Search Results (134)

This study conducted systematic experimental research on methane safety issues in industrial production environments, with a particular focus on the impacts of high-temperature conditions and complex atmospheres on methane explosion characteristics. The research team designed and constructed a dedicated combustible gas explosion experimental setup, performing in-depth experimental analyses across a broad temperature range from 25 °C to 600 °C. The results demonstrate that elevated temperatures significantly reduced the methane’s lower explosion limit (LEL), with the LEL decreasing to approximately 40% of its room-temperature value at 600 °C. The investigation systematically examined the influence mechanisms of common industrial atmospheric components, including carbon dioxide (CO₂), ammonia (NH₃), oxygen (O₂), and water vapor (H₂O) on methane explosion behavior. Key findings reveal that CO₂ exhibited notable suppression effects, increasing methane’s LEL by approximately 15% per 10% increment in CO₂ concentration. NH₃ demonstrated dual mechanisms, promoting methane explosions at low concentrations (<5%) while inhibiting them at higher concentrations. Increased O₂ concentration significantly expanded the methane’s explosive range, with the LEL decreasing by about 22% when O₂ concentration increased from 21% to 30%. Water vapor manifested differentiated impacts depending on temperature regimes, primarily elevating LEL through dilution effects below 200 °C while reducing LEL via radical reaction promotion above 400 °C. Furthermore, this study reveals synergistic coupling effects between temperature and gas components—for instance, CO₂’s suppression efficacy weakened under high temperatures, whereas NH₃’s promotion effect intensified. These discoveries provide scientific foundations for formulating industrial safety standards, designing explosion-proof equipment, and conducting risk assessments in production processes. Full article

This study analyzes the risks posed by high-temperature summer conditions to atmospheric storage tanks containing acrylic acid and proposes mitigation measures. Recent increases in heat waves and tropical nights have led to an increase in the temperatures of acrylic acid storage tanks. This temperature increase results in higher vapor pressure and promotes spontaneous polymerization, thereby increasing the risk of explosions in atmospheric storage tanks. Hazard and operability (HAZOP) analysis identified explosions due to pressure buildup as a major risk scenario. To mitigate this risk, a spray-tower system was introduced through a layer of protection analysis (LOPA), which effectively reduced the hazards associated with atmospheric storage tanks. Additionally, the removal of flame-arrester replacement operations not only achieves economic benefits, such as reduced replacement costs and labor time, but also enhances safety by eliminating worker exposure to hazardous chemicals. These findings have significant implications for improving safety at industrial sites and highlight the potential economic benefits of preventing chemical accidents. Full article

Gas distribution stations (GDSs), pivotal nodes in long-distance natural gas transportation networks, are susceptible to catastrophic fire and explosion accidents stemming from system failures, thereby emphasizing the urgency for robust safety measures. While previous studies have mainly focused on gas transmission pipelines, GDSs have received less attention, and existing risk assessment methodologies for GDSs may have limitations in providing accurate and reliable accident probability predictions and fault diagnoses, especially under data uncertainty. This paper introduces a novel dynamic accident probability assessment framework tailored for GDS under data uncertainty. By integrating Bayesian network (BN) modeling with fuzzy expert judgments, frequentist estimation, and Bayesian updating, the framework offers a comprehensive approach. It encompasses accident modeling, root event (RE) probability estimation, undesired event (UE) predictive analysis, probability adaptation, and accident diagnosis analysis. A case study demonstrates the framework’s reliability and effectiveness, revealing that the occurrence probability of major hazards like vapor cloud explosions and long-duration jet fires diminishes significantly with effective safety barriers. Crucially, the framework acknowledges the dynamic nature of risk by incorporating observed failure incidents or near-misses into the assessment, promptly adjusting risk indicators like UE probabilities and RE criticality. This underscores the importance for decision-makers to maintain a heightened awareness of these dynamics, enabling swift adjustments to maintenance strategies and resource allocation prioritization. By mitigating assessment uncertainty and enhancing precision in maintenance strategies, the framework represents a significant advancement in GDS safety management, ultimately striving to elevate safety and reliability standards, mitigate natural gas distribution risks, and safeguard public safety and the environment. Full article

