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In this study, a new method is proposed to quantitatively determine the extraction of boreholes under the effect of time. The main purpose of this method is to divide the gas drilling holes in the monitoring time into two stages: the early stage of extraction and the middle stage of extraction. The early stage of extraction uses the gas pressure difference to detect the gas tightness of the drilling holes and determine the type of gas leakage of the drilling holes. Moreover, the unqualified drilling holes are repaired immediately to ensure that all the drilling holes in the middle stage of the monitoring are the qualified holes at the early stage of the inspection. In the middle of extraction, the average attenuation rate of the fitting attenuation curve of pure gas extraction in the extraction borehole is calculated by monitoring, the growth rate of the linear fitting of the pure air leakage is calculated, and their ratios are used as the basis for judging the repair value of the gas leak borehole and the gas leak borehole. This quantitative evaluation method is used to monitor the extraction effect of four test holes in a field test conducted at Ping Coal Mine 10. The results indicate that, after adopting in situ downhole repair technology in the early stage of extraction, the average air leakage of Borehole No. 4, which failed due to air leakage in the borehole, decreased from 9.33 to 1.26 L/min (a reduction of 86.5%). Similarly, that of Borehole No. 3, which failed due to penetration of fissures in the surrounding rock, had an air leakage drop from 8.35 to 1.34 L/min (a reduction of 84.0%). These improvements demonstrate the remarkable effectiveness of the method. The quantitative evaluation method established in this study is not only simple to operate but also stable and reliable. Thus, it is feasible to be applied in the field, which provides important technical support for the improvement in gas extraction effect during coal mine safety production. [ABSTRACT FROM AUTHOR]

A well-connected network formed by multi-scale fractures is a key factor in the formation of high-quality reservoirs and the achievement of high and stable oil and gas production in tight sandstones. Taking the Upper Paleozoic of the Qingshimao gas field in the Ordos Basin, China, as an example, based on data from image logs, cores, and thin sections, fine quantitative characterization of multi-scale natural fractures in tight sandstone reservoirs was carried out. We also established a method for dividing network patterns of multi-scale fractures and discussed the effect of each fracture network pattern on the gas enrichment and production capacity. Results indicate regular changes in the length, density, aperture, porosity, permeability, and connectivity of natural fractures at different scales. Based on the spatial combination patterns and connectivity of fractures of different scales, four types of fracture network patterns were established: multi-scale fracture network with high density and multi-orientations, multi-scale fracture network with moderate-high density and dual orientations, small-scale fracture network with moderate density and dual orientations, small-scale fracture network with low density and single orientation. The first fracture network pattern can destroy the integrity of the cap layer, causing natural gas leakage. The second fracture network pattern is a favorable area for natural gas enrichment. The third fracture network pattern requires the use of hydraulic fracturing to obtain commercial airflow. The fourth fracture network pattern has little effect on reservoir control and storage. The study of natural fractures in tight sandstone reservoirs is usually based on a single-scale perspective. Understanding the development characteristics of multi-scale fractures and their controlling effects on the reservoir helps to comprehensively understand the spatial configuration relationship of multiscale fracture network structure patterns and promotes the development of multi-scale fractures in tight reservoir research. [ABSTRACT FROM AUTHOR]

As a new energy system, the hydrogen-electric coupling system（HECS） involves complex conditions with multiple hazards such as electricity, hydrogen, heat, and high pressure, and there are complex safety risk issues. This paper proposes a hydrogen leakage risk assessment method of HECS based on dynamic Bayesian networks (DBN). Firstly, the bow-tie model is mapped to the DBN model, and the prior probability of the DBN model is obtained using intuitionistic fuzzy theory, Dempster-Shafer evidence theory, and an improved similarity aggregation algorithm. Then, leakage noise or gate models are introduced to improve the conditional probability table of the DBN model, and Weibull distributions are used to construct the probability transfer process between different time periods. Finally, through practical case studies, it is shown that the proposed method can effectively predict the dynamic probability and potential consequences of failure events through time series information. Sensitivity analysis proves the practicality and robustness of the proposed method. The research results can provide theoretical support for risk assessment and daily maintenance of HECS. • Established a DBN-based risk assessment method for hydrogen leakage in HECS. • Enhancing prior probability with intuitive fuzzy set theory and Dempster-Shafer evidence theory. • Obtained probability time series and high-failure probability nodes of HECS. [ABSTRACT FROM AUTHOR]

The field of emergency risk management in chemical parks has been characterized by a lack of fast, precise and dynamic prediction methods. The application of computational fluid dynamics (CFD) models, which offer the potential for dynamic and precise prediction, has been hindered by high computational costs. Therefore, taking liquid benzene as a case study, this paper combined machine learning (ML) algorithms with a CFD-based precise prediction model, to develop an ML model for fast dynamic prediction of heavy gas leakage consequences in chemical parks. Employing the CFD data as the input, the prediction models were developed using ML algorithms, refined with Bayesian optimization for parameter tuning. This study utilized PHOENICS software to establish a dynamic prediction model for the diffusion of liquid benzene leakage, validated by Burro nine experiment data. Comparative analyses of models based on five ML algorithms were conducted to evaluate the reliability of their predictions using both CFD standard and noisy data. The results indicated that temperature had the most significant effect on the consequences of the leakage accidents among four key factors (wind speed, temperature, leakage aperture and atmospheric stability), followed by wind speed. These factors served as input variables for ML model training. Among the models evaluated, the eXtreme Gradient Boosting (XGBoost) model showed superior performance, irrespective of the presence of noise in the data. An XGBoost-based fast prediction model was ultimately developed for predicting the consequences of liquid benzene leakage. A case analysis was conducted to validate the feasibility of the model prediction. The relative errors between the predicted and actual values of the model for acute exposure guideline level-1 (AEGL-1), AEGL-2, and AEGL-3 distances were 2.70%, 2.58%, and 0.23%, respectively. Furthermore, the XGBoost model completed the prediction in only 0.218 s, a stark contrast to the hours necessitated by the CFD model, thus offering substantial computational time savings while maintaining high accuracy levels. This paper introduces an ML model for fast dynamic prediction of heavy gas leakage, enabling chemical parks to make more timely and accurate decisions in emergency risk management. [ABSTRACT FROM AUTHOR]

