Objective The complex working environment of oil and gas pipelines, characterized by widely distributed risk sources, often hinders employees' ability to identify risks, which is a key cause of workplace safety incidents. Supplementary safety signs act as important information carriers that complement the content of main safety signs, effectively nudging employees toward better risk identification behavior. However, their effectiveness is significantly influenced by core attributes such as color, shape, and format, but systematic research on this issue is currently lacking in the oil and gas pipeline industry. This study aims to identify how different attributes of supplementary safety signs influence employees' visual attention and nudge risk identification behavior, providing a theoretical framework for optimizing safety sign design and enhancing hazard recognition efficiency in oil and gas pipeline enterprises. Methods Based on a major oil and gas pipeline network enterprise in China, this study selected three representative operational scenarios: anti-corrosion, pressure testing, and maintenance and repair of pipelines and storage tanks. Using 38 risk identification points as sign content, 18 groups of supplementary safety signs were designed following national standards. An orthogonal design was used to combine different attributes of color (red, blue, black) u00D7 shape (triangle, circle, rectangle) u00D7 format (text, icon). The study recruited 180 participants with at least 2 years of work experience in oil and gas pipelines, who were randomly divided into 18 groups to participate in the orthogonal experiments. A Tobii Pro Glasses 2 wearable eye tracker was used to collect four eye-tracking metrics: time to first fixation, first fixation duration, total fixation duration, and fixation count. Furthermore, two behavioral metricsu2014response time and accuracy of risk identificationu2014were recorded. Multivariate analysis of variance (MANOVA), Least Significant Difference post hoc tests, and interaction plots were employed to systematically evaluate the main and interaction effects of color, shape, format, and their combinations on visual attention characteristics and behavioral nudge effects. Results Color and shape were the primary drivers of visual attention orientation and behavioral accuracy. Specifically, red and triangular designs significantly shortened the time to first fixation (by 411 ms and 213 ms, respectively) and improved behavioral accuracy by 5.79% and 2.20%, respectively. Notably, color (Partial u03B72 = 83.6%) and shape (Partial u03B72 = 55.2%) exhibited the strongest main effects on time to first fixation. The format was the main driver of sustained visual attention. Specifically, text signs drew more engagement than icon signs, increasing first fixation by 363 ms, total fixation by 350 ms, and fixation count by 1.93. The findings also demonstrated that color and format were critical factors influencing risk identification response time. Red signs outperformed blue and black variations, shortening the response times by 564 ms and 540 ms, respectively. Similarly, icon signs shortened the response time by 878 ms relative to text signs. Color (Partial u03B72 = 90.3%) and format (Partial u03B72 = 96.3%) exhibited the strongest main effects on response time. Significant synergistic effects were observed among attributes: the redu2013triangle combination yielded the highest behavioral accuracy, outperforming blue and black combinations by 7.93% and 8.92%, respectively. Furthermore, the redu2013icon combination resulted in the fastest response time, reducing it by approximately 792 ms compared with blueu2013icon and blacku2013icon combinations. Finally, shape and format interacted significantly regarding total fixation duration; triangles and circles attracted longer fixation durations in text format but shorter durations when paired with icons. Conclusions The color, shape, and format differentially influence employees' visual attention orientation, sustained attention, and risk identification nudge effects among employees in oil and gas pipeline enterprises. Red and triangular attributes rapidly capture attention and enhance behavioral accuracy, text facilitates sustained attention, and icons accelerate behavioral responses significantly. Synergistic designs further amplify these nudge effects. This study recommends that the oil and gas pipeline industry treat supplementary safety signs as independent management units using optimal coloru2013shapeu2013format combinations. The findings offer theoretical and empirical support for optimizing safety signage in high-risk, large-scale industrial scenarios. Future research should validate the generalizability of these findings in real-world operational settings.

