and attaining a weight of 10 grams. The findings from this study highlight the efficiency and effectiveness of the smart mini greenhouse's monitoring and control system, offering promising applications in broader greenhouse management and crop cultivation practices.

MiningLLM: Towards Mining 5.0 via Large Language Models in Autonomous Driving and Smart Mining

MiningLLM: Towards Mining 5.0 via Large Language Models in Autonomous Driving and Smart Mining

Pametno rudarstvo

Industry 4.0 has become a global strategic initiative of scientific and technological development, which was launched by the most developed industrial countries in the world, and today has gone the furthest in its implementation. In essence, it is a new model of application of information and communication technology, based primarily on their integration with the object of application (for example in mining - production (BTO), transport, maintenance, etc.). The development and application of the elements of Industry 4.0 in this area leads to the establishment of the smart mining (SM) model, the elements of which will be presented in this paper. As an illustration of the application of this model in our conditions, and for the "Drmno" surface mine, this paper will present a model of planning and maintenance management of ERP (production-technological resource planning) and MES (work order management) model for Industry 4.0 , which uses a private cloud computing model.