Search Results (43)

When using supplementary cementitious materials to replace cement partially, the carbon emissions of cement products can be reduced, but it often leads to reduced strength. This study explores the application potential of carbide slag (CS) and calcined coal gangue (CCG), byproducts of acetylene production, to partially replace cement. The effects of these two materials on the macroscopic properties and microstructure of cement-based materials were analyzed through systematic experiments. The compressive strength, ultrasonic pulse velocity, and electrical resistivity test results showed that replacing 20% of cement with CCG did not cause significant changes in the test results of the specimens. An X-ray diffraction (XRD) analysis showed that these two materials can produce additional hydration products. Scanning electron microscopy images (SEM) further revealed that CCG produces hydration products to fill microscopic pores. Thermogravimetric analysis (TG) results after 28 days showed that with the addition of supplementary cementitious materials, calcium hydroxide (CH) in CS reacts with CCG, resulting in the consumption of CS. Finally, the environmental impact of CS and CCG was assessed. It was found that CS is more favorable for reducing carbon emissions compared to CCG. However, when considering the effect of cement replacement on compressive strength, combining these two materials is more advantageous for sustainable development. Overall, the use of CS and CCG demonstrated good performance in promoting sustainable development. Full article

Coal gangue, the primary bulk solid waste generated during coal utilization, requires decarbonization and the enrichment of valuable components such as calcium and magnesium through methods like hydrocyclone separation for comprehensive utilization. This study observed the free-settling behavior of coal gangue particles using a high-speed dynamic image analysis system and analyzed their kinematic characteristics to guide the hydrocyclone separation process. The results indicate that particle size and density significantly influence settling behavior. Fine-grained, low-density particles exhibited more pronounced directional deflection and velocity fluctuations, while high-density coarse particles demonstrated higher settling velocities. Based on terminal velocity, the drag coefficient of fluid resistance acting on particles was calculated. The findings show that high-density coarse particles have larger drag coefficients, likely due to fluid disturbances and the hydrophobic nature of particle surfaces. Additionally, the mechanical properties of settling motion were analyzed, indicating that gravity dominates the settling process of coarse particles, while fine particles are subjected to relatively balanced forces. Furthermore, density variations primarily affect hydrodynamic drag, which is related to the surface properties of particles. Therefore, enhancing the centrifugal force field through cyclone structural optimization is necessary to improve separation precision for fine coal and gangue particles. Full article

The accurate detection of coal gangue is an important prerequisite for the intelligent sorting of coal gangue. Aiming at existing coal gangue detection methods, which have problems such as low detection accuracy and complex model structure, a multi-scale fusion lightweight coal gangue target detection method based on the EMBS-YOLOv8s model is proposed. Firstly, the coal gangue images collected through the visual dark box platform are preprocessed using CLAHE to improve the contrast and clarity of the images. Secondly, the PAN-FAN structure is replaced by the EMBSFPN structure in the neck network. This structure can fully utilize the features of different scales, improve the model’s detection accuracy, and reduce its complexity. Finally, the CIoU loss function is replaced by the Wise-SIoU loss function at the prediction end. This improves the model’s convergence and stability and solves the problem of the imbalance of hard and easy samples in the dataset. The experimental results show that the mean average precision of the EMBS-YOLOv8s model on the self-constructed coal gangue dataset reaches 96.0%, which is 2.1% higher than that of the original YOLOv8s model. The Params, FLOPs, and Size of the model are also reduced by 29.59%, 12.68%, and 28.44%, respectively, relative to those of the original YOLOv8s model. Meanwhile, the detection speed of the EMBS-YOLOv8s model is 93.28 f.s⁻¹, which has certain real-time detection performance. Compared with other YOLO series models, the EMBS-YOLOv8s model can effectively avoid the occurrence of false detection and missed detection phenomena in complex scenes such as low illumination, high noise, and motion blur. Full article

