Search Results (369)

Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic online recognition of flame combustion status during MSWI is a key technical approach to ensuring system stability, addressing issues such as high pollution emissions, severe equipment wear, and low operational efficiency. However, when manually selecting optimized features and hyperparameters based on empirical experience, the MSWI flame combustion state recognition model suffers from high time consumption, strong dependency on expertise, and difficulty in adaptively obtaining optimal solutions. To address these challenges, this article proposes a method for constructing a flame combustion state recognition model optimized based on reinforcement learning (RL), long short-term memory (LSTM), and parallel differential evolution (PDE) algorithms, achieving collaborative optimization of deep features and model hyperparameters. First, the feature selection and hyperparameter optimization problem of the ViT-IDFC combustion state recognition model is transformed into an encoding design and optimization problem for the PDE algorithm. Then, the mutation and selection factors of the PDE algorithm are used as modeling inputs for LSTM, which predicts the optimal hyperparameters based on PDE outputs. Next, during the PDE-based optimization of the ViT-IDFC model, a policy gradient reinforcement learning method is applied to determine the parameters of the LSTM model. Finally, the optimized combustion state recognition model is obtained by identifying the feature selection parameters and hyperparameters of the ViT-IDFC model. Test results based on an industrial image dataset demonstrate that the proposed optimization algorithm improves the recognition performance of both left and right grate recognition models, with the left grate achieving a 0.51% increase in recognition accuracy and the right grate a 0.74% increase. Full article

The paper presents the application of high-speed flame imaging combined with convolutional neural networks (CNNs) for determining different states of biomass–coal co-combustion in terms of thermal power and excess air coefficient. The experimental setup and methodology used in a laboratory-scale co-combustion system are described, highlighting tests conducted across nine defined operational variants. The performance of several state-of-the-art CNN architectures was examined, focusing particularly on those achieving the highest classification metrics and exploring the dependence of input image resolution and applying a transfer learning paradigm. By benchmarking various CNNs on a large, diverse image dataset without preprocessing, the research advances intelligent, automated control systems for improved stability, efficiency, and emissions control, bridging advanced visual diagnostics with real-time industrial applications. The summary includes recommendations and potential directions for further research related to the use of image data and machine learning techniques in industry. Full article

Self-fluxing Ni-based coatings (NiCrFeBSiC) were deposited through gas-flame spraying and evaluated in three conditions: as-sprayed, flame-remelted, and furnace-heat-treated (1025 °C/5 min). Phase analysis (XRD) revealed FeNi₃ together with strengthening carbides/borides (e.g., Cr₇C₃, Fe₂₃(C,B)₆); post-treatments increased lattice order. Cross-sectional image analysis showed progressive densification (thickness ~805 → 625 → 597 µm) and a drop in porosity from 7.866% to 3.024% to 1.767%. Surface roughness decreased from Ra = 31.860 to 14.915 to 13.388 µm. Near-surface microhardness rose from 528.7 ± 2.3 to 771.6 ± 4.6 to 922.4 ± 5.7 HV, while adhesion strength (ASTM C633) improved from 18 to 27 to 34 MPa. Wettability followed the densification trend, with the contact angle increasing from 53.152° to 79.875° to 89.603°. Under dry ball-on-disk sliding against 100Cr6, the friction coefficient decreased and stabilized (0.648 ± 0.070 → 0.173 ± 0.050 → 0.138 ± 0.003), and the counterbody wear-scar area shrank by ~95.6% (0.889 → 0.479 → 0.0395 mm²). Wear-track morphology evolved from abrasive micro-cutting (as-sprayed) to reduced ploughing (flame-remelted) and a polishing-like regime with a thin tribo-film (furnace). Potentiodynamic tests indicated the lowest corrosion rate after furnace treatment (CR ≈ 0.005678 mm·year⁻¹). Overall, furnace heat treatment provided the best structure–property balance (lowest porosity and Ra, highest HV and adhesion, lowest and most stable μ, and superior corrosion resistance) and is recommended to extend the service life of NiCrFeBSiC coatings under dry sliding. Full article

