Search Results (650)

Rotary kiln-based activated carbon production combines high-temperature operation with flammable/reducing gases, carbonaceous dust, and downstream off-gas treatment and acid/base washing, creating complex escalation pathways. This study prioritizes safety improvements by applying classical failure modes and effects analysis (FMEA) and a transparent Fuzzy-FMEA framework to 18 representative failure modes (six each for kiln/activation, acid/base handling, and atmosphere/control). Five experts evaluated Severity, Occurrence, and Detection on a 10-point scale. The fuzzy model used triangular membership functions (L/M/H), a monotonic 27-rule base, Mamdani max–min inference, and centroid defuzzification to compute a continuous fuzzy risk priority number (FRPN, 0–10). Classical FMEA identified dust explosion (RPN = 405), temperature control failure (RPN = 378), and off-gas leakage (RPN = 324) as the highest-ranked risks. Fuzzy-FMEA preserved the top-risk group while more strongly highlighting barrier-related risks, placing off-gas leakage, instrumentation/interlock failure, and electrostatic ignition control alongside dust explosion (FRPN 9.221–9.332). The rankings were strongly correlated (Spearman ρ = 0.871; Kendall τ = 0.752), yet mid-risk items were rearranged (mean |Δrank| = 2.06; max = 5), improving discrimination within tied RPN clusters. The five highest-priority scenarios were reconstructed into actionable engineering packages, including dust and ignition control, off-gas integrity linked to shutdown logic, interlock proof testing and bypass management, and independent protection layers for kiln temperature control. Full article

Parabens, a prevalent class of endocrine-disrupting chemicals (EDCs), are ubiquitous in consumer products; however, their role in linking pediatric allergic phenotypes remains poorly understood. This case-control study analyzed paraben levels in urine and indoor dust as proxies for internal and external exposures and investigated their associations with allergic rhinitis only (AR Only), asthma only (AS Only), and comorbidities (AR&AS) among children in Shanghai. The concentrations for each of four paraben compounds were quantitatively measured, and multi-pollutant frameworks—including Bayesian Kernel Machine Regression (BKMR) and Weighted Quantile Sum (WQS) regression—were employed to characterize the mixture exposure and risk. Propylparaben (PrP) was detectable in 100% of urine samples and over 90% of dust samples, and the concentrations ranked the highest out of the four compounds in both samples. Benzylparaben (BzP) was detected in >70% of urine samples and over 50% of dust samples at relatively lower levels. Urinary PrP exhibited significantly positive associations with all phenotypes (OR in 2.18–2.92) and BzP with the AR&AS Comorbidity (OR = 3.55, 95% CI: 1.32–9.55). Dust-borne PrP was associated with AR Only (OR = 2.26, 95% CI: 1.16–4.43), indicating a potential “Portal of Entry” effect via direct nasal deposition. According to BKMR and WQS analyses, urinary PrP and BzP emerged as two primary risk drivers. Using interaction analysis, an additive synergistic effect was observed between urinary PrP and BzP with parental history of allergy, suggesting heightened vulnerability to paraben exposure in genetically predisposed subgroups. In conclusion, children with respiratory allergies were associated with higher exposure to PrP and BzP and exhibited higher susceptibility in those with a parental history of allergy. Full article

The concentration of heavy metals in the environment has been steadily increasing, raising concerns about their adverse effects on ecosystems and human health. Fine-grained particulate matter is of particular concern due to its enhanced mobility, bioavailability, and potential for inhalation exposure. Facilities involved in the mechanical processing of electronic waste (e-waste) represent a significant potential source of metal-containing fine particles. In this study, crushed e-waste components containing precious metals were separated into particle-size fractions ranging from 3.0 to 0.15 mm using a vibratory sieving system. The elemental composition of the individual fractions was determined by energy-dispersive X-ray fluorescence spectrometry (ED-XRF), while the spatial distribution of selected metals in fine fractions was further investigated using scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (SEM–EDS). The results demonstrate that e-waste contains a wide range of heavy non-ferrous metals whose distribution is strongly dependent on particle size. A pronounced enrichment of metals was observed in the finest fractions, particularly below 0.25 mm. Compared to the coarse fraction (>3 mm), the zinc concentration increased by approximately one order of magnitude, while chromium, nickel, and cadmium exhibited increases of up to approximately 20-fold. Lead showed particularly high enrichment, reaching approximately 2 wt.% in the finest fraction (<0.15 mm), corresponding to nearly fiftyfold enrichment relative to the coarse fraction. Tin concentrations also increased markedly, in some cases by up to two orders of magnitude. Trace amounts of arsenic and selenium were detected in the finest fractions, whereas mercury was not detected. The combined ED-XRF and SEM–EDS results confirm that fine-grained e-waste fractions are the dominant carriers of hazardous metals and respirable particles generated during mechanical processing. These findings highlight the dual character of fine fractions as both a critical environmental and occupational risk and a potentially valuable secondary resource. The study emphasizes the importance of controlled handling, effective dust management, and targeted processing strategies to minimize human exposure while enabling efficient recovery of valuable metals from e-waste. Full article