To address the challenges of low accuracy in coal gangue image recognition and poor segmentation performance under the influence of dust in underground coal mines, a scaled simulation platform was constructed to replicate the longwall top coal caving face. This platform utilized real coal gangue particles as the raw material and employed dust simulation to mimic the dust conditions typically found in coal mines. Images of coal gangue without dust and under varying dust concentrations were then collected for analysis. In parallel, an improved DeeplabV3+ coal gangue image segmentation model is proposed, where ResNeSt is employed as the backbone network of DeeplabV3+, thereby enhancing the model’s capability to extract features of both coal and gangue. Furthermore, two channel attention modules (ECAs) are incorporated to augment the model’s ability to recognize edge features in coal gangue images. A class-label smoothing training strategy was adopted for model training. The experimental results indicate that, compared to the original DeepLabV3+ model, the optimized model achieves improvements of 3.14%, 4.70%, and 3.83% in average accuracy, mean intersection over union (mIoU), and mean pixel accuracy, respectively. Furthermore, the number of parameters was reduced from 44.18 M to 43.86 M, the floating-point operations decreased by 8.33%, and the frames per second (FPS) increased by 45.03%. When compared to other models such as UNet, PSANet, and SegFormer, the proposed model demonstrates superior performance in coal gangue segmentation, accuracy, and parameter efficiency. A method combining dark channel prior and Gaussian weighting was employed for defogging coal gangue images under varying dust concentration conditions. The recognition performance of the coal gangue images before and after defogging was assessed across different dust concentrations. The model’s segmentation accuracy and practical applicability were validated through defogging and segmentation of both indoor and underground dust images. The recognition accuracy of coal and gangue, before and after defogging, improved by 6.8–71.8% and 5.8–45.8%, respectively, as the dust concentration increased, thereby demonstrating the model’s effectiveness in coal gangue image defogging segmentation in underground dust environments. Full article

The efficient separation of coal and gangue remains a critical challenge in modern coal mining, directly impacting energy efficiency, environmental protection, and sustainable development. Current machine vision-based sorting methods face significant challenges in dense scenes, where label rewriting problems severely affect model performance, particularly when coal and gangue are closely distributed in conveyor belt images. This paper introduces CGDet (Coal and Gangue Detection), a novel compact convolutional neural network that addresses these challenges through two key innovations. First, we proposed an Object Distribution Density Measurement (ODDM) method to quantitatively analyze the distribution density of coal and gangue, enabling optimal selection of input and feature map resolutions to mitigate label rewriting issues. Second, we developed a Relative Resolution Object Scale Measurement (RROSM) method to assess object scales, guiding the design of a streamlined feature fusion structure that eliminates redundant components while maintaining detection accuracy. Experimental results demonstrate the effectiveness of our approach; CGDet achieved superior performance with AP50 and AR50 scores of 96.7% and 99.2% respectively, while reducing model parameters by 46.76%, computational cost by 47.94%, and inference time by 31.50% compared to traditional models. These improvements make CGDet particularly suitable for real-time coal and gangue sorting in underground mining environments, where computational resources are limited but high accuracy is essential. Our work provides a new perspective on designing compact yet high-performance object detection networks for dense scene applications. Full article

Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. In response to the above, this article proposes a new “hardware friendly” coal gangue image recognition algorithm, RRBM-YOLO, which is combined with dark light enhancement. Specifically, coal gangue image samples were customized in two scenarios: normal lighting and simulated underground lighting with poor lighting conditions. The images were preprocessed using the dim light enhancement algorithm Retinexformer, with YOLOv8 as the backbone network. The lightweight module RepGhost, the repeated weighted bi-directional feature extraction module BiFPN, and the multi-dimensional attention mechanism MCA were integrated, and different datasets were replaced to enhance the adaptability of the model and improve its generalization ability. The findings from the experiment indicate that the precision of the proposed model is as high as 0.988, the mAP@0.5(%) value and mAP@0.5:0.95(%) values increased by 10.49% and 36.62% compared to the original YOLOv8 model, and the inference speed reached 8.1GFLOPS. This indicates that RRBM-YOLO can attain an optimal equilibrium between detection precision and inference velocity, with excellent accuracy, robustness, and industrial application potential. Full article

To address the low recognition accuracy of models for coal gangue images in intelligent coal preparation systems—especially in identifying small target coal gangue due to factors such as camera angle changes, low illumination, and motion blur—we propose an improved coal gangue separation model, Yolov8n-improvedGD(GD—Gangue Detection), based on Yolov8n. The optimization strategy includes integrating the GCBlock(Global Context Block) from GCNet(Global Context Network) into the backbone network to enhance the model’s ability to capture long-range dependencies in images and improve recognition performance. The CGFPN (Contextual Guidance Feature Pyramid Network) module is designed to optimize the feature fusion strategy and enhance the model’s feature expression capabilities. The GSConv-SlimNeck architecture is employed to optimize computational efficiency and enhance feature map fusion capabilities, thereby improving the model’s robustness. A 160 × 160 scale detection head is incorporated to enhance the sensitivity and accuracy of small coal and gangue detection, mitigate the effects of low-quality data, and improve target localization accuracy. Full article