Natural disasters like severe snowstorms and ice storms induce abrupt spikes in electrical demand within process industries, precipitating uncontrolled thermal runaway in power cables and subsequent system failures or electrical fires. In high-risk facilities, these fires frequently trigger catastrophic cascading effects, including explosions, toxic chemical releases, and complete operational shutdowns, ultimately resulting in Natech disasters. Conventional monitoring systems only activate alarms following fault occurrence, lacking the capability to detect pre-fault abnormal circuit heating and address the inherent trade-off between early warning reliability and operational cost-effectiveness. This paper proposes a novel three-stage frequency-conversion prediction and prevention system specifically tailored for resistive circuit electrical fires (RCEF), with three core innovations: (1) a dynamic sampling mechanism that adaptively matches sampling frequencies to distinct fire evolution stages; (2) a physics-guided adjusted Long Short-Term Memory (aLSTM) model that fuses thermal balance equations to correct systematic prediction biases; and (3) an edge-cloud collaborative three-layer distributed platform enabling real-time risk response and full-life-cycle closed-loop management. A real case study from a hospital power consumption verifies that the standard LSTM achieves an average RMSE of 3.97°C for long-term trend prediction in the latent abnormal stage. The aLSTM further reduces prediction error to 2.09–2.52°C in the accelerated heating stage, eliminating the prediction lag of pure data-driven models. The system identifies electrical fire risks in advance with major reduction in daily energy consumption, providing a cost-effective proactive loss prevention solution readily extendable to high-risk process industrial facilities such as petrochemical plants and pharmaceutical factories, to enhance Natech risk management.

Building electrical circuit temperature perception and prediction are crucial for preventing the building electrical fire risks. Nevertheless, existing engineering prediction methods often lack sufficient accuracy and timeliness by virtue of the fixed threshold fire warning or failure to predict the temperature step-change. In this study, a novel mathematics method was proposed to dynamically predict the building electrical circuit temperature. First, the Long Short-Term Memory (LSTM) neural network was selected as the basic predictive framework. Two types of three-phase electrical overload experiments (short term single-overloading and long term periodic-overloading) were implemented to establish the training dataset (temperature, voltage, current, and residual current) which were distinguished by sampling conditions and frequencies Second, a cross model was built to validate the prediction accuracy with conditions transformation. Third, the temperature step-change was calibrated via the linear relationship with accumulated temperature variation and the usage of complex structure LSTM. Subsequently, the second-order temperature residual as well as its normality test were calculated. Finally, the temperature probability distribution was expressed via the first-order Taylor expansion of the temperature residual. The results indicated that the basic model is able to predict both the high and low frequency temperature tendency. The temperature probability distribution interval accurately covers the actual temperature variation. The comparison with temperature probability quantiles and actual value enables the fire risk of building electrical circuit. This study illuminated the step-change of electrical temperature and dynamical prediction in building electrical fire safety.

With the development of intelligence and green concepts, triboelectric nanogenerators (TENGs) have gained tremendous attention in wearable devices due to their high sustainability, light weight, and excellent flexibility. However, they may be damaged in high-temperature fires. It is vital to develop a kind of triboelectric material with flame retardance, biodegradability, and high triboelectric properties for the high-temperature alarm and motion monitoring. Herein, a degradable triboelectric material (P/C3P20) with core (polylactic acid/carboxylated multiwalled carbon nanotubes, PLA/C-MWCNT) and shell (polylactic acid/calcium phytate, PLA/PA-Ca) was constructed by coaxial electrospinning. P/C3P20 exhibited burning without molten droplets and self- extinguishing within 6 s, which was due to gas and condensed phases synergistic effects of PO center dot radicals and the dense char layer. Meanwhile, the P/C3P20 membrane degraded up to 70 % on the 4th day in the proteinase K solution. The open-circuit voltage of the P/C3P20-TENG (4 cm2, 76.21 V) was nearly 6.1 times that of the casting PLA-TENG. It maintained 71.29 % at 160 degrees C and 13.40 % at a 520 degrees C flame with burning 20 s, respectively, and successfully outputted stable signals to self-powered sensing. This work provides a novel idea to prepare triboelectric materials and devices with flame retardance, high-temperature resistance, and degradation, which will benefit for early-warning fire and guaranteeing firefights' safety.