As a paradigm of clean energy, hydrogen is gradually attracting global attention. However, its unique characteristics of leakage and autoignition pose significant challenges to the development of high-pressure hydrogen storage technologies. In recent years, numerous scholars have made significant progress in the field of high-pressure hydrogen leakage autoignition. This paper, based on diffusion ignition theory, thoroughly explores the mechanism of high-pressure hydrogen leakage autoignition. It reviews the effects of various factors such as gas properties, burst disc rupture conditions, tube geometric structure, obstacles, etc., on shock wave growth patterns and autoignition characteristics. Additionally, the development of internal flames and propagation characteristics of external flames after ignition kernels generation are summarized. Finally, to promote future development in the field of high-pressure hydrogen energy storage and transportation, this paper identifies deficiencies in the current research and proposes key directions for future research. [ABSTRACT FROM AUTHOR]

Gas flexible pipes are critical multi-layered equipment for offshore oil and gas development. Under high pressure conditions, small molecular components of natural gas dissolve into the polymer inner liner of the flexible pipes and further diffuse into the annular space, incurring annular pressure build-up and/or production of acidic environment, which poses serious challenges to the structure and integrity of the flexible pipes. Gas permeation in pipes is a complex phenomenon governed by various factors such as internal pressure and temperature, annular structure, external temperature. In a long-distance gas flexible pipe, moreover, gas permeation exhibits non-uniform features, and the gas permeated into the annular space flows along the metal gap. To assess the complex gas transport behavior in long-distance gas flexible pipes, a mathematical model is established in this paper considering the multiphase flow phenomena inside the flexible pipes, the diffusion of gas in the inner liner, and the gas seepage in the annular space under varying permeable properties of the annulus. In addition, the effect of a variable temperature is accounted. A numerical calculation method is accordingly constructed to solve the coupling mathematical equations. The annular permeability was shown to significantly influence the distribution of annular pressure. As permeability increases, the annular pressure tends to become more uniform, and the annular pressure at the wellhead rises more rapidly. After annular pressure relief followed by shut-in, the pressure increase follows a convex function. By simulating the pressure recovery pattern after pressure relief and comparing it with test results, we deduce that the annular permeability lies between 123 and 512 mD. The results help shed light upon assessing the annular pressure in long distance gas flexible pipes and thus ensure the security of gas transport in the emerging development of offshore resources. [ABSTRACT FROM AUTHOR]

Toxic heavy gas leakage in enclosed workplaces can result in severe safety accidents. Reasonable estimations of toxic heavy gas leakage and its subsequent concentration distribution are crucial for disaster assessment and emergency response. This study combines a three-dimensional (3D) gas dispersion model developed based on computational fluid dynamics (CFD) with the iterative ensemble Kalman filter (IEnKF) algorithm for accurate estimation of heavy gas leakage source parameters and detailed prediction of concentration distributions, with a case study of hydrogen sulfide (H2S) leakage. The inversions of two uncertain parameters are considered: H2S leakage velocity and air supply velocity. The results show that the 3D CFD-based gas dispersion model can work well with the robust IEnKF algorithm to predict the spatiotemporal concentration distribution of heavy gas with high confidence. Moreover, the multiple correction procedures performed in the IEnKF module can greatly improve the efficiency and accuracy of parameter estimations compared to the single correction of the EnKF algorithm. When the maximum number of iterations is set to ten, the assimilation time steps required to achieve satisfactory estimations of uncertain parameters are reduced by 86.67%; the accuracy of H2S leakage velocity and air supply velocity estimation is improved by 10.59% and 46.25%, respectively. In addition, after 13 assimilation time steps, the estimated H2S leakage velocity (17.2 m/s) is almost equal to the assumed true value (17 m/s). This study provides a novel approach to assess the impact of poisonous dense gas leakage in enclosed workplaces. [ABSTRACT FROM AUTHOR]

In acoustic signal–based natural gas pipeline leak detection, it is a technical difficulty to effectively remove the strong background noise hidden in the acoustic signal. Unfortunately, existing techniques usually study denoising in isolation from the model, and do not consider the synergistic effect of integrating denoising with the model to update filtering parameters adaptively, which limits the performance development of the diagnostic model. To solve this problem, this paper proposes a fault diagnosis method that incorporates acoustic signal hybrid filtering and a one-dimensional convolutional neural network (1D-CNN) to jointly update the denoising parameters. Firstly, the acoustic signal is sampled at a fixed frequency by the sensor unit. Secondly, a hybrid filtering method with low-frequency characteristics is designed, which integrates low-pass filtering and median filtering to eliminate background noise. Finally, an adaptive fault diagnosis network that fuses acoustic signal denoising with 1D-CNN feature extraction is developed. The network updates the filtering parameters during training based on the model recognition accuracy, so that the denoising parameters and model weights change dynamically. The experimental results show that the method is capable of performing adaptive parameter search with 97.07% identification accuracy of fault type. [ABSTRACT FROM AUTHOR]

Pipelines stand as the most cost‐effective method for large‐scale transportation of CO2 from a source point to the storage site, especially over extensive distances. The potential for crack propagation following a pipeline rupture highlights the need for precise analysis of decompression wave propagation. To accurately model this, understanding the decompression wave's propagation laws becomes imperative. Although previous studies have predominantly focused on pipeline leaks within the dense phase or supercritical state, the transition from liquid to gas during leakage significantly affects the decompression wave propagation. When a gaseous CO2 pipeline ruptures, the high Joule‐–Thomson coefficient causes a swift temperature plunge, potentially leading to a gas–liquid transition. However, research on how this phase transition impacts the decompression wave characteristics is limited. To address this gap, this study proposes a transition computational fluid dynamics model to predict the decompression wave behavior. The model is validated with an industrial‐scale full‐bore rupture experiment. The results reveal that the gaseous CO2 leakage induces a pressure plateau at a certain distance from the leakage due to the gas‐liquid phase transition. The influences of initial conditions on this pressure plateau and decompression wave are also explored. This study provides valuable insights into understanding the decompression wave behaviors of gaseous CO2 pipelines, which are essential for ensuring the safety and reliability of CO2 transportation within the carbon capture and storage technology chain. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Poor sealing of gas extraction boreholes is one key to restrict gas extraction efficiency. In this paper, a novel borehole sealed quality detection device for the gas extraction of a coal mine is developed based on the theory of air leakage. By comparing the amount of gas extracted at different test points, it is possible to determine whether there is air leakage around the borehole, and the specific leakage position and leakage amount. Moreover, this device has the advantages of simple operation and a short test period. Based on the above analysis, a corresponding air leakage disposal method was proposed to handle the leaky boreholes. Field tests showed that the air–gas mixture flow in the test borehole was reduced by a factor of approximately 1.55 and the concentration of pure seam gas was increased approximately six times after the disposal of the air leaks. The combination of the leakage disposal method and the leakage detection device can accurately seal the borehole at the position of the leak, thus effectively ensuring the effectiveness of gas extraction from the borehole. The findings have important implications for improving the efficiency of gas extraction in coal mining operations. [ABSTRACT FROM AUTHOR]