Coal gangue, as a subgrade filler, is of great significance for the sustainable development of the economy, society, and environment. Particle crushing tests were conducted on coal gangue coarse-grained subgrade filler (CGSF) under uniaxial compression conditions, and the relationships between load and displacement, crushing strength, failure pattern, and gradation after crushing were analyzed. A new visual analysis method for the crushing patterns of particles was provided through image analysis, and a new gradation equation based on the traditional fractal model was proposed to describe the crushed particles. The results indicate that as the particles are gradually compressed the sharp corners of particles are gradually crushed and fall off, causing the relationship curve between load and displacement to fluctuate and grow, and particle splitting failure leads to the approximately linear growth curve. Moreover, the distribution of particle crushing strength for coal gangue is between 3.02 and 11.11 MPa, and the crushing probability and the applied load well satisfy the Weibull distribution function. Furthermore, as the particle size decreases, the shapes of crushed coal gangue particles are block, flaky, acicular, and powder, and the particles with a size greater than 5 mm are mainly flaky. In addition, comparative analysis shows that the new gradation equation can better describe the gradation of coal gangue fragments after crushing. Full article

The utilization of CO₂ mineralization fly ash (F) and coal gangue (G) technology is proposed in this research work to prepare underground backfilling materials. The test process can be divided into pre-treatment and post-treatment stages. In the pre-treatment stage, a sealed stirring vessel is used to conduct CO₂ wet mineralization. The ratios of F and G were selected as follows: 20%:60% (F2G6), 30%:50% (F3G5), 40%:40% (F4G4), 50%:30% (F5G3), and 60%:20% (F6G2). The ratios were prepared into Φ50 mm × 100 mm cylindrical samples, with curing durations of 3 d, 7 d, 14 d, and 28 d. In the post-processing stage, the SANS microcomputer-controlled electronic universal testing machine and FLIR A615 infrared thermal imager were used to carry out uniaxial loading and temperature detection, respectively. The unconfined compressive strength (UCS), X-ray diffraction (XRD), average infrared radiation temperature (AIRT), variance of original infrared image temperature (VOIIT), and variance of successive minus infrared image temperature (VSMIT) of the samples were compared and analyzed. The results indicated that when curing reaches 14 d, the strength approaches its peak, with minimal changes in strength over a delayed period; furthermore, as the ratio of F to G continues to increase, the mineralization effect gradually strengthens, reaching its optimum level at a ratio of 5:3. However, when the ratio exceeds 5:3, signs of deteriorating mineralization effect start to appear. During the loading process, the AIRT of the mineralized samples showed a continuous increase, but the VOIIT and VSMIT of the mineralized sample both exhibited significant fluctuations or rapid increases during damage rupture. Moreover, the rise in the AIRT value was found to be linked to the increase in the ratio of F to G. This indicates that F has a higher thermal–mechanical conversion efficiency compared to G, so the temperature change will be greater during the loading process. The drastic changes in the VOIIT and VSMIT indicate that they can be used as sensitive response indicators for sample rupture, and can predict and warn of damage rupture in mineralized samples. Research work can provide practical guidance and reference for underground backfilling of CO₂ mineralization industrial waste. Full article

To address the lightweight and real-time issues of coal sorting detection, an intelligent detection method for coal and gangue, Our-v8, was proposed based on improved YOLOv8. Images of coal and gangue with different densities under two diverse lighting environments were collected. Then the Laplacian image enhancement algorithm was proposed to improve the training data quality, sharpening contours and boosting feature extraction; the CBAM attention mechanism was introduced to prioritize crucial features, enhancing more accurate feature extraction ability; and the EIOU loss function was added to refine box regression, further improving detection accuracy. The experimental results showed that Our-v8 for detecting coal and gangue in a halogen lamp lighting environment achieved excellent performance with a mean average precision (mAP) of 99.5%, was lightweight with FLOPs of 29.7, Param of 12.8, and a size of only 22.1 MB. Additionally, Our-v8 can provide accurate location information for coal and gangue, making it ideal for real-time coal sorting applications. Full article

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. Full article

Intelligent gangue sorting with high precision is of vital importance for improving coal quality. To tackle the challenges associated with coal gangue target detection, including algorithm performance imbalance and hardware deployment difficulties, in this paper, an intelligent gangue separation system that adopts the elevated YOLO-v5 algorithm and dual-energy X-rays is proposed. Firstly, images of dual-energy X-ray transmission coal gangue mixture under the actual operation of a coal mine were collected, and datasets for training and validation were self-constructed. Then, in the YOLOv5 backbone network, the EfficientNetv2 was used to replace the original cross stage partial darknet (CSPDarknet) to achieve the lightweight of the backbone network; in the neck, a light path aggregation network (LPAN) was designed based on PAN, and a convolutional block attention module (CBAM) was integrated into the BottleneckCSP of the feature fusion block to raise the feature acquisition capability of the network and maximize the learning effect. Subsequently, to accelerate the rate of convergence, an efficient intersection over union (EIOU) was used instead of the complete intersection over union (CIOU) loss function. Finally, to address the problem of low resolution of small targets leading to missed detection, an L2 detection head was introduced to the head section to improve the multi-scale target detection performance of the algorithm. The experimental results indicate that in comparison with YOLOv5-S, the same version of the algorithm proposed in this paper increases by 19.2% and 32.4% on mAP @.5 and mAP @.5:.95, respectively. The number of parameters decline by 51.5%, and the calculation complexity declines by 14.7%. The algorithm suggested in this article offers new ideas for the design of identification algorithms for coal gangue sorting systems, which is expected to save energy and reduce consumption, reduce labor, improve efficiency, and be more friendly to the embedded platform. Full article