Fire is one of the most serious threats faced by immovable cultural heritage (ICH). In recent years, multiple ICH fires have occurred worldwide, exposing the shortage in fire protection work. It is largely due to insufficient attention on city-scale fire protection governance (FPG). This study aims to provide the FPG framework and strategies through a case analysis of the city with an extraordinary number of ICH sites. Beijing is selected as the case city, which has never experienced an ICH fire accident since 2010. The fire vulnerability drivers (FVDs), facing challenges, and contents of FPG plans are analyzed to clarify the lessons learned from past accidents and current countermeasures. It is found that there are diverse factors that can increase the fire occurrence probability and firefighting difficulty, including the inherent attributes, diversified uses, and restricted locations of ICH sites. There are also multiple challenges in FPG activities. However, Beijing has already formulated a series of response plans which highlight the significant role of institutionalized, source-oriented, technological, and socialized governance. These plans can be summarized as "Beijing mode". It may serve as an example of how city-scale FPG can be implemented in practice. This study also reminds people that FPG is a global task always on the road. It needs the efforts and experience from all countries to improve the current situation.

ObjectiveRisk quantification is crucial in risk assessment of accidents or disasters. This study aims to investigate the risk quantification method utilized in over-temperature faults in electrical circuits. The existing technologies of the abovementioned method are summarized in electrical and fire signals of disaster early warning. Thus, a new method based on the fire big data is proposed.MethodsDifferent frequency electrical parameters are collected by the detector arranged at the front end and transmitted in real time to the fire big data to mine the influencing factors and changing patterns of electrical circuit temperature based on the deep-learning method. Subsequently, the probability distribution of temperature is determined statically, and risk is described by comparison of prediction temperature in different cumulative probabilities with actual temperature. To predict the electrical circuit temperature, a recurrent neural network (RNN) is utilized to model temperature prediction. The input parameters are voltage, current, temperature, and residual current. Among the parameters, there are two data sources for the model: one is real electrical fire data, 6 min-1, used to learn the periodic law of temperature increase in electrical circuits of RNN for low-frequency data (LF-RNN), and the other is experimental data based on simulated fault of the temperature increase in electrical circuits. This experiment is implemented in three-phase resistive electrical circuits. Exceeding rated current is utilized to produce temperature rising. Meanwhile, electrical parameters are collected to study the law of temperature oscillation of RNN for high-frequency data (HF-RNN). Among these electrical parameters, the sampling frequency of voltage, current, and residual current is 50 kHz, but 1 Hz for temperature exceptionally. The optimization method, hyperparameter traversal, aims to minimize the loss function and root mean square error; thus, temperature prediction in electrical circuits is preliminarily applied. To increase the accuracy of the prediction model and elucidate the relationship between fire risk and prediction result, a temperature probability prediction model is established based on its second-order residual normal distribution. The error and its reducing methods are analyzed, and the relationship between prediction error and temperature mutation is determined.ResultsThe results demonstrated that temperature mutation within three window lengths had a remarkable linear correction effect with temperature prediction error; moreover, the second-order residual approximately followed a normal distribution. The upper and lower limited of temperature prediction confidence intervals with different significant levels (α=0.02, 0.04, 0.06, …, 0.98) can be computed by interval estimation, which had a one-to-one correspondence with temperature prediction accumulate probability (1−α2) and α2. The results revealed that the cumulative distribution probability 1% prediction curve and 99% prediction curve appeared to have a fine coverage effect on the actual temperature. With the aim of measuring temperature prediction probability distribution with electrical fire risk, the concept of "early-warning quantile" similar to cumulative distribution probability, was proposed. The ability to predict temperature was established using different "early-warning quantile curves" and confirmed through 2 943 sets of real electrical fire scene data. The results demonstrated that early-warning quantiles in the range of 10%-30% could overlap the majority of the actual temperature data, and the higher the quantile of the curve was, the higher the frequency of overestimating the temperature was.ConclusionsTo summarize, when the temperature in electrical circuits suddenly increases, there is a substantial upward trend in the early-warning quantile of the actual temperature. Thus, the use of LF-RNN and HF-RNN can timely and accurately predict the temperature probability distribution to characterize fire risks in electrical circuits so that early dynamic perception of fire risk is realized.