Leakage from improper mask wear can significantly reduce the effectiveness of a mask. Existing mask fit test results only tell whether there is gas leakage while wearing the mask, but not where the airflow is leaking, and they do not allow for real-time adjustment of the mask wearing style. A mask fit test device is designed using schlieren technology. The technique utilizes the perturbation of light waves by airflow to visualize airflow changes that are invisible to the naked eye by converting them into images that can be seen. The mask fit test device can be used to observe the leakage of airflow from masks under different masks and different wearers, and measure the striation area of the leaked airflow. The device was used to test the fit of masks when worn by a head model or a real person. The schlieren of the leaking airflow was most pronounced when the head model wore a medical surgical mask. The leakage airflow also varies among individuals wearing the same type of mask. The effectiveness of the device was verified using a medical surgical mask, and the results obtained from the test showed that the edge leakage rate was 36.83% when the mask was worn correctly. [ABSTRACT FROM AUTHOR]

The leakage gas from a buried natural gas pipelines has the great potential to cause economic losses and environmental pollution owing to the complexity of the mountainous environment. In this study, computational fluid dynamics (CFD) method was applied to investigate the diffusion law and hazard range of buried natural gas pipeline leakage in mountainous environment. Based on cloud chart, concentration at the monitoring site and hazard range of lower explosion limit (LEL) and upper explosion limit (UEL), the influences of leakage hole direction and shape, soil property, burial depth, obstacle type on the diffusion law and hazard range are analyzed. Results show that the leakage gas is not radially diffused until it reaches the ground, and the velocity of gas diffusion to the ground and the hazard range decrease as the angle between the leaking direction and the buoyancy direction increases. Triangular and square leak holes have a faster diffusion rate and a wider hazard range than circular. The diffusion rate of leakage gas in soil rises as soil granularity and porosity increase. The time of leakage gas diffusion to the ground increases significantly with the increase of burial depth, and the hazard range reduces as burial depth increases. Boulder-type obstacles will alter the diffusion path of the leakage gas and accelerate the expansion of the hazard distance, while trench-type obstacles will cause the natural gas to accumulate in the trench and form a high concentration region slowing the expansion of the surface gas concentration. [ABSTRACT FROM AUTHOR]

Pipeline leakage seriously threatens the efficient and safe gas drainage in coal mines. To achieve the accurate detection and localization of gas drainage pipeline leakages, this study proposes a gas drainage pipeline leakage detection and localization approach based on the pressure gradient method. Firstly, the basic law of gas flow in the drainage pipeline was analyzed, and a pipeline network resistance correction formula was deduced based on the pressure gradient method. Then, a drainage pipeline model was established based on the realizable k - ε turbulence model, and the pressure and flow velocity distribution during pipeline leakage under different leakage degrees, leakage locations, and pipeline negative pressures were simulated and analyzed, thus verifying the feasibility of the pipeline leakage detection and localization method. It is concluded that the positioning errors of pipeline leakage points under different leakage degrees, different leakage positions, and different pipeline negative pressures were 0.88~1.08%, 0.88~1.49%, and 0.68~0.88%, respectively. Finally, field tests were conducted in the highly located drainage roadway 8421 of the Fifth Mine of Yangquan Coal Industry Group to verify the accuracy of the proposed pipeline leakage detection and localization method, and the relative error was about 8.2%. The results show that with increased pipeline leakage hole diameters, elevated pipeline negative pressures, and closer leakage positions to the pipeline center, the relative localization error was smaller, the localization accuracy was higher, and the stability was greater. The research results could lay the foundation for the fault diagnosis and localization of coal mine gas drainage pipeline networks and provide technical support for safe and efficient coal mine gas drainage. [ABSTRACT FROM AUTHOR]

Cooking from ancient times has evolved from using open fires to wood, gas cookers, using liquefied petroleum gas (LPG). This has also come with various adverse effects ranging from gas leakages to burnt food due to absent-mindedness, thereby creating a significant disaster that could lead to loss of life and property damage. The study aimed to reduce the rate of liquefied petroleum gas related accidents in domestic usage and improve the safety of domestic gas users. An automated method to enforce safety was proposed to avoid unwanted cooking gas flow consequences, especially in homes. The paper presents a control system using an Arduino Uno with a control design interfaced with a utensil sensor, solenoid valve, and a timer circuit to allow gas flow to commence and ignite a flame automatically. The automatic ignition apparatus, which has a high-voltage electric circuit, begins to function once the utensil detector comes in contact with silverware. The system is designed to function in different modes to ensure safety and prevent gas flow. The prototype serves as a means of curbing gas wastage and increasing the safety of people who use LPG as a source of fuel for cooking. [ABSTRACT FROM AUTHOR]

Buried pipelines, as the most common method of natural gas transportation, are prone to pipeline leakage accidents and are difficult to detect due to their harsh and concealed environment. This paper focused on the problem regarding the free dissipation of residual gas in buried gas pipelines and soil after closing the gas supply end valve after a period of leakage by numerical simulation. A multiple non-linear regression model was established based on the least squares method and multiple regression theory, and MATLAB 2016b mathematical calculation software was used to solve the problem. The research results indicated that compared to the gas leakage diffusion stage, the pressure and velocity distribution during the free dissipation stage were significantly reduced. The increase in leakage time, pipeline pressure, leakage size, and pipeline burial depth led to a large accumulation of natural gas, which increased the concentration and distribution range of gas on the same free dissipation stage monitoring line. A prediction model for natural gas concentration in the free dissipation stage was established with an average error of 7.88%. A calculation model for the safety repair time of buried gas pipeline leakage accidents was further derived to determine the safety repair time of leakage accidents. [ABSTRACT FROM AUTHOR]