Under electrical overload conditions, the molten dripping of thermoplastic wire insulation materials—particularly crosslinked polyethylene (XLPE)—poses a severe fire hazard and significantly complicates fire prevention and control. This study systematically investigated the formation mechanism, periodic characteristics, and flame interaction behavior of molten dripping in XLPE-insulated wires subjected to varying overload currents (0–80 A). Experiments were conducted using a custom-designed test platform equipped with precise current regulation and high-resolution video imaging systems. Key dripping parameters—including the initial dripping time, dripping frequency, and period—were extracted and analyzed. The results indicate that increased current intensifies Joule heating within the conductor, accelerating the softening and pyrolysis of the insulation, thus resulting in earlier and more frequent dripping. A thermodynamic prediction model was developed to reveal the nonlinear coupling relationships between the dripping frequency, period, and current, which showed strong agreement with the experimental data, especially at high current levels. Further flame morphology analysis showed that molten dripping induced pronounced vertical flame disturbances, while the lateral flame spread remained relatively unchanged. This phenomenon promotes vertical flame propagation and can trigger multiple ignition points, thereby increasing the fire complexity and hazard. The study enhances our understanding of the coupling mechanisms between electrical loading and molten dripping behavior and provides theoretical and experimental foundations for fire-safe wire design and early-stage risk assessment. Full article

Aiming at the core problem of high false and missed alarm rate and insufficient interference resistance of existing smoke and fire detection algorithms in complex scenes, this paper proposes a target detection network based on improved feature pyramid structure. By constructing a Context Guided Convolutional Block instead of the traditional convolutional operation, the detected target and the surrounding environment information are fused with secondary features while reconfiguring the feature dimensions, which effectively solves the problem of edge feature loss in the down-sampling process. The Poly Kernel Inception Block is designed, and a multi-branch parallel network structure is adopted to realize multi-scale feature extraction of the detected target, and the collaborative characterization of the flame profile and smoke diffusion pattern is realized. In order to further enhance the logical location sensing ability of the target, a Manhattan Attention Mechanism Unit is introduced to accurately capture the spatial and temporal correlation characteristics of the flame and smoke by establishing a pixel-level long-range dependency model. Experimental tests are conducted using a self-constructed high-quality smoke and fire image dataset, and the results show that, compared with the existing typical lightweight smoke and fire detection models, the present algorithm has a significant advantage in detection accuracy, and it can satisfy the demand for real-time detection. Full article

This study presents a flame detection model that is based on real experimental data that were collected during turbopump hot-fire tests of a liquid rocket engine. In these tests, a MEMRECAM ACS-1 M40 high-speed camera—serving as an optical sensor within the test instrumentation system—captured plume images for analysis. To detect abnormal flame phenomena in the plume, a Gaussian support vector machine (SVM) model was developed using image features that were derived from both color and gradient information. Six representative frames containing visible flames were selected from a single test failure video. These images were segmented in the YCbCr color space using the k-means clustering algorithm to distinguish flame and non-flame pixels. A 10-dimensional feature vector was constructed for each pixel and then reduced to five dimensions using the Maximum Relevance Minimum Redundancy (mRMR) method. The reduced vectors were used to train the Gaussian SVM model. The model achieved a 97.6% detection accuracy despite being trained on a limited dataset. It has been successfully applied in multiple subsequent engine tests, and it has proven effective in detecting ablation-related anomalies. By combining real-world sensor data acquisition with intelligent image-based analysis, this work enhances the monitoring capabilities in rocket engine development. Full article