In this study, an interconnection was formed between a Cu/SnAg pillar bump and an Ni-less surface-treated Cu pad through laser-assisted bonding (LAB), and its bonding characteristics were evaluated. The LAB process influences the bond quality and mechanical strength based on the laser irradiation time and laser power density. The growth of the intermetallic compound (IMC) in the joint cross-section was observed via FE-SEM analysis. Under optimized LAB conditions, minimal IMC growth and high bonding strength were achieved compared to conventional thermo-compression bonding (TCB) and mass reflow (MR) processes. As the laser irradiation time and laser power density increased, solder splashing was observed at bump temperatures above 300 °C. This is hypothesized to be due to the rapid temperature rise causing the flux to vaporize explosively, resulting in simultaneous solder splashing. With increasing laser power density, the failure mode transitioned from the solder to the IMC. Full article

Hydrogen peroxide (H₂O₂) detection in both liquid and gas phases has garnered significant attention due to its importance in various biological and industrial processes. Monitoring H₂O₂ levels is essential for understanding its effects on biology, industry, and the environment. Significant advancements in the physical dimensions and performance of biosensors for H₂O₂ detection have been made, mainly through the integration of fluorescence techniques and nanotechnology. These advancements have resulted in more sensitive, selective, and versatile detection systems, enhancing our ability to monitor H₂O₂ in both liquid and gas phases effectively. However, limited comprehensive reviews exist on the detection of vaporized H₂O₂, which is used in disinfection and the production of explosive agents, making its detection vital. This review provides an overview of recent progress in nanostructured fluorescence sensors for H₂O₂ detection, covering both liquid and gas phases. It examines various fluorescence-based detection methods and focuses on emerging nanomaterials for sensor development. Additionally, it discusses the dual applications of H₂O₂ detection in biomedical and non-biomedical fields, offering insights into the current state of the field and future directions. Finally, the challenges and perspectives for developing novel nanostructured fluorescence sensors are presented to guide future research in this rapidly evolving area. Full article

This study investigated the inhibitory effect and mechanism of modified ultrafine ABC powder on the explosion of a methane (CH₄)/coal dust mixed system. Through experiments, it was found that the addition of ABC powder significantly weakened the deflagration characteristics of the CH₄/coal dust mixture system. During decomposition, heat was absorbed to generate ammonia and phosphoric acid. Inert gases such as CO₂ and water vapor produced during decomposition could dilute the oxygen concentration. Phosphate ions produced during the decomposition of ammonium phosphate would bind with free radicals during combustion, reducing their reactivity. The explosion reaction was suppressed through a dual mechanism of physical cooling and chemical consumption of free radicals. The experimental results showed that the weight loss rate of modified ABC powder was 49% at 800 °C, while the weight loss rate of unmodified ABC powder was 78%. The modified ABC powder had better thermal stability and could absorb more heat at high temperatures, further suppressing explosive reactions. This study provides a new modification scheme for explosion suppressants for coal mine safety, which has important theoretical and practical application value. Full article

To mitigate the risk of hydrogen leakage in ship fuel systems powered by internal combustion engines, a Bayesian network model was developed to evaluate the risk of hydrogen fuel leakage. In conjunction with the Bow-tie model, fuzzy set theory, and the Noisy-OR Gate model, an in-depth analysis was also conducted to examine both the causal factors and potential consequences of such incidents. The Bayesian network model estimates the likelihood of hydrogen leakage at approximately 4.73 × 10⁻⁴ and identifies key risk factors contributing to such events, including improper maintenance procedures, inadequate operational protocols, and insufficient operator training. The Bow-tie model is employed to visualize the causal relationships between risk factors and their potential consequences, providing a clear structure for understanding the events leading to hydrogen leakage. Fuzzy set theory is used to address the uncertainties in expert judgments regarding system parameters, enhancing the robustness of the risk analysis. To mitigate the subjectivity inherent in root node probabilities and conditional probability tables, the Noisy-OR Gate model is introduced, simplifying the determination of conditional probabilities and improving the accuracy of the evaluation. The probabilities of flash or pool fires, jet fires, and vapor cloud explosions following a leakage are calculated as 4.84 × 10⁻⁵, 5.15 × 10⁻⁵, and 4.89 × 10⁻⁷, respectively. These findings highlight the importance of strengthening operator training and enforcing stringent maintenance protocols to mitigate the risks of hydrogen leakage. The model provides a valuable framework for safety evaluation and leakage risk management in hydrogen-powered ship fuel systems. Full article