Aiming at the complex characteristics of negative pressure waves in low-pressure pipelines inside of buildings, we proposed an estimation method of pressure fluctuation trends based on the robust Kalman filter and the improved VMD, which can be used for leakage detection. The reconstructed baseline signal can accurately describe the fluctuation trend of the negative pressure wave after the pressure drop, and quantitatively express the characteristic difference between the leakage condition and the gas usage condition. The robust Kalman filter was used to estimate the pressure fluctuations. The parameters of VMD were adaptively calculated based on the WAA and discrete scale space. The trend components contained in the IMFs were separated by a reconstruction based on the Fourier series. Based on the simulation signal, the method can accurately restore the trend component contained in the complex pressure signal. Based on the actual signals, the accuracy of small leakage detection is 96.7% and the accuracy of large leakage detection is 73.3%. [ABSTRACT FROM AUTHOR]

Polymer gels have shown application potential in minimizing gas channeling during CO2 enhanced oil recovery and storage processes, but the pumping pressure of preformed gels was too high, and the in‐situ cross‐linking in reservoirs was uncontrollable. Here we developed polyelectrolyte complex gels through the columbic attraction between sodium carboxymethyl cellulose (NaCMC) and protonated polyethyleneimine (PEI+) by CO2 in the presence of water. Without CO2, the viscosity of polymer complex solution is relatively low; after streaming CO2, the viscosity of the NaCMC‐PEI+ mixture aqueous solution increases significantly, forming a gel. It was also found that the complex solution shows an evolution of "sol–gel‐separated phase" as the polymer concentration increases, which could be attributed to the enhanced electrostatic attraction between CMC− and PEI+. The viscosity of the gel decreases upon increasing salinity owing to the reduced charge repulsion by the introduced inorganic salt. The CMC−‐PEI+ complex can retain its gel behavior until 60°C, after which the complex collapses. The uptake tests show that 11.34 mg CO2 can be absorbed by each gram of gel composed of 0.9 wt% NaCMC and 0.15 wt% PEI at room temperature and ambient pressure. This work provides an inexpensive route to control gas leakoff and improve CO2 absorption. [ABSTRACT FROM AUTHOR]

Predicting the concentration of dissolved gases in transformer oil is a critical activity for the early detection of potential faults. To address the prevalent issue of data leakage in current prediction methods, this paper proposes a prediction method that completely avoids data leakage. First, the Hodrick Prescott (HP) filter is used for stepwise decomposition to obtain the long-term trend and high-frequency periodic component. The high-frequency periodic component is further decomposed using singular spectrum analysis (SSA) to extract periodic features. Dispersion entropy (DE) and fuzzy entropy (FE) are utilized alongside the HP and SSA methods to determine the optimal decomposition windows during the process, enhancing the ability of the model to acquire time series features. Then, variational mode decomposition (VMD) is applied to remove noise from the high-frequency component. Finally, the long short-term memory network (LSTM) is employed to predict each decomposed component, and the network parameters undergo optimization through the sparrow search optimization algorithm (SSOA). The two case studies in this work verify that the proposed model excels over other prediction means, providing strong support for subsequent fault prediction. [ABSTRACT FROM AUTHOR]

Existing mid-infrared thermographic cameras rely on a stack of refractive lenses, resulting in bulky and heavy imaging systems that restrict their broader utility. Here, we demonstrate a lightweight metalens-based thermographic camera (MTC) enabled by a single 0.5-mm-thick, 3.7-g-weight, flat, and mass-producible metalens. The large aperture size (5 cm) of our metalens, when combined with an uncooled focal plane array, enables thermal imaging at distances of tens of meters. By computationally removing the veiling glare, our MTC realizes the temperature mapping with an inaccuracy of less than ±0.7% within the range of 35° to 700°C and shows exceptional environmental adaptability. Furthermore, by using intelligent algorithms and spectral filtering, our uncooled MTC enables visualization and quantification of the SF6 gas leakage at a long distance of 5 m, with a remarkable minimum detectable leak rate of 0.2 sccm. Our work opens the door to the lightweight and multifunctional intelligent thermal imaging systems. [ABSTRACT FROM AUTHOR]

The integration of visual algorithms with infrared imaging technology has become an effective tool for industrial gas leak detection. However, existing research has mostly focused on simple scenarios where a gas plume is clearly visible, with limited studies on detecting gas in complex scenes where target contours are blurred and contrast is low. This paper uses a cooled mid-wave infrared (MWIR) system to provide high sensitivity and fast response imaging and proposes the MWIRGas-YOLO network for detecting gas leaks in mid-wave infrared imaging. This network effectively detects low-contrast gas leakage and segments the gas plume within the scene. In MWIRGas-YOLO, it utilizes the global attention mechanism (GAM) to fully focus on gas plume targets during feature fusion, adds a small target detection layer to enhance information on small-sized targets, and employs transfer learning of similar features from visible light smoke to provide the model with prior knowledge of infrared gas features. Using a cooled mid-wave infrared imager to collect gas leak images, the experimental results show that the proposed algorithm significantly improves the performance over the original model. The segment mean average precision reached 96.1% (mAP50) and 47.6% (mAP50:95), respectively, outperforming the other mainstream algorithms. This can provide an effective reference for research on infrared imaging for gas leak detection. [ABSTRACT FROM AUTHOR]

As demand for energy increases, the number of "three-high" (high-pressure, high-yield, high-sulfur) gas Wells has doubled, the severe stress fluctuation of pipe string caused by downhole high-pressure environment leads to the failure of rope fracture is particularly prominent. It is easy to cause pipeline blockage and large amount of high-pressure toxic gas leakage caused by tool string accidental falling. Therefore, based on the theory of Love and elastoplastic contact theory, and considering the influence of Poisson's ratio effect, this article establishes a calculation model for tensile fatigue of interaction between rope strands to study the influence of key structural parameters and breaking wire position on stress distribution and fatigue life of rope. The results show that: the core strand of the rope is the main force position, the overall stress shows the trend of decreasing with the increase of winding radius; when the rope is stretched, the axial deformation near the core of the rope is smaller than the deformation far from the core; under the same number of broken wires, the fatigue life of different ropes is related to the location of broken wires, the farther the location of broken wires from the core of the rope, the higher the fatigue life. The research work in this article provides theoretical guidance for the safe operation of ropes in "three-high" gas wells. [ABSTRACT FROM AUTHOR]