The industrial dismantling and recycling of end-of-life photovoltaic (PV) modules require robust visual inspection under dust contamination, inter-class similarity, and constrained edge-computing conditions. This study proposes an end-to-end framework that detects key module components (junction box, backsheet label, aluminum frame, and shadow region) and estimates the aluminum frame gap height for dismantling control. The primary novelty is a dust erosion-aware detection and metrology framework that couples frequency-enhanced visual perception with shadow-guided geometric measurement, while lightweight deployment modules serve as supporting engineering components. Specifically, DWT/FFT-based enhancement with CLAHE is used to improve degraded features, and YOLOv8 is strengthened by GSConv and Coordinate Attention in the backbone and neck; transfer learning, INT8 quantization-aware training, and CMFH-based compact rechecking are further introduced for practical deployment. Experiments show that the proposed method improves mAP@0.5 by 5.08 percentage points over baseline YOLOv8 while increasing speed from 45 to 52 FPS. For geometric metrology, the method achieves 93.0% accuracy with a mean error of 0.45 mm. The results demonstrate an accurate, robust, and edge-deployable solution for the automated inspection and recycling of end-of-life PV modules under dusty conditions. Full article

Urban–industrial environments can generate mixed geogenic and traffic-related metal signatures in paediatric scalp hair, yet interpretation is challenged by left-censoring and limited health-based guidance values for hair. We quantified barium (Ba), strontium (Sr) and vanadium (V) in archived scalp hair collected in 2001 from children (6–9 years, n = 120) and adolescents (13–16 years, n = 97) residing in Alcalá de Henares (central Spain). Samples were washed, digested and quantified by Inductively coupled plasma mass spectrometry (ICP–MS; laboratory processing in 2025); results below the limit of detection (LoD) were treated as left-censored using NADA2 (no substitution). In children, Ba and Sr were frequently quantifiable (medians 0.193 and 0.412 µg/g; 38.3% and 23.3% <LoD), whereas V was heavily censored (74.2% <LoD; median 0.003 µg/g). Adolescents showed higher Ba and Sr and broader upper tails (Ba median 0.287 µg/g, P95 2.061 µg/g; Sr median 1.105 µg/g, P95 4.995 µg/g), while V remained low (median 0.011 µg/g, P95 0.052 µg/g). Ba and Sr displayed strong spatial gradients across four residential zones in adolescents (censored-data Peto–Peto tests p < 1 × 10⁻⁸), but V did not (p = 0.162). Co-located residential topsoils were available only for V and showed limited between-zone contrast; soil–hair correspondence was weak overall but moderate in adolescent girls (Spearman ρ = 0.433). These findings provide a historical baseline and support a cautious tracer-oriented interpretation in which the observed Ba–Sr spatial patterning is consistent with heterogeneous contact with dust- and traffic-influenced surface materials, while V appears less discriminatory in low-contrast community settings. Full article