The content and methods of traditional safety training can no longer meet the needs of the industry or accident-targeted safety training. To solve this problem, this study proposes an accidental case data accident causing model-driven safety training method (ACDACM method) based on an analysis of the content and methods of China's safety training. This method is theoretically driven by the accident causation model and data-driven by the accident case information. Through data analysis of historical accident cases, basic information and comprehensive reasons for accidents can be obtained. Using data mining algorithms, the causes and combinations of high-frequency and high-risk accidents can be mined, supported by accident data, and a path map of the accident causes can then be obtained. The developed safety training project is more targeted by comparing accident causes with industry standards and regulations. This study considers coal and gas outburst accidents as an example and uses 24Model to analyze 84 coal and gas outburst accidents in China. The targeted safety training plan had eight parts of training content, and supporting PPTs, handbooks, and videos were produced. The application shows that this method can provide targeted safety training designs for industries or accidents and has industry universality. Finally, the advantages and development of the ACDACM method are elaborated and the future of safety training is discussed. This study provides theoretical and methodological support for targeted safety-training programs.

The traditional storage method of fire accident cases is mainly in the form of text, and it is difficult to effectively conduct comprehensive analysis due to the limited ability to display key information and fire knowledge. In this paper, a structured storage form of building fire cases was proposed based on knowledge graph, which can comprehensively describe and visualize the fire causes, the dynamic fire development process and evacuation process. It enables readers to get information and knowledge from building fire cases intuitively, and supports the comprehensive analysis for building fire prevention strategies. The knowledge graphs are constructed for two common building types (residential and public buildings), and have the capacity to reflect the dynamic development law of fires from ignition to spread in different buildings. Meanwhile, as the occupants’ evacuation is the first concern when a fire occurs, the knowledge graphs also visualize the relationship among various conditions in the evacuation process. Different application scenarios are displayed in the paper, including case query, root-cause analysis and consequence forecasting, which shows the advantages and applicability of building fire knowledge graph.

In modern safety management, it is very important to study the influence of the whole safety system on unsafe acts in order to prevent accidents. However, theoretical research in this area is sparse. In order to obtain the influence law of various factors in the safety system on unsafe acts, this paper used system dynamics simulation to carry out theoretical research. First, based on a summary of the causes of the coal and gas outburst accidents, a dynamic simulation model for unsafe acts was established. Second, the system dynamics model is applied to investigate the influence of various safety system factors on unsafe acts. Third, the mechanism and the control measures of unsafe acts in the enterprise safety system are studied. This study’s main result and conclusions are as follows: (1) In the new coalmines, the influence of the safety culture, safety management system, and safety ability on the safety acts were similar. The order of influence on the safety acts in production coalmines is as follows: safety management system > safety ability > safety culture. The difference is most evident in months ten to eighteen. The higher the safety level and safety construction standard of the company, the greater the difference. (2) In the construction of the safety culture, the order of influence was as follows: safety measure elements > safety responsibility elements = safety discipline elements > safety concept elements. It shows the difference in influence from the 6th month and attains its maximum value from the 12th month to the 14th month. (3) In the construction of the safety management system, the degree of influence in new coalmines was as follows: safety policy > safety management organization structure > safety management procedures. Among them, especially in the first 18 months, the impact of the safety policy was most apparent. However, in the production mine, the degree of influence was as follows: safety management organization structure > safety management procedures > safety policy, but the difference is very small. (4) The degree of influence on the construct of safety ability was as follows: safety knowledge > safety psychology = safety habits > safety awareness, but the difference on the impact was small.