Liquid hydrogen storage is an important way of hydrogen storage and transportation, which greatly improves the storage and transportation efficiency due to the high energy density but at the same time brings new safety hazards. In this study, the liquid hydrogen leakage in the storage area of a hydrogen production station is numerically simulated. The effects of ambient wind direction, wind speed, leakage mass flow rate, and the mass fraction of gas phase at the leakage port on the diffusion behavior of the liquid hydrogen leakage were investigated. The results show that the ambient wind direction directly determines the direction of liquid hydrogen leakage diffusion. The wind speed significantly affects the diffusion distance. When the wind speed is 6 m/s, the diffusion distance of the flammable hydrogen cloud reaches 40.08 m, which is 2.63 times that under windless conditions. The liquid hydrogen leakage mass flow rate and the mass fraction of the gas phase have a greater effect on the volume of the flammable hydrogen cloud. As the leakage mass flow rate increased from 5.15 kg/s to 10 kg/s, the flammable hydrogen cloud volume increased from 5734.31 m3 to 10,305.5 m3. The installation of a barrier wall in front of the leakage port can limit the horizontal diffusion of the flammable hydrogen cloud, elevate the diffusion height, and effectively reduce the volume of the flammable hydrogen cloud. This study can provide theoretical support for the construction and operation of hydrogen production stations. [ABSTRACT FROM AUTHOR]

. Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios. [ABSTRACT FROM AUTHOR]

The increasing discovery of underwater methane leakage underscores the importance of monitoring methane emissions for environmental protection. Underwater remote sensing of methane leakage is critical and meaningful to protect the environment. The construction of sensor arrays is recognized as the most effective technique to increase the accuracy and sensitivity of underwater remote sensing of methane leakage. With the aim of improving the reliability of underwater methane remote-sensing sensor arrays, in this work, a deep learning method, specifically an explainable sparse spatio-temporal transformer, is proposed for detecting the failures of the underwater methane remote-sensing sensor arrays. The data input into the explainable sparse block could decrease the time complexity and the computational complexity (O (n)). Spatio-temporal features are extracted on various time scales by a spatio-temporal block automatically. In order to implement the data-driven early warning system, the data-driven warning return mechanism contains a warning threshold that is associated with physically disturbing information. Results show that the explainable sparse spatio-temporal transformer improves the performance of the underwater methane remote-sensing sensor array. A balanced F score (F1 score) of the model is put forward, and the anomaly accuracy is 0.92, which is superior to other reconstructed models such as convolutional_autoencoder (CAE) (0.81) and long-short term memory_autoencoder (LSTM-AE) (0.66). [ABSTRACT FROM AUTHOR]

A polymer valve seat is an essential component of ball valves. However, a gas pipeline valve leakage during the gas or oil transportation process causes major issues. Therefore, the fine finishing of the polymer valve seat is important. This work describes the effect of the magnetorheological finishing (MRF) process on the surface characteristics of the polymer valve seat. To achieve a finely finished polymer valve seat surface, the best process parametric combination of current, workpiece rotational, and tool rotational speed are obtained using the response surface methodology. The optimum process parameters are further used to enhance surface characteristics (including surface roughness, microhardness, and surface morphology) through fine finishing. Using the optimum process conditions, the surface roughness was reduced to 110nm from 570nm in 140min of finishing on a 4878mm2 surface area of the polymer valve seat. The circularity of the valve seat surface’s dimensional correctness is investigated further. The current MRF process can fine-finish the polymer valve seat surface consistently, resulting in improved surface characteristics and functional performance. [ABSTRACT FROM AUTHOR]

When leakage occurs in the titanium tubes of a nuclear power plant condenser, the ability to quickly and accurately locate the position of the leaking tubes holds significant importance for the safe and stable operation of the condenser. SF6 and He are commonly used tracer gases for pinpointing the location of leaking titanium tubes in large condensers. In order to investigate the diffusion behavior of these two different tracer gases in the condenser of a nuclear power unit, a three-dimensional computational fluid dynamics (CFD) model of the condenser was established based on the porous media. Concentration distribution characteristics and diffusion rate differences of SF6 and He inside the condenser are analyzed through transient analyses. The results indicate that the amount of tracer gas leakage significantly influences the time it takes for the tracer gas to diffuse to the suction port, and the location of tracer gas leakage has a significant impact on the concentration distribution pattern at the suction port. He exhibits a faster diffusion rate compared to SF6. When a leak occurs at location 1 of the B4 module with a leak rate of 0.007 kg/s, the mass concentration of He peaks at the suction port of the A3 module after ∼2.2 s, whereas the mass concentration of SF6 reaches its maximum at the suction port of the A3 module after around 9.5 s. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

NH3 is a dangerous gas, posing significant threats to human health and the delicate equilibrium of the environment. It emerges as a primary contributor to the noxious gas emissions prevalent in the selective catalytic reduction (SCR) processes employed by marine diesel engines. Its deleterious impact extends beyond the immediate surroundings, permeating the air with potential health hazards and exacerbating environmental degradation. The repercussions of NH3 emissions are far-reaching, impacting respiratory health, ecosystem vitality, and overall air quality. In this paper, BaTiO3/Ti3C2Tx composites were synthesized by anchoring BaTiO3 nanospheres on multilayer Ti3C2Tx nanosheets. The shortcomings of low sensitivity and poor recovery of the pristine Ti3C2Tx MXene gas sensor were improved. The BaTiO3/MXene composite sensor exhibits an improved response (10.43) to 20 ppm NH3 at room temperature, which is about 3.5 times higher than that of the pure MXene sensor. Furthermore, the BaTiO3/MXene composite sensor greatly improves the recovery time and exhibits outstanding repeatability, stability, and high selectivity to NH3. The exceptional characteristics of these composites underscore the potential for advancing novel sensitive materials in the form of BaTiO3/MXene composites, specifically tailored for high-performance NH3 sensors. [ABSTRACT FROM AUTHOR]

Magnetic coupling is an approach employed to prevent gas leakage by transforming the dynamic seal into a non-contact static seal for the recovery of natural gas pressure energy. The impact of thermal demagnetization necessitates the consideration of the heat dissipation characteristics resulting from eddy current losses in the rotating magnetic field. We performed a numerical study of thermal-magnetic coupling in a magnetic transmission validated by experimental results. The Maxwell software was utilized to simulate the distribution characteristics of induced current, while the Fluent software was employed to analyze the dissipation of heat caused by eddy currents. The obtained simulation results reveal a proportional increase in induced current and eddy current losses with the rotation speed. Also, the eddy current losses increase together with the thickness of the isolation cover, since more volume of the conducting media is affected by the eddy currents. Furthermore, reducing the electrical conductivities of the isolation cover and enhancing the internal flow rates can effectively decrease the temperature of the magnetic coupling and mitigate thermal demagnetization. These research findings offer valuable insights for the design and optimization of non-contact transmission methods, ultimately enhancing the safety of natural gas top-pressure energy recovery equipment. [ABSTRACT FROM AUTHOR]