The rapid detection and confirmation of Suspicious Regions of Forest Fire (SRoFF) are critical for timely alerts and firefighting operations. In the early stages of forest fires, small flames and heavy occlusion lead to low accuracy, false detections, omissions, and slow inference in existing target-detection algorithms. We constructed the Suspicious Regions of Forest Fire Dataset (SRFFD), comprising publicly available datasets, relevant images collected from online searches, and images generated through various image enhancement techniques. The SRFFD contains a total of 64,584 images. In terms of effectiveness, the individual augmentation techniques rank as follows (in descending order): HSV (Hue Saturation and Value) random enhancement, copy-paste augmentation, and affine transformation. A detection model named SRoFF-Yolover is proposed for identifying suspicious regions of forest fire, based on the YOLOv8. An embedding layer that effectively integrates seasonal and temporal information into the image enhances the prediction accuracy of the SRoFF-Yolover. The SRoFF-Yolover enhances YOLOv8 by (1) adopting dilated convolutions in the Backbone to enlarge feature map receptive fields; (2) incorporating the Convolutional Block Attention Module (CBAM) prior to the Neck’s C2fLayer for small-target attention; and (3) reconfiguring the Backbone-Neck linkage via P2, P4, and SPPF. Compared with the baseline model (YOLOv8s), the SRoFF-Yolover achieves an 18.1% improvement in mAP@0.5, a 4.6% increase in Frames Per Second (FPS), a 2.6% reduction in Giga Floating-Point Operations (GFLOPs), and a 3.2% decrease in the total number of model parameters (#Params). The SRoFF-Yolover can effectively detect suspicious regions of forest fire, particularly during winter nights. Experiments demonstrated that the detection accuracy of the SRoFF-Yolover for suspicious regions of forest fire is higher at night than during daytime in the same season. Full article

Benzene, a well-established human carcinogen and major industrial pollutant, poses significant health risks through occupational exposure due to its no-threshold effect, leading to multi-system damage involving the hematopoietic, nervous, and immune systems. This makes the investigation of its toxic mechanisms crucial for precise prevention and control of its health impacts. Programmed cell death (PCD), an orderly and regulated form of cellular demise controlled by specific intracellular genes in response to various stimuli, has emerged as a key pathway where dysfunction may underlie benzene-induced toxicity. This review systematically integrates evidence linking benzene toxicity to PCD dysregulation, revealing that benzene and its metabolites induce abnormal subtypes of PCD (apoptosis, autophagy, ferroptosis) in hematopoietic cells. This occurs through mechanisms including activation of Caspase pathways, regulation of long non-coding RNAs, and epigenetic modifications, with recent research highlighting the IRP1-DHODH-ALOX12 ferroptosis axis and oxidative stress–epigenetic interactions as pivotal. Additionally, this review describes a comprehensive monitoring system for early toxic effects comprising benzene exposure biomarkers (urinary t,t-muconic acid (t,t-MA), S-phenylmercapturic acid (S-PMA)), PCD-related molecules (Caspase-3, let-7e-5p, ACSL1), oxidative stress indicators (8-OHdG), and genetic damage markers (micronuclei, p14ARF methylation), with correlative analyses between PCD mechanisms and benzene toxicity elaborated to underscore their integrative roles in risk assessment. Furthermore, the review details analytical techniques for these biomarkers, including direct benzene detection methods—direct headspace gas chromatography with flame ionization detection (DHGC-FID), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and portable headspace sampling (Portable HS)—alongside molecular imprinting and fluorescence probe technologies, as well as methodologies for toxic effect markers such as live-cell imaging, electrochemical techniques, methylation-specific PCR (MSP), and Western blotting, providing technical frameworks for mechanistic studies and translational applications. By synthesizing current evidence and mechanistic insights, this work offers novel perspectives on benzene toxicity through the PCD lens, identifies potential therapeutic targets associated with PCD dysregulation, and ultimately establishes a theoretical foundation for developing interventional strategies against benzene-induced toxicity while emphasizing the translational value of mechanistic research in occupational and environmental health. Full article

Combustion instability poses a significant challenge in aerospace propulsion systems, particularly in afterburners that employ bluff-body flame stabilizers. The flame transfer function (FTF) is essential for characterizing the dynamic response of flames to perturbations, which is critical for predicting and controlling these instabilities. This study experimentally investigates the effect of varying the number of fuel injection holes (N = 3, 4, 5, 6) on the FTF and flame dynamics in a model afterburner combustor. Using acoustic excitations, the FTF was measured across a range of frequencies, with flame behavior analyzed via high-speed imaging and chemiluminescence techniques. Results reveal that the FTF gain exhibits dual-peak characteristics, initially decreasing and then increasing with higher N values. The frequencies of these gain peaks shift to higher values as N increases, while the time delay between velocity and heat release rate fluctuations decreases, indicating a faster flame response. Flame morphology analysis shows that higher N leads to shorter, taller flames due to enhanced fuel distribution and mixing. Detailed examination of flame dynamics indicates that different pulsation modes dominate at various frequencies, elucidating the observed FTF behavior. This research provides novel insights into the optimization of fuel injection configurations to enhance combustion stability in afterburners, advancing the development of more reliable and efficient aerospace propulsion systems. Full article