ObjectiveFire is a serious threat to public life and property safety. Insurance is an effective means to deal with fire risk, and accurately determining the premium rate of buildings according to the fire risk is a concern of the insurance industry. Currently, the premium rate is mainly based on the fire frequency and loss expectation from the insurance statistics, and adjustments are based on building risk assessment results. The adjustment scheme can be divided into two types. One is the rate floating model, which gives the floating range of the premium rate based on the risk level, but the floating proportion is fairly subjective. The other is the rate calculation model, which establishes the quantitative risk assessment method to calculate the specific premium rate. However, comprehensively reflecting the hazardous in the buildings as well as the uncertainty of losses with the current risk assessment method is difficult. Thus, the premium rate is relatively rough.MethodsA quantitative model for building fire insurance premium rates is constructed in this paper. First, the Bayesian network method is used to calculate the building fire probability considering the influences of various risk sources. The specific factors affecting ignition were comprehensively analyzed from the aspects of humans, things, and environments. Therefore, 14 factors were selected to construct the Bayesian network of building ignition, based on which the probability of building fire can be calculated rather quantitatively and objectively. Second, the Latin hypercube sampling (LHS) is used to stratify the burn rate in different fire stages from ignition, growth, and development to spread with certain distributions to reflect the staging and random characteristics of fire losses. Thus, the final loss distribution, including the expected value, standard deviation, probability density function, and cumulative probability density function, can be acquired accurately.ResultsTherefore, the quantitative and dynamic risk assessment of building fire is realized, and the rate calculation model is used to compute the rate based on the result. Fifteen households were selected to calculate their premium rates based on the quantitative assessment of building fire risk, including ignition probability and loss distribution, and the premium rates are compared with the rate in the insurance market.ConclusionsResults show that the proposed premium rate determination model can effectively reflect the differentiated level of fire risk and ensure the fairness of insurance. The premise of the building fire insurance premium rate model in this paper is that the insurance company covers all the fire risks of the building and disregards the case of deductible due to the retainment of fire risk by the insured. In addition, the foreign statistics were adopted, and the normal loss distribution at each stage after the ignition was assumed due to the lack of domestic data. Deductibles can be considered in further research to construct premium rate models, and accurate data can be acquired to obtain results consistent with the building fire risk level in China.

The traditional research of building fire probability analysis is from statistics or fire science. This paper combines the two methods and aims to improve the statistical method of building ignition probability determination according to the research conclusion of fire science. The specific factors that affect the ignition probability are divided into three aspects: humans, ignition sources and combustibles and environments. On this basis, the Bayesian network of building ignition probability is constructed, the nodes and conditional probability table in the Bayesian network are introduced in detail, according to which the ignition probability of building can be calculated quantitatively and objectively. Then some typical buildings are chosen as examples for the application of the method, the posterior probability value is calculated by obtaining the relevant building information and substituting them into the Bayesian network. The ignition probability is dynamic, and the comparison with the statistical data of building fire also proves its rationality.

Analyzing the causes of accidents, excavating accident paths, and applying accident prevention are important tasks in safety management. Focusing on coal and gas outburst accidents, this study examined the primary accident path and conducted applied research on the reasoning of the accident case. First, combined with the obtained accident causes, a coupling analysis of the causes of coal and gas outburst accidents was conducted. Second, using the method of data mining coupled with Apriori algorithm, the coupling relationship between each cause module of the coal and gas outburst accident was obtained, and consequently, a path map of the coal and gas outburst accident was drawn. Third, a Bayesian network model for the causes of coal and gas outburst accidents was established based on the accident path map and the probability of occurrence of each cause. Finally, considering the safety concept element (SC1) as an example, the Bayesian network model was used to conduct a sensitivity analysis of accident causes. Thereafter, considering the coal and gas outburst accident of the Sanjia Coal Mine in Guizhou Province as an example, probabilistic reasoning research on the cause of the accident was conducted. The application results showed that (1) under normal conditions, there are approximately 797,280 accident paths for coal and gas outbursts. Following data mining, 188 main accident paths were found. (2) Sensitivity analysis determined 19 factors that were sensitive to safety concept elements (SC1), of which the three most sensitive factors were (i) resource management system procedures (SM7), (ii) safety policy (SM1), and (iii) safety training system procedure (SM8). 13 paths exhibited a sensitivity ≥0.5%, of which 7 exhibited strong sensitivity. (3) The absolute accuracy rate of accident cause reasoning in the Sanjia Coal Mine in Guizhou Province was 71.43%, while the relative accuracy rate was close to 100%. Thus, it was concluded that: (1) the accident path mining method proposed in this paper is feasible for main accident path mining. (2) The Bayesian network model for the causes of coal and gas outburst accidents established in this study can be practically applied for the sensitivity analysis of accident causes and exhibits high reliability in the probabilistic reasoning of accident causes. The results of this study is expected to aid in the prevention of coal and gas outburst accidents, and provide reference and help for the path mining of other accident causes and the probabilistic reasoning of accident causes.