Collisions between over-height vehicles and low-clearance bridges cause infrastructure damage and pose safety risks. Existing detection systems rely primarily on optical sensors, which suffer from performance degradation in adverse weather conditions. This paper presents an alternative approach based on a 94 GHz millimeter-wave radar that achieves velocity-independent height measurement. The proposed technique exploits the ratio of Doppler shifts from two scattering centers on a vehicle, specifically the roof and the wheel–road interface. This ratio depends only on the measurement geometry, as the unknown vehicle velocity cancels algebraically, enabling direct height computation without speed measurement. The paper provides a closed-form height estimation model, analyzes the trade-off between frequency resolution and geometric constancy during integration, and presents experimental validation using a scaled laboratory testbed. An optical tracking system is used solely for ground-truth validation in the laboratory and is not required for operational deployment. Results across six test cases with heights ranging from 20 cm to 46 cm demonstrate an average absolute error of 0.60 cm and relative errors below 3.3 percent. A scaling analysis for representative full-scale geometries indicates that at highway speeds of 80 km/h, integration times in the millisecond range (approximately 3–18 ms for representative 20–50 m measurement standoff) are feasible; warning distance can be extended independently by upstream radar placement. The expected advantage in fog, rain, and dust is based on established W-band propagation characteristics; dedicated adverse-weather and full field validation (including multipath, clutter, and multi-vehicle scenarios) remain future work. Full article

Existing intelligent monitoring methods are limited by insufficient training samples and target-feature degradation in complex environments. To address these issues, an industrial visual inspection scheme with dual verification is proposed for material sheds. The scheme integrates sample enhancement preprocessing based on a Dynamic Enhanced Generative Adversarial Network (DEGAN) with an Attention-Enhanced YOLO-SLOWFAST (AE-YOLO-SLOWFAST) model for target and behavior detection, enabling feature enhancement, real-time dust monitoring, and timely dust suppression. A dynamic enhancement module is first introduced into a GAN, creating DEGAN to generate high-quality samples and augment the training dataset. An AE-YOLO model is then developed to improve static feature extraction under low illumination and enhance small-target detection. The objective function is refined to improve recognition of hard-to-distinguish samples during training. AE-YOLO is combined with SLOWFAST to recognize vehicle behaviors. Dual verification is performed using dust and vehicle detection results together with action recognition outputs, enabling precise control of dust suppression equipment for targeted water mist spraying. The improved AE-YOLO model achieves an mAP@50 of 94.4%. The proposed method delivers a vehicle–dust association matching accuracy of up to 97.2%, which enables all-weather, intelligent, traceable dust suppression in material sheds, reduces false recognition interference, and ensures timely suppression in areas where vehicles are operating. Full article

Phosphate mining companies in Morocco pose many environmental and occupational safety risks, especially through the release of airborne particulates, gas pollutants, and heavy metals. While there is increased implementation of monitoring systems within industrial mining contexts, current methodologies are still predominantly founded on rule-based systems or classical statistics that presume linearity in relationships between an arbitrary set of environmental parameters and the likelihood of an incident. Conversely, mining operations are characterized by intricately dynamic nonlinear combinations of numerous environmental and operational variables. As a result, a potential research opportunity exists for the application of sophisticated machine learning techniques that provide the ability to detect various levels of operational risk within phosphate mining scenarios. This study has three objectives. First, to examine the mining environmental and operational data from the phosphate mining sites to determine the mining operational conditions that present the highest risk. Second, to create a machine learning classification model which utilizes a Feedforward Neural Network (FNN) to identify operational states that are prone to incidents based on multivariate sensor data. Third, to assess the validity and reliability of the model using machine learning validity and reliability evaluation techniques along with statistical validation methods. In this study, an artificial intelligence-based approach for AI-based safety monitoring was proposed by using a Feedforward Neural Network (FNN) on a detailed data set of 1536 hourly measurements, directly recorded onsite at OCP plants in Benguerir and Khouribga. Environmental and industrial parameters (dust concentration, gas emissions, temperature, and toxic metal content) were measured using industrial-grade sensors certified for such a type of application. By means of training the proposed FNN model with adaptive gradient descent and dropout regularization with early stopping, a test mean squared error of 0.057 and over 85% accuracy on incident detection were obtained. Gradient tracking and m-adaptive validation proved the stability and convergence of the model. Emissions and dust were identified as the main risk classifiers in a variable importance analysis. The findings demonstrate that the mining sector may move from reactive to proactive safety management and validate the incorporation of AI into a real-time monitoring infrastructure inside the OCP ecosystem. Practical concerns of industrial data gathering, model interpretability, and the moral application of AI in high-risk settings are also addressed by the study. Full article