This article investigates the effects of pressure vacuum impregnation using inorganic, organic, and natural flame retardants on enhancing the fire resistance and chemical composition of structural beech wood (Fagus sylvatica). The study examines fire resistance characteristics such as the limiting oxidation number and heat of combustion, which indicate the effectiveness of the flame retardants used. Chemical changes in the beech wood were characterized through various analyses, including changes in chemical composition, FTIR spectra, DSC thermograms, and SEM images. The relationships between combustion characteristics and chemical changes were assessed using multiple methods. The results demonstrate that using 5% potassium acetate achieved a lower heat of combustion compared to 15% sodium phosphate, and it was significantly lower than the heat of combustion observed with 5% arabinogalactan or the reference sample of beech wood. However, neither potassium acetate nor diammonium phosphate significantly affected the macromolecular structures of the wood when compared to the reference sample. Low concentrations of flame retardants reduce environmental release and environmental impact while increasing fire resistance, which could be used for structural solutions made of hardwoods. Full article

The increasing frequency and intensity of wildfires pose serious threats to ecosystems, property, and human safety worldwide. Accurate semantic segmentation of wildfire images is essential for real-time fire monitoring, spread prediction, and disaster response. However, existing deep learning methods heavily rely on large volumes of pixel-level annotated data, which are difficult and costly to obtain in real-world wildfire scenarios due to complex environments and urgent time constraints. To address this challenge, we propose a semi-supervised wildfire image segmentation framework that enhances segmentation performance under limited annotation conditions by integrating multi-scale structural information fusion and pixel-level contrastive consistency learning. Specifically, a Lagrange Interpolation Module (LIM) is designed to construct structured interpolation representations between multi-scale feature maps during the decoding stage, enabling effective fusion of spatial details and semantic information, and improving the model’s ability to capture flame boundaries and complex textures. Meanwhile, a Pixel Contrast Consistency (PCC) mechanism is introduced to establish pixel-level semantic constraints between CutMix and Flip augmented views, guiding the model to learn consistent intra-class and discriminative inter-class feature representations, thereby reducing the reliance on large labeled datasets. Extensive experiments on two public wildfire image datasets, Flame and D-Fire, demonstrate that our method consistently outperforms other approaches under various annotation ratios. For example, with only half of the labeled data, our model achieves 5.0% and 6.4% mIoU improvements on the Flame and D-Fire datasets, respectively, compared to the baseline. This work provides technical support for efficient wildfire perception and response in practical applications. Full article

Benzoxazine resins are gaining attention for their impressive thermal stability, low water uptake, and strong mechanical properties. In this work, two new bio-based benzoxazine monomers were developed using renewable arbutin: one combined with 3-(2-aminoethylamino) propyltrimethoxysilane (AB), and the other with furfurylamine (AF). Both were synthesized using a simple Mannich-type reaction and verified through FT-IR and ¹H-NMR spectroscopy. By blending these monomers in different ratios, copolymers with adjustable thermal, dielectric, and surface characteristics were produced. Thermal analysis showed that the materials had broad processing windows and cured effectively, while thermogravimetric testing confirmed excellent heat resistance—especially in AF-rich blends, which left behind more char. The structural changes obtained during curing process were monitored using FT-IR, and XPS verified the presence of key elements like carbon, oxygen, nitrogen, and silicon. SEM imaging revealed that AB-based materials had smoother surfaces, while AF-based ones were rougher; the copolymers fell in between. Dielectric testing showed that increasing AF content raised both permittivity and loss, and contact angle measurements confirmed that surfaces ranged from water-repellent (AB) to water-attracting (AF). Overall, these biopolymers (AB/AF copolymers) synthesized from arbutin combine environmental sustainability with customizability, making them strong candidates for use in electronics, protective coatings, and flame-resistant composite materials. Full article