The leakage from submarine gas pipelines presents a significant threat to marine ecological environments. Bubble plumes can be formed when gas escapes, which can give rise to the distinctive fountain phenomenon by interacting with the water surface. It is imperative to understand the factors that influence the physical dimensions and temporal characteristics of the resulting fountain to elucidate the formation mechanism of this phenomenon. Gas leakage experiments under various conditions are conducted to investigate the dynamic characteristics of the fountain in the study. The findings reveal the following: The initial kinetic energy of the escaping gas varies significantly under different leakage scenarios, resulting in the positive correlation between the maximum fountain height and width, as well as the leakage pressure and diameter of the leakage hole. Then, the analysis of dimensional indicates that the dynamic equilibrium of the bubbles is intricately tied to both the Froude number (Fr) and Reynolds number (Re). Notably, the effect of leakage pressure on Fr and Re follows similar patterns. However, the trends of Fr and Re values across varying leak hole diameters are opposite. At last, the frequency analysis of the time series data related to the fountain height demonstrates that higher leakage pressure and larger leak hole diameters result in an increase in both the main frequency and bandwidth. The research results offer valuable insights for evaluating the impact of gas leakage intensity on the marine environment by analyzing the size parameters of fountain. [ABSTRACT FROM AUTHOR]

To investigate the leakage characteristics of pure hydrogen and hydrogen-blended natural gas in medium- and low-pressure buried pipelines, this study establishes a three-dimensional leakage model based on Computational Fluid Dynamics (CFD). The leakage characteristics in terms of pressure, velocity, and concentration distribution are obtained, and the effects of operational parameters, ground hardening degree, and leakage parameters on hydrogen diffusion characteristics are analyzed. The results show that the first dangerous time (FDT) for hydrogen leakage is substantially shorter than for natural gas, emphasizing the need for timely leak detection and response. Increasing the hydrogen blending ratio accelerates the diffusion process and decreases the FDT, posing greater risks for pipeline safety. The influence of soil hardening on gas diffusion is also examined, revealing that harder soils can restrict gas dispersion, thereby increasing localized concentrations. Additionally, the relationship between gas leakage time and distance is determined, aiding in the optimal placement of gas sensors and prediction of leakage timing. To ensure the safe operation of hydrogen-blended natural gas pipelines, practical recommendations include optimizing pipeline operating conditions, improving leak detection systems, increasing pipeline burial depth, and selecting materials with higher resistance to hydrogen embrittlement. These measures can mitigate risks associated with hydrogen leakage and enhance the overall safety of the pipeline infrastructure. [ABSTRACT FROM AUTHOR]

Hydrogen will play a key part in the decarbonization of the global energy supply as the wealth of hydrogen continues to rise. However, applying hydrogen knowledge necessitates the installation of strong safety protocols, including the use of reliable hydrogen gas-detecting devices. Gas detection and monitoring at a large scale in industries need programmable electronic systems that can provide fast response and switching. The objective of the research work is to analyze the hydrogen gas detection system using a field programmable gate array (FPGA). The system is integrated with the gas sensor module, temperature control module, buzzer for alarm, liquid crystal display (LCD) and light-emitting diode (LED) for information display, and global system for mobile communication (GSM) for wireless message communication to the mobile. The temperatures module works on the sampling of the heater resistance using a voltage divider and inbuilt analog-to-digital converter (ADC), which depend on the output voltage of the digital-to-analog converter (DAC). The comparator module compares the gas concentration with the pre-decided threshold limit and acknowledges the buzzer accordingly. The system behavior for monitoring and gas concentration detection is simulated in Xilinx Vivado design suite 2020.1. The functionality of the chip is also verified in the Modelsim 10.0 waveform simulator. for the testbench tata sand samples used. LCD (16x2) displays the messages whether hydrogen gas is detected or not and the same message is delivered on mobile. The novelty of the work is that the system is applicable for faster switching in industrial applications and the designed system supported 550 MHz frequency in our simulation and synthesis. [Display omitted] • Hydrogen gas detection and monitoring using FPGA for programmable electronic device switching. • Integration of gas sensor, temperature sensor, alarm, LCD, and GSM wireless communication. • Supports 550 MHz frequency for fast switching operations. • Chip assessment for hydrogen leakage identification, and validation on FPGA hardware. [ABSTRACT FROM AUTHOR]

Shelf seas are experiencing a rise in shallow gas leaks, primarily methane, raising concerns due to their environmental impact. However, the effect of the leaks on early diagenesis remains poorly understood. This study analyzes sediment lithology, organic geochemistry and porewater geochemistry of two short cores collected nearby the pockmarks in the muddy inner shelf of the East China Sea. Our findings clearly demonstrate the impact of methane leakage on early diagenesis, evidenced by the shallower position of the SMTZ (sulfate-methane transition zone), higher concentrations of uranium (U), vanadium (V), and manganese (Mn) in the porewater near and above the SMTZ, and downcore decrease in Mg2+, Ca2+, and Sr2+ concentrations versus increase in Mg2+/Ca2+ and Sr2+/Ca2+ ratios. Their profile variations and the difference between two cores are determined by the intensity of methane leakage. The estimated methane diffusive flux of 619 mmol m-2 yr-1 at YEC7-2 is roughly 8.5 times that at YEC7-1 (73 mmol m-2 yr-1), consistent with a shorter distance of YEC7-2 to the pockmark with active methane leakage. A schematic model is summarized to demonstrate the response of early diagenesis processes to the increasing methane leakages in response to changing sedimentation regimes from accretion to severe erosion. This study undoubtedly improves our understanding mutual promotion effect between seafloor erosion and gas leakage, and their impact on early diagenesis processes and resultant porewater geochemical changes and authigenic mineral records. [ABSTRACT FROM AUTHOR]