Coal gangue image recognition is a critical technology for achieving automatic separation in coal processing, characterized by its rapid, environmentally friendly, and energy-saving nature. However, the response characteristics of coal and gangue vary greatly under different illuminance conditions, which poses challenges to the stability of feature extraction and recognition, especially when strict illuminance requirements are necessary. This leads to fluctuating coal gangue recognition accuracy in industrial environments. To address these issues and improve the accuracy and stability of image recognition under variable illuminance conditions, we propose a novel coal gangue recognition method based on laser speckle images. Firstly, we studied the inter-class separability and intra-class compactness of the collected laser speckle images of coal and gangue by extracting gray and texture features from the laser speckle images, and analyzed the performance of laser speckle images in representing the differences between coal and gangue minerals. Subsequently, coal gangue recognition was achieved using an SVM classifier based on the extracted features from the laser speckle images. The fusion feature approach achieved a recognition accuracy of 94.4%, providing further evidence of the feasibility of this method. Lastly, we conducted a comparative experiment between natural images and laser speckle images for coal gangue recognition using the same features. The average accuracy of coal gangue laser speckle image recognition under various lighting conditions is 96.7%, with a standard deviation of the recognition accuracy of 1.7%. This significantly surpasses the recognition accuracy obtained from natural coal and gangue images. The results showed that the proposed laser speckle image features can facilitate more stable coal gangue recognition with illumination factors, providing a new, reliable method for achieving accurate classification of coal and gangue in the industrial environment of mines. Full article

Coal gangue sorting is a necessary process in coal mine production, and removing gangue is the basis for the coal production of clean energy; it is also an important approach to reduce the cost of washing, improve the grade of finished coal and increase the economic efficiency of coal mining enterprises. For the problem of high similarity and low-degree dynamic recognition of coal and gangue, a coal gangue target detection method based on improved YOLOv5s is proposed. Based on the YOLOv5s network, the decoupled head and SimAM attention mechanism are introduced and the CSP module in the neck part of YOLOv5s is replaced with the VoV-GSCSP structure. The experimental results show that the proposed method improves the mAP value by 6.1% over YOLOv5s in the gangue target detection task, while maintaining a higher detection speed. The coal gangue classification precision reaches 99.7% when tested on 1479 images. Compared with YOLOv5 series, YOLOv7 series, SSD and Faster-RCNN, the proposed method invariably yields higher precision and detection speed to meet the requirements of real-time detection. The experiments prove that the method proposed in this paper can be applied to the coal gangue sorting industry for fast and high-precision identification of coal gangue. Full article

In response to the multiscale shape of coal and gangue in actual production conditions, existing coal separation methods are inefficient in recognizing coal and gangue, causing environmental pollution and other problems. Combining image data preprocessing and deep learning techniques, this paper presents an improved EfficientNetV2 network for coal and gangue recognition. To expand the dataset and prevent network overfitting, a pipeline-based data enhancement method is used on small sample datasets to simulate coal and gangue production conditions under actual working conditions. This method involves modifying the attention mechanism module in the model, employing the CAM attention mechanism module, selecting the Hardswish activation function, and updating the block structure in the network. The parallel pooling layer introduced in the CAM module can minimize information loss and extract rich feature information compared with the single pooling layer of the SE module. The Hardswish activation function is characterized by excellent numerical stability and fast computation speed. It can effectively be deployed to solve complex computation and derivation problems, compensate for the limitations of the ReLu activation function, and improve the efficiency of neural network training. We increased the training speed of the network while maintaining the accuracy of the model by selecting optimized hyperparameters for the network structure. Finally, we applied the improved model to the problem of coal and gangue recognition. The experimental results showed that the improved EfficientNetV2 coal and gangue recognition method is easy to train, has fast convergence and training speeds, and thus achieves high recognition accuracy under insufficient dataset conditions. The accuracy of coal and gangue recognition increased by 3.98% compared with the original model, reaching 98.24%. Moreover, the training speed improved, and the inference time of the improved model decreased by 6.6 ms. The effectiveness of our proposed model improvements is confirmed by these observations. Full article