To address the problems of the reduced evaporation rate and increased ignition time of kerosene droplets at sub-atmospheric pressures and high temperatures, boron and ethanol/water were selected as additives to be blended with RP-3 kerosene, respectively. The effects of different types of blended fuels on the evaporation, micro-explosion, and spontaneous ignition characteristics of RP-3 kerosene droplets were tested and compared using an independently designed, high-temperature, controlled-pressure experimental droplet system. A low-pressure environment (0.4 bar) promoted the high-intensity micro-explosion of RP-3/B and RP-3/water/ethanol droplets while reducing the number of puffing events. A comparative study of RP-3/B and RP-3/ethanol/water found that ethanol/water blended fuels had a higher micro-explosion intensity (1000–10,000 vs. 0.2–15 mm/s) and shorter droplet lifetimes and self-ignition times at low pressure. The 30%water fuel (30 vol.%water in water/ethanol sub-droplet) had the shortest ignition/breakup time, with an ignition time of 0.5715 s at 0.8 bar, 26.92% shorter than RP-3’s 0.782 s. This 30%water fuel mixture can increase the release rate of combustible vapors prior to ignition by inducing puffing and micro-explosions at high temperatures. Full article

Zenith Tropospheric Delay (ZTD) is a significant source of atmospheric error in the Global Navigation Satellite System (GNSS). Developing a high-accuracy ZTD prediction model is essential for both GNSS positioning and GNSS meteorology. To address the challenges of incomplete information extraction and gradient explosion present in current single and combined neural network models that utilize serial ensemble learning, this study proposes a VMD-Informer-BiLSTM-EAA hybrid model based on a parallel ensemble learning strategy. Additionally, it takes into account the non-stationarity of the ZTD sequence. The model employs the Variational Mode Decomposition (VMD) method to address the non-stationarity of ZTD. It utilizes both the informer and Bidirectional Long Short-Term Memory (BiLSTM) architectures to learn ZTD data in parallel, effectively capturing both long-term trends and short-term dynamic changes. The features are then fused using the Efficient Additive Attention (EAA) mechanism, which assigns weights to create a more comprehensive representation of the ZTD data. This enhanced representation ultimately leads to improved predictions of ZTD values. We fill in the missing parts of the GNSS-derived ZTD using the ZTD data from ERA5, sourced from the IGS stations in the Australian region, specifically at 12 IGS stations. These interpolated data are then used to develop a VMD-Informer-BiLSTM-EAA hybrid model for ZTD predictions with a one-year forecast horizon. We applied this model to predict the ZTD for each IGS station in our study area for the year 2021. The numerical results indicate that our model outperforms several comparative models, such as VMD–Informer, Transformer, BiLSTM and GPT3, based on the following key metrics: a Root Mean Square Error (RMSE) of 1.43 cm, a Mean Absolute Error (MAE) of 1.15 cm, a Standard Deviation (STD) of 1.33 cm and a correlation coefficient (R) of 0.96. Furthermore, our model reduces the training time by 8.2% compared to the Transformer model, demonstrating superior prediction performance and robustness in long-term ZTD forecasting. This study introduces a novel approach for high-accuracy ZTD modeling, which is significantly beneficial for precise GNSS positioning and the detection of water vapor content. Full article

Incidents involving hazardous materials (HAZMAT incidents) can impact human health and the environment. For the development of risk mitigation strategies, it is essential to understand the circumstances of such incidents. A retrospective study (2016–2023) of acute occupational HAZMAT incidents involving multiple patients (>1, including workers, emergency responders and bystanders) reported to the Dutch Poisons Information Center was conducted. We only included incidents that occurred during the performance of work or as a result of a disruption of a work-related process. Patient characteristics, exposure circumstances (such as the substances involved, chemical phase, and type of release (e.g., spill/release or fire/explosion)) and business classes were analyzed to identify risk factors. From 2016 to 2023, the DPIC was consulted about 516 HAZMAT incidents. Inhalation was the most common route of exposure (89%). Patients were often exposed to chemical asphyxiants (n = 156) and acids (n = 151). Most incidents occurred in fixed facilities (n = 447), while 49 incidents occurred during transport. The primary cause was a spill/release (n = 414), followed by a fire/explosion (n = 65). Most patients were exposed to a gas/vapor (n = 421), followed by a liquid (n = 59) or solid (n = 28). Incidents frequently occurred in industry (20%). The majority of patients reported mild to moderate health effects. Surveillance data on HAZMAT incidents are essential for incident preparedness. Poison Center data can help identify risk factors, which can be used to develop risk mitigation strategies to prevent future incidents. Full article