This study investigates the development and application of advanced predictive modeling techniques to address the critical environmental challenge of fugitive emission mitigation in industrial valve seal stacks, specifically in the context of the oil and gas sector. Emphasizing the reduction of greenhouse gas emissions, the research systematically evaluates the effectiveness of multiple seal-stack configurations in minimizing emissions. The experimental framework utilizes argon gas as a surrogate for methane to simulate real-world scenarios. The research employs a comprehensive suite of predictive models, including advanced statistical and machine learning algorithms such as linear regression, ridge regression, Lasso (least absolute shrinkage and selection operator), MARS (multivariate adaptive regression splines), and elastic net. These models are rigorously tested to ascertain their predictive accuracy in estimating the emission levels of two different seal-stack arrangements. Each seal stack contains five individual seals of PTFE and AFLAS in different sequences. The MARS model, identified for its superior performance, is then applied to predict the efficacy of various seal-stack configurations against the stringent ISO 15848–1 standards for allowable emission limits. The results of this comparative analysis offer critical insights into the optimal combination of seal stacks, contributing significantly to the advancement of environmental sustainability practices in industrial applications. This research not only provides a methodological framework for predictive analysis in this domain but also underscores the importance of integrating environmental considerations into industrial design and operation. [ABSTRACT FROM AUTHOR]

In terms of its significance, the oil & gas industry is ranked among the top global industries. Like any other industry, it also faces various problems, such as leakage of oil and gas pipelines. The detection of such leakage in the pipelines is essential for an industry or plant to operate properly and maintain environmental safety as well as to minimize its supply-chain losses. The undergoing study systematically reviews the literature comprising of more than a decade (2010-2021) span to summarize the systems, methods and techniques used in oil & gas pipeline leakage detection. Likewise, this paper investigates effective and low-cost leakage detection systems for oil & gas pipelines with their pros and cons. The existing methods are classified into three categories based on their technical characteristics, named as hardware-based (where some hardware is deployed for monitoring the leakage), software-based (where some software is deployed for monitoring the leakage), and intelligent based (where some intelligent predictive algorithm is deployed for the leakage detection) techniques. Each technique was reviewed according to the datasets used, preprocessing techniques (mainly used in the intelligent based techniques that are based on the imagery such as image preprocessing largely like image enhancement, image denoising and filtering), investigated classifiers' efficiencies, results, and limitations. A comparative analysis was conducted to help determine which technology is best for a given operational environment, that is software, hardware, intelligent or hybrid. Further, the paper highlights the gaps in the research and unresolved concerns regarding the development of dependable pipeline leak detection systems and suggests possible research directions to mitigate it. [ABSTRACT FROM AUTHOR]

To explore the dynamics of flow and heat transfer behaviors associated with bubbles during solution absorption in a vacuum environment, we present the design of an experimental setup for measuring the absorption and transport properties of bubbles in a pressurized vertical tube. The structure and operational principle of the setup are detailed. The reliability and accuracy of the system are validated through a series of experiments, including vacuum level maintenance, bubble flow verification, and energy checks. The findings reveal that the supercharging technology effectively facilitates bubble absorption under negative pressure. Over a 12 h period, the system vacuum level elevates by only 2.33%, indicating a minimal gas leakage rate of 2.4 mL/h and affirming the device's exceptional reliability. The observed bubble formation, rise, collision, coalescence, and rupture behaviors in the experiment are consistent with previous studies on bubble flow. The maximum relative deviations of temperature and concentration at the solution and cooling water outlets are 0.08%, 0.02%, and 0.01%, respectively, validating the device's excellent accuracy. Additionally, the energy check experiments, performed with varying solution inlet temperature and flow rate, reveal the maximum errors of 10.4 J and 12.5 J, respectively, demonstrating the device's satisfactory accuracy. In summary, this work lays a robust experimental foundation for subsequent investigations into the transport properties and transfer mechanisms of bubble absorption in a vacuum environment. [ABSTRACT FROM AUTHOR]

With the rapid increase in urban gas consumption, the frequency of maintenance and repair of gas pipelines has escalated, leading to a rise in safety accidents during these processes. The traditional manual supervision model presents challenges such as inaccurate monitoring results, incomplete risk factor analysis, and a lack of quantitative risk assessment. This research focuses on developing a dynamic risk assessment technology for gas emergency repair operations by integrating the monitoring outcomes of artificial olfactory for gas leakage information and video object recognition for visual safety factor monitoring data. To quantitatively evaluate the risk of the operation process, a three-dimensional risk assessment model combining gas leakage with riskcorrelated sensitivity was established as well as a separate three-dimensional risk assessment model integrating visual risk factors with predictable risk disposition. Furthermore, a visual risk quantification expression mode based on the risk matrix-radar map method was introduced. Additionally, a risk quantification model based on the fusion of visual and olfactory results was formulated. The verification results of simulation scenarios based on field data indicate that the visual-olfactory fusion risk assessment method can more accurately reflect the dynamic risk level of the operation process compared to simple visual safety factor monitoring. The outcomes of this research can contribute to the identification of safety status and early warning of risks related to personnel, equipment, and environmental factors in emergency repair operations. Moreover, these results can be extended to other operational scenarios, such as oil and gas production stations and long-distance pipeline operations. [ABSTRACT FROM AUTHOR]

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The fault cavern type reservoir in Block 1 of Shunbei area is located in the ultra-deep layer and is classified as a volatile oil reservoir. The reservoir is characterized by very deep burial with an oil/gas ratio between 250 and 500 m3/tone and a saturation pressure range of 28-36 MPa. As of March 2021, the formation pressure has dropped to 30.5 MPa and the pressure retention rate is 35.8%. In order to effectively develop this reservoir, it was decided to use a water injection development method with a planned transition to natural gas injection in March 2022 to further enhance recovery and maintain formation pressure. Through evaluation and research, the pressure distribution, production capacity change and reservoir dynamic response during the water injection development stage were analyzed with respect to the characteristics of fracture-controlled fracture-cave type reservoir in Shunbei-1 block. On this basis, the necessity, feasibility, and expected effects of conversion to natural gas injection are explored, including improvement of displacement efficiency, reduction of fluid output, and enhancement of formation energy replenishment. In addition, this study also carries out in-depth research and prediction on the optimization of natural gas injection parameters, the selection of injection methods and possible problems, aiming to provide scientific basis and technical support for the efficient and sustainable development of this block. In order to smoothly promote the implementation of natural gas injection development, a special reservoir description technique applicable to fault cavern type reservoirs was adopted. Based on this foundation, an in-depth study was conducted on the mechanism of natural gas displacement, so as to optimize the layout of the well network and the setting of injection and extraction parameters. In addition, a set of evaluation system for natural gas injection effect in fault cavern type reservoirs was constructed. By adopting the natural gas injection development method and combining the corresponding supporting technology development and optimization measures, the reservoir development efficiency and oil (gas) production rate have been effectively improved, and the risk of gas flaring has been reduced at the same time. These research results are of great significance for ensuring the comprehensive development and effective utilization of oil and gas resources, and provide strong technical support for future development work. [ABSTRACT FROM AUTHOR]