The specific risk assessment of informal settlements (IS) is important for the management of IS and protection of environmental safety and public health. In this paper, we introduced the different types of IS in China, and conducted the fire risk assessment on 26 burning buildings in these IS, providing a semi-quantitative and scenario fire risk perception of IS in China for the readers. Two methods, the risk index and the Bayesian network, are proposed and adopted for the fire risk assessment in IS. First, a risk index system with a total of 69 factors is used to assess the degree of fire risk of buildings in IS semi-quantitatively, and the result shows that fire equipment and fire safety management on IS are seriously lacking. Then, a Bayesian network of building fire risk with a total of 66 nodes was established to assess the fire risk from ignition to spread as well as the safety evacuation. Overall, the possibility of ignition is high, but due to the role of fire equipment and fire protection design, the possibilities of fire from ignition to spread is gradually reduced. Finally, we also put forward some feasible suggestions for occupants in IS, community organizations and emergency managers to reduce the fire risk from the aspects of fire equipment and fire safety management.

针对地铁车站侧向机械排烟系统中的烟气吸穿现象,本文在排烟口下沿加装排烟挡板以提高机械排烟效率.应用火灾模拟软件FDS数值模拟计算站台内烟气温度分布,排烟口的流场分布,压力损失增加量和排烟口CO体积浓度等,分析了排烟挡板的宽度和设置方式对机械排烟中烟气层吸穿的影响.研究表明,排烟挡板的设置有利于避免烟气吸穿现象的发生,改善机械排烟效果.排烟挡板宽度为0.6 m,角度为30° 时机械排烟效果最好.

This paper focusses on real-time detection and abnormal state prediction through a set of wireless equipment of fire water system to provide a better solution for fire inspection. The main goal of this research is to improve the fire extinguishing efficiency. Firefighting and rescue operations are urgent, and concealed or dry fire hydrants may cause failure cases of firefighting. To prevent this situation, this paper proposed the wireless equipment of fire water system to monitor the multiple indicators of outdoor hydrant, indoor hydrant, and sprinkler network. The objective of the experiment is to realize the real-time monitoring and obtain the data information of the state of the fire water system. Also, the multiple linear regression analysis method is adopted to analyze the monitored water pressure data to achieve rapid identification of abnormal water pressure value. The results indicate that the data transmission of the wireless equipment of fire water system is reliable, the pressure is accurately monitored below 0.40 MPa, and the average relative error of abnormal water pressure monitoring results is less than 6%. The new equipment and algorithm are used not only to monitor the state of water supply system, but also to depict the change of water consumption in the monitoring environment and predict the unconventional state, which provides directional help for fire patrol and inspection.

Coal and gas outbursts are severe accidents that can occur in coal mines. Half of coal and gas outburst accidents have occurred in China. Previous studies on coal and gas outburst accidents have focused on the mechanisms that result in accidents, gas extraction, accident prediction, and early warning. However, accidents involving coal and gas outbursts result from the combined effects of multiple factors. However, a systematic analysis of coal and gas outburst accidents is still lacking. In this study, 84 coal and gas outburst accidents occurred in China from 2008 to 2018 were used as a sample. The 24Model was used as the accident analysis theory, and the accident case data driven method was used to analyse the causes of coal and gas outburst accidents. In particular, the reasons for behaviour were systematically studied. The following can be inferred from analysis: (1) The causes of unsafe conditions are primarily reflected in three aspects. (i) Gas factor. Coal mines with high gas content and pressure remain the focus of outburst prevention. (ii) Coal factors. The occurrence of coal and gas outburst accidents has no strictly positive correlation with the depth of a coal seam. 50% of coal and gas outburst accidents occur in medium-thick coal seams. (iii) Geological structural factors. Ground stress (30.95%), coal thickness change (22.62%), and faults (22.62%) are the most frequently occurring geological structural factors in outburst accidents. (2) The reasons for unsafe acts are primarily reflected in 151 unsafe acts, 17 key basic unsafe acts, nine key categories unsafe acts, and four key stages. The personnel and proportion of these unsafe acts are senior leaders (53.55%), middle managers (31.56%), and front-line miners (14.89%). (3) Reasons for individual safety capabilities. Insufficient safety knowledge is manifested in seven aspects, including insufficient anti-outburst knowledge. The performance and proportion of poor safety awareness are safety system awareness (60.66%), safety risk awareness (20.49%), and safety responsibility awareness (18.85%). From 151 unsafe acts, 90 habitual violations acts that caused coal and gas outburst accidents were categorised. The performance and proportion of poor safety psychology were fluke psychology (40.85%), adventure psychology (23.36%), convenient psychology (20.22%), and paralysis psychology (15.57%). (4) The deficiencies of the safety management system are reflected in the failure to comply with the safety policy, imperfect safety management organization structure, lack of professional and technical personnel, and lack of 29 safety procedures in seven systems. (5) The lack of safety culture is primarily reflected in 22 safety culture elements. Among these, there are 19 elements had a frequency of & GE; 50%. The systematic analysis of coal and gas outburst accidents conducted in this study can reveal the causes of such accidents more comprehensively and provide a reference and basis for both safety training and management.