Li-ion batteries are at risk of explosions caused by fires, primarily because of the high energy density of Li ions, which raises the temperature. Battery cases are typically made of plastic, aluminum, or SAF30400. Although plastic and aluminum aid weight reduction, their strength and melting points are low. SAF30400 offers excellent strength and corrosion resistance but suffers from work hardening and low high-temperature strength at 700 °C. Additionally, Ni used for plating has a low current density of 25% international copper alloy standard (ICAS). SAF2507 is suitable for use as a Li-ion battery case material because of its excellent strength and corrosion resistance. However, the heterogeneous microstructure of SAF2507 after casting and processing decreases the corrosion resistance, so it requires solution heat treatment. To address these issues, in this study, SAF2507 (780 MPa, 30%) is solution heat-treated at 1100 °C after casting and coated with Ag (ICAS 108.4%) using physical vapor deposition (PVD). Ag is applied at five different thicknesses: 0.5, 1.0, 1.5, 2.0, and 2.5 μm. The surface conditions and electrochemical properties are then examined for each coating thickness. The results indicate that the PVD-coated surface forms a uniform Ag layer, with electrical conductivity increasing from 1.9% ICAS to 72.3% ICAS depending on the Ag coating thickness. This enhancement in conductivity can improve Li-ion battery safety on charge and use. This result is expected to aid the development of advanced Li-ion battery systems in the future. Full article

Zenith tropospheric delay (ZTD) is a significant atmospheric error that impacts the Global Navigation Satellite System (GNSS). Developing a high-precision, long-term forecasting model for ZTD can provide valuable insights into the overall trends of predicted ZTD, which is essential for improving GNSS positioning and analyzing changes in regional climate and water vapor. To address the challenges of incomplete information extraction and gradient explosion in a single neural network when forecasting ZTD long-term, this study introduces an Informer–LSTM Hybrid Prediction Model. This model employs a parallel ensemble learning strategy that combines the strengths of both the Informer and LSTM networks to extract features from ZTD data. The Informer model is effective at capturing the periodicity and long-term trends within the ZTD data, while the LSTM model excels at understanding short-term dependencies and dynamic changes. By merging the features extracted by both models, the prediction capabilities of each can complement one another, allowing for a more comprehensive analysis of the characteristics present in ZTD data. In our research, we utilized ERA5-derived ZTD data from 11 International GNSS Service (IGS) stations in Europe to interpolate the missing portions of GNSS-derived ZTD. We then employed this interpolated data from 2016 to 2020, along with an Informer–LSTM Hybrid Prediction Model, to develop a long-term prediction model for ZTD with a prediction duration of one year. Our numerical results demonstrate that the proposed model outperforms several comparative models, including the LSTM–Informer based on a serial ensemble learning model, as well as the Informer, Transformer, LSTM, and GPT3 empirical ZTD models. The performance metrics indicate a root mean square error (RMSE) of 1.91 cm, a mean absolute error (MAE) of 1.45 cm, a mean absolute percentage error (MAPE) of 0.60, and a correlation coefficient (R) of 0.916. Spatial distribution analysis of the accuracy metrics showed that predictive accuracy was higher in high-latitude regions compared to low-latitude areas, with inland regions demonstrating better performance than those near the ocean. This study introduced a novel methodology for high-precision ZTD modeling, which is significant for improving accurate GNSS positioning and detecting water vapor content. Full article

Hydrogen is considered the next energy to replace fossil fuels, but it must be handled with care given that it is a flammable gas. A barrier wall is an effective way to mitigate the effect of an explosion, and to build a safe barrier wall, research on hydrogen explosions is necessary. Experiments and CFD (computational fluid dynamics) are two commonly used methods, but both are costly to use under any condition. Machine learning can be used to enhance the data from experiments and CFD as the trained model can predict explosion pressure levels very rapidly under various conditions. We propose the prediction of a hydrogen VCE (vapor cloud explosion) with a barrier wall using various machine learning methods. This research uses CFD simulation data from KAERI (Korea Atomic Energy Research Institute) as training data. MLP (multi-layer perceptron), LSTM (long short-term memory), and the Transformer architectures are used to train the hydrogen VCE and are compared. In our research, MLP produces the best score among all learning processes, with an R² value exceeding 0.97, outperforming both LSTM and Transformer in terms of accuracy and speed. The trained machine learning model can be used to build safe barrier walls in hydrogen refueling stations. Evaluating the safe distance from the barrier wall and evaluating the optimal position of the barrier wall are possible usages. Full article