Smoke appears earlier than flames, so image-based fire monitoring techniques mainly focus on the detection of smoke, which is regarded as one of the effective strategies for preventing the spread of initial fires that eventually evolve into serious fires. Smoke monitoring in mine fires faces serious challenges: the underground environment is complex, with smoke and backgrounds being highly integrated and visual features being blurred, which makes it difficult for existing image-based monitoring techniques to meet the actual needs in terms of accuracy and robustness. The conventional ground-based methods are directly used in the underground with a high rate of missed detection and false detection. Aiming at the core problems of mixed target and background information and high boundary uncertainty in smoke images, this paper, inspired by the principle of information entropy, proposes a method for recognizing smoke from mine fires by integrating entropy-enhanced image processing and improved YOLOv8. Firstly, according to the entropy change characteristics of spatio-temporal information brought by smoke diffusion movement, based on spatio-temporal entropy separation, an equidistant frame image differential fusion method is proposed, which effectively suppresses the low entropy background noise, enhances the detail clarity of the high entropy smoke region, and significantly improves the image signal-to-noise ratio. Further, in order to cope with the variable scale and complex texture (high information entropy) of the smoke target, an improvement mechanism based on entropy-constrained feature focusing is introduced on the basis of the YOLOv8m model, so as to more effectively capture and distinguish the rich detailed features and uncertain information of the smoke region, realizing the balanced and accurate detection of large and small smoke targets. The experiments show that the comprehensive performance of the proposed method is significantly better than the baseline model and similar algorithms, and it can meet the demand of real-time detection. Compared with YOLOv9m, YOLOv10n, and YOLOv11n, although there is a decrease in inference speed, the accuracy, recall, average detection accuracy mAP (50), and mAP (50–95) performance metrics are all substantially improved. The precision and robustness of smoke recognition in complex mine scenarios are effectively improved. Full article

Heat release rate (HRR) is a key indicator for characterizing fire behavior, and it is conventionally measured under laboratory conditions. However, this measurement is limited in its widespread application to various fire conditions, due to its high cost, operational complexity, and lack of real-time predictive capability. Therefore, this study proposes an image-based HRR prediction model that uses deep learning and image processing techniques. The flame region in a fire video was segmented using the YOLO-YCbCr model, which integrates YCbCr color-space-based segmentation with YOLO object detection. For comparative analysis, the YOLO segmentation model was used. Furthermore, the fire diameter and flame height were determined from the spatial information of the segmented flame, and the HRR was predicted based on the correlation between flame size and HRR. The proposed models were applied to various experimental fire videos, and their prediction performances were quantitatively assessed. The results indicated that the proposed models accurately captured the HRR variations over time, and applying the average flame height calculation enhanced the prediction performance by reducing fluctuations in the predicted HRR. These findings demonstrate that the image-based HRR prediction model can be used to estimate real-time HRR values in diverse fire environments. Full article

This study investigates the application of deep learning (DL) techniques for predicting temperature fields in the SM1 swirl-stabilized turbulent non-premixed flame. Two distinct DL approaches were developed using a comprehensive CFD database generated via the steady laminar flamelet model coupled with the SST k-ω turbulence model. The first approach employs a fully connected dense neural network to directly map scalar input parameters—fuel velocity, swirl ratio, and equivalence ratio—to high-resolution temperature contour images. In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. In the first approach, the results of the Inception V3 model and the developed Dense Model were found to be better than Res-Net and Efficient Net. At the same time, file sizes and usability were examined. The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. In the secondary approach, similar to the first approach, studies were conducted with different deep learning models, namely Res-Net, Efficient Net, and Inception Net, to evaluate model performance. The U-Net model, which is well developed, stands out with its low error and small file size. The dense network is appropriate for direct parametric analyses, while the image-based U-Net model provides a rapid and scalable option to utilize the cold flow CFD images. This framework can be further refined in future research to estimate more flow factors and tested against experimental measurements for enhanced applicability. Full article