为了研究少量汽油液雾对低体积分数甲烷爆炸特征的影响,利用20 L球形爆炸测试装置,研究了1、2 mL汽油的液雾单独与空气混合的爆炸情况.通过改变甲烷体积分数,研究了甲烷分别与1、2 mL汽油的液雾混合后的爆炸特征,分析了汽油添加量对整个体系的爆炸下限影响.结果表明,汽油对甲烷-空气混合物爆炸影响非常显著,添加量分别为2、2.5、3 mL时,pmax分别是0.11、0.79、0.82 MPa,相应的(dp/dt)max分别是10.57、32.52、108.53 MPa/s.甲烷体积分数为6％时,汽油添加量为2 mL时,pmax是1.01 MPa,比添加1 mL汽油时增大31％,比未添加汽油时增大了320％.甲烷和汽油液雾混合后,其混合体系的爆炸下限低于各自在空气中的爆炸下限.1 mL汽油与空气混合物不发生爆炸,与体积分数≥3.5％的甲烷混合后能够发生爆炸.2 mL汽油与体积分数≥0.3％的甲烷混合,该体系依然能够发生爆炸.研究结果能够为封闭和半封闭空间中泄漏燃气与其他可燃性液体蒸气混合物的爆炸及预防提供数据支撑.

积极开展消防安全文化建设和消防知识科普工作具有重大而深刻的现实意义.应急科普场馆是消防知识科普的主要场所之一,针对7类不同类型的应急科普场馆,引入场景化应急科普的理念,研究了开展场景化应急科普的优势,并按照传播学中的5W理论,提出了不同类型应急科普场馆的场景化应急科普模式,从而提升消防应急科普场馆的科普能力.

为了研发高效干粉灭火剂粉体,提升干粉灭火剂的灭火效果,本文以蛭石粉为研究对象,采用NaCl、NaHCO3、MgCl2和KCl四类离子溶液对不同粒径的蛭石粉进行改性处理,并分析了原始蛭石粉末以及改性后的蛭石粉的膨胀率和表面微观结构.随后开展了ABC干粉灭火剂、未改性蛭石粉、MgCl2改性蛭石粉、NaCl改性蛭石粉、KCl改性蛭石粉、NaHCO3改性蛭石粉扑灭油池火实验,分析了灭火时间、灭火剂用量以及复燃情况.研究结果表明:粒径150μm的蛭石粉末膨胀率最大,且经过改性的蛭石粉末的膨胀率要明显大于原始蛭石粉末,其中MgCl2改性后的蛭石粉膨胀率最大,接近2.4左右;从灭火时间来看,灭火效果从低到高依次为ABC干粉灭火剂、未改性蛭石粉、MgCl2改性蛭石粉、NaCl改性蛭石粉、KCl改性蛭石粉、NaHCO3改性蛭石粉;从灭火剂用量角度可得出:改性蛭石粉灭火用量要明显小于未改性蛭石粉.其中,NaHCO3改性蛭石粉用量与ABC干粉灭火剂用量相对接近.从复燃角度来看,膨胀蛭石粉末的密度较小,可长时间悬浮在油品表