This study develops and validates the Saharan Dust Flux and Transport Index (SDFTI) using a 22-year dataset (2003–2024) of dust-related and dynamical variables across the Mediterranean. The index integrates six components (surface-particulate matter, satellite-derived desert-dust optical depth, free-tropospheric dust mass, transport score, North-Atlantic Oscillation and Oceanic Niño Indices) combined through a physically calibrated weighting scheme. To assess the stability of the formulation, three alternative variants are constructed (dust-enhanced, dynamics-enhanced, and equal-weight) and evaluated across four Mediterranean sub-regions using seasonal means, inter-annual anomalies, component correlations, and extreme-event detection. The results show that the SDFTI is highly robust over the full 2003–2024 period. Across all regions, the calibrated variants reproduce nearly identical seasonal cycles (e.g., spring–summer peaks of +0.53 to +0.58 in Western Mediterranean), identify the same dusty and non-dusty years (2008–2012 minima, 2021–2022 maxima), and capture the same major dust outbreaks (e.g., March 2022, June 2021). SDFTI consistently provides the most balanced representation of dust-mass loading and transport dynamics, while the equal-weight variant diverges as expected due to its lack of physical calibration. Overall, the SDFTI offers a stable and regionally coherent measure of Saharan dust transport. The methodological framework (variable selection, normalisation, weighting, and sensitivity testing) is general and can be adapted to other dust-affected regions worldwide. Full article

Background: Following the earthquakes that occurred in Türkiye in 2023, the resulting demolition dust (DD) negatively impacted air quality and led to an increase in respiratory diseases. Although the harmful effects of crystalline and amorphous silica are known, the effects of hydrated cement dust (HCD), autoclaved aerated concrete dust (AACD), and DD on the lungs have not been sufficiently investigated. This rat study presents the first experimental data on the toxicity of these dusts. Methods: In the study, the structural properties of dust particles smaller than 5 µm were characterized using XRD analysis. Subsequently, 48 female rats were divided into four groups: HCD, AACD, DD, and control. The relevant dust suspensions were administered to the experimental groups, and physiological saline was administered to the control group intranasally a total of five times over a 15-day period, once every 3 days. Subsequently, bronchoalveolar lavage fluid, blood, and lung tissues were analyzed. Results: An increase in emphysema was observed in all exposure groups, and this increase was significant in the AAC and HC groups. Inflammation and alveolar wall thickness increased in the HC and DD groups. Goblet cell hyperplasia was detected only in the HC group; increases in CD68⁺ macrophages and TGF-β, as well as elevated hydroxyproline, were detected only in the DD group and supported the fibrotic response (p < 0.05). Neutrophil increase was specific to the AAC group. In all exposure groups, Akt/NF-κB pathway proteins, caspase-9, and MPO levels increased, while Bcl-xl levels decreased (p < 0.05). The findings indicate that the examined dusts trigger inflammation and apoptosis. Conclusion: Exposure to HCD, AACD, and DD causes lung damage by modulating the Akt/NF-κB signaling cascade; it enhances the apoptotic process through Bcl-xl suppression and caspase-9 increase. DD also induces a marked fibrotic response. Full article

This paper presents the design and implementation of a low-cost microcontroller-based dual-channel fan controller optimized for high-power audio amplifiers, yet adaptable to power supplies, electronic loads, and other thermally intensive systems. Unlike conventional designs that drive all fans uniformly, the proposed solution provides fully independent cooling via dual I²C temperature sensors, predictive trend analysis, and multi-stage hysteresis. The controller incorporates advanced features including an anti-dust startup sequence, predictive boost with latching, active cross-cooling, anti-heat-soak protection, and stall detection via tachometer monitoring, complemented by LED-based fault signaling and automatic channel muting during overheating or fan failure. Hardware support for 12 V and 24 V fans, dual power-input options, and a compact PCB layout enhance integration flexibility. The firmware employs temperature-driven PWM mapping with EMA filtering and multi-level hysteresis. The experimental results confirm that all implemented features operate as intended, with each function demonstrating clear practical relevance, whether in improving responsiveness, preventing heat accumulation, or enhancing system reliability under a wide range of operating conditions. Full article

Underground mines’ complex environments with dim lighting and high dust and humidity hamper feature extraction and reduce detection accuracy. To address this, we propose a low-light image enhancement-based target detection algorithm. Firstly, LIENet enhances low-light image quality and brightness via a dual-gamma curve and non-reference loss function-guided iterations. Secondly, the hierarchical feature extraction (HFE) method with a dual-branch structure captures long-term and local correlations, focusing on critical corner regions. Finally, HFE is combined with a feature pyramid structure for comprehensive feature representation through a top-down global adjustment. Our method, validated on a self-built dataset, outperforms other algorithms with an mAP@0.5 of 96.96% and mAP@0.5:0.95 of 71.1%, proving excellent low-light detection performance in mines. Full article

The intricate layout of underground mine tunnels and the operation of large-scale mining equipment create extensive blind zones, leading to an average of 17 personnel collision accidents per 100,000 working hours in China’s metal mines. To tackle this issue, we constructed a specialized Jinchuan Underground Mining Personnel Dataset covering intersecting tunnels and long straight tunnels, with precise bounding box annotations for personnel locations under varying illumination and dust conditions. We propose the Attention-Augmented PointPillars for Enhanced Mining Personnel Detection. Incorporating Recursive Gated Convolutions into the feature extraction network enables long-range modeling and higher-order spatial interactions. Moreover, the pyramidal design in gⁿ Conv with channel width gradually increasing during spatial interactions enhances the model’s efficiency in processing complex spatial information. Additionally, a Channel and Spatial Attention module integrating spatial and channel attention feature fusion strengthens feature expression via multiple weighting mechanisms. Field tests in Jinchuan underground mine show optimal performance with a batch size of 8, a learning rate of 0.003, and a spatial interaction order of 5, achieving 3% higher accuracy than the original network. Furthermore, comparisons with mainstream methods on the Underground Personnel Dataset confirm our method’s state-of-the-art performance. Full article

Lime kiln dust (LKD), a by-product of the paper industry, generates about 100 tonnes of waste per 400,000 tonnes of kraft paper produced, while global aquaculture yields more than 16 million tonnes of oysters annually, 65–90% of which is made up of shells. This study explores their valorisation in the production of more eco-friendly mortars by partially replacing hydrated lime with LKD and oyster shell powder (OSP). In addition, a vinegar solution (VS), prepared by reacting oyster shells with white vinegar (~5% acetic acid), was used as an alternative mixing liquid instead of water. The LKD and OSP were tested at different substitution levels, showing promising mechanical performance, supporting their use as sustainable alternatives in mortar production. Replacement levels of 25%, 50% and 90% achieved compressive strengths ≥ 0.4 MPa at 28 days. At 28 days, the reference lime mortar prepared with water reached 0.83 MPa, while the use of the vinegar solution increased the compressive strength to 1.86 MPa, representing an improvement of approximately 124%. Regarding binder replacement by wastes, the most efficient mechanical performance was obtained for mixtures with 50% LKD substitution, reaching 2.04 MPa at 28 days and 3.11 MPa at 60 days, increasing by 10% and 43%, respectively, while mixtures incorporating oyster shell powder showed more stable mechanical behaviour across substitution levels. Using a hot-mixing process with quicklime in the presence of the vinegar-based solution and sand may account for the higher strengths, due to the heat/steam generated during lime hydration prior to moulding and verified by microscopy. In addition, VS-containing mixes showed higher aragonite contents and detectable phosphorus-bearing compounds, which may further contribute to matrix densification and strengthening. Overall, the results indicate that the combined use of uncalcined calcium-based wastes and a vinegar-based solution can contribute to the development of calcium-based mortars with good mechanical performance, supporting circular economy strategies and the reduction in calcined-binder use in construction materials. Full article

Fire hazards in closed industrial environments pose a significant threat to workers, infrastructure and production processes. Traditional detection systems, such as smoke and heat detectors, often have limitations in their settings, including delayed response time and a tendency for false alarms due to non-fire factors such as dust, humidity and vapors. This paper researches the applicability of gas sensors as an alternative or complementary method for early fire detection. This research presents an experimental evaluation of six gas sensors integrated with a microcontroller. Tests were conducted in a controlled environment, simulating industrial conditions by monitoring the combustion of different materials, such as wood, plastic and textile. Sensor responses were analyzed at horizontal distances of 2 m and 4 m from the fire source. Results show that all sensors detected combustion byproducts, with those at 2 m exhibiting a faster response and higher concentration readings. The findings confirm that a multi-sensor approach significantly increases detection reliability and enables an earlier response compared to conventional systems. Full article