Industry experts anticipate that Internet of Things (IoT)-based Immersive Visual Technologies (IVT) will revolutionize air quality applications. However, the enormous variety of IoT and IVT poses a significant challenge, impeding widespread adoption in both domains. To fully harness the potential of IoT and IVT in air quality applications, we must confront the complexity arising from their diverse integration. Overcoming this challenge requires an innovative architectural framework that facilitates seamless coexistence and integration. In this paper, we present a New Air Quality Architecture (NAQA) framework designed to model IoT-based immersive technologies for air quality applications. The framework comprises a cohesive collection of architectural perspectives, serving as a guideline to describe specific IoT-based immersive systems with precision. To validate our proposed architecture, we conduct a comprehensive evaluation, including critical case studies like gas leakage and Coronavirus-based air quality monitoring. Comparing NAQA's performance to expert assessments reveals higher precision in detecting errors and recommending solutions. Our framework's versatility and efficacy are demonstrated across various use cases, fostering collaboration and knowledge reuse for accelerated progress in the field. [ABSTRACT FROM AUTHOR]

Exploring the transport characteristics of leakage gas in the condenser can facilitate quicker identification of leak points when using Helium tracer gas method for detection. We construct a 3-D physical model of the condenser to simulate the Helium gas leakage process within the tube bundle. On the steam side, we adopt RNG k-e, porous media, steam condensation, and convective diffusion models to describe steam and leakage gas-flow. On the waterside, we use the tube bundle thermal resistance model to describe the steam-water heat transfer. The research concludes with three key points. When the centripetal pressure gradient is insufficient, there will be leakage gas enrichment, resulting in flowing out in the form of diffusion. When there is no centripetal pressure gradient in the tube bundle region, it will extract only a small amount of upstream leakage gas along with steam through the flow. When reaching a stable level for leakage gas, the leakage intensity is proportional to the outlets' flow rate but is independent of the transport form. The deviation of the mass-flow rate decreases with the mesh quantity increasing, which is less than 2% when the mesh quantity is over 638228. The deviation between simulated and actual values of the two parameters is less than 5%, which reveals the good agreement between numerical calculation and actual work conditions. These conclusions can assist employees and researchers in evaluating data on leak points and enhancing detection techniques. [ABSTRACT FROM AUTHOR]

Helium (He) tracer method is one of the common methods used to detect tube bundle leakage in the condenser. To improve the detection accuracy, sulfur hexafluoride (SF6) is considered a tracer gas instead of He. This paper combines the N-S equation, porous medium model, flow diffusion model and steam heat transfer model to develop the numerical model of tube bundle leakage in the condenser. The simulation results show that the transport of leaked gases (He and SF6) consists of flow and diffusion patterns. The existence of the diffusion process is confirmed further through theoretical analysis. The two gases have the same transport behavior in the pure flow process. When it involves the diffusion process, the flow rate of He is 6.67 times that of SF6. In other words, the time required for He to reach the same concentration difference is 1/6.67 times that of SF6. In addition, the influence of leakage intensity and gas species on the transport is analyzed. The study results provide a theoretical basis for SF6 to replace He as a tracer gas to detect tube bundle leakage. [ABSTRACT FROM AUTHOR]

Appropriate and timely first aid reduces morbidity in burns. This study aims to assess the knowledge of first aid in burns among healthcare workers (HCW) and nonhealthcare workers. (NHCW). A survey, distributed in both paper and Google survey formats, presented five sample cases with multiple-choice answers. Participants recorded the most suitable first aid management for each scenario. Correct answers were provided on completion. Out of the total 753 participants, only 89 (11.8%) got all five answers correct. 16% HCW and 6% NHCW could answer all 5 questions correctly (with a true HCW:NHCW ratio of 2.67:1). Providing care for individuals with burns substantially raised the probability of giving accurate responses (p = 0.0001). While attending the general First Aid Course did not affect the responses (p = 0.08), participation in the Burns First Aid Course demonstrated slightly improved results (p = 0.052). The scenario involving liquid petroleum gas leakage saw a high proportion of correct responses, likely influenced by media coverage. We find a clear need for adequate training in burns first aid due to low awareness among healthcare workers (HCW) and non-healthcare workers (NHCW). Factors such as participation in burn first aid courses and gaining firsthand experience in treating burns were found to be linked to improved knowledge of burns prevention and first aid. Utilizing various media channels could be a valuable strategy to reach a broader audience, especially in remote and inaccessible areas. • There is limited awareness of burns first aid and prevention among both healthcare and non-healthcare workers • Experience in treatment of burn injuries improves knowledge of burns first aid better than first aid training. • Efficient use of multimedia and technology enhances burns prevention and first aid knowledge, reaching remote areas. [ABSTRACT FROM AUTHOR]

Sealing of soft tissues prevents leakage of gas and liquid, closes wounds, and promotes healing and is, therefore, of great significance in the clinical and medical fields. Although various formulations have been developed for reliable sealing of soft tissue, tradeoffs between adhesive properties, degradation profile, and tissue toxicity limit their clinical use. Hydrogel‐based adhesives, for example, are highly biocompatible but adhere very weakly to the tissue and degrade quickly, while oxidized cellulose patches are poorly absorbed and may cause healing complications postoperatively. Here, we present a novel strategy for tissue sealing based on bioadhesive microneedle patches that can spontaneously adhere to tissue surface through electrostatic interactions and swell within it. A series of microneedle patches made of pullulan, chitosan, Carbopol, poly (lactic‐co‐glycolic acid), and a Carbopol/chitosan combination were fabricated and characterized for their use in tissue sealing. The effect of microneedle composition on the fabrication process, physical and mechanical properties, in vitro cytotoxicity, and in vivo biocompatibility were examined. The needle structure enables microneedles to strongly fix onto various tissues via physical interlocking, while their adhesive properties improve staying time and sealing capabilities. The microneedle patch comprising Carbopol needles and chitosan as a second pedestal layer presented the best results in terms of sealing and adhesion, a consequence of the needle's swelling and adhesion features combined with the supportive chitosan base layer. Finally, single Carbopol/chitosan patches stopped intense liver bleeding in a rat model significantly quicker and with less blood loss compared with commercial oxidized cellulose patches. These microneedles can be considered a promising cost‐effective platform for adhering and sealing tissues as they can be applied quickly and painlessly, and require less trained medical staff and equipment. [ABSTRACT FROM AUTHOR]