Coal mining frequently sees explosions caused by methane/coal dust mixtures, resulting in significant harm to people and property damage. This study utilized the Hartmann pipe experiment to investigate the inhibition mechanisms of ultrafine water mist (UWM) containing phosphorus-based sodium inhibitors (sodium dihydrogen phosphate (NaH₂PO₄) and sodium phytate (C₆H₆Na₁₂O₂₄P₆)) on methane/coal dust hybrid explosions. The results indicate that UWM containing NaH₂PO₄ and C₆H₆Na₁₂O₂₄P₆ significantly reduces flame propagation velocity, flame height, and flame temperature, thereby effectively inhibiting the development of methane/coal dust hybrid explosion flames. UWM containing C₆H₆Na₁₂O₂₄P₆ exhibited superior inhibition performance, reducing the flame temperature to 157.6 °C, the peak flame propagation velocity by 2.26 m/s, and the flame height by 5.66 mm. The inhibition mechanism of UWM containing phosphorus-based sodium inhibitors primarily involves physical heat absorption and chemical inhibition. The evaporation of UWM absorbs heat, thereby reducing the temperature in the reaction zone. Simultaneously, it generates a large amount of water vapor, which dilutes the fuel concentration per unit volume and reduces the collision frequency between fuel molecules and oxygen. The active free radicals (such as sodium oxygen radical (NaO), metaphosphoric acid (HPO₂), HOPO (peroxyphosphate radical), etc.) produced by the decomposition of NaH₂PO₄ and C₆H₆Na₁₂O₂₄P₆ react with free radicals (O, H, and OH), effectively reducing the concentration of free radicals, interrupting the chain reaction, and weakening the explosive severity. The decomposition products of the phosphorus-sodium components increase the heat capacity of the combustion products, dilute and isolate the combustion zone, and further reduce the explosive severity. These findings provide significant scientific and engineering support for the safe management of coal mines. Full article

Explosive-detecting canines (EDCs) show high sensitivity in detecting explosives that they are trained to detect. The ability of canines to detect explosive residues to the parts per trillion level can sometimes result in nuisance alerts. These nuisance alerts can occur when various materials (i.e., substrates) are exposed to volatile organic compounds (VOCs) present in explosive mixtures, leading to contamination—the unintended absorption or adsorption of VOCs by the substrate. Chemical constituents such as taggant, plasticizer, and residual solvent in explosives are often composed of VOCs that canines are trained on to detect explosives. Composition C-4 (C4) is a common explosive that EDCs are trained to detect and hence is this study’s focus. Common VOCs of interest emitted from C4 include 2,3-dimethyl-2,3-dinitrobutane (DMNB), 2-ethyl-1 hexanol (2E1H), and cyclohexanone. In this study, we developed a protocol for comparing different substrates such as cotton, cardboard, wood, sheet metal, and glass that were exposed to volatiles from C4. 1-bromooctane (1-BO) was used as a single-odor compound to compare the complex odor originating from C4. Triplicates of substrates such as cotton, wood, cardboard, sheet metal, and glass were exposed to 1 g of C4 in a paint can for one week and the substrates were then extracted using various extraction methods such as liquid injection, direct SPME, and headspace analysis coupled with gas chromatography–mass spectrometry. An extraction time study was performed to determine the optimal extraction time for SPME analysis, and it was found to be 20 min. Comparison of extraction methods revealed that SPME surpassed other techniques as DMNB was found on all substrates using SPME. It was observed that porous substrates such as wood and cardboard have a higher retention capacity for volatiles in comparison to non-porous substrates such as sheet metal and glass. Finally, swabbing was evaluated as a sampling technique for the substrates of interest and the extracts were analyzed using the total vaporization–solid phase microextraction (TV-SPME) technique. No volatiles associated with C4 were identified on conducting a GC-MS analysis, suggesting that swabbing is not an ideal technique for analysis of substrates exposed to C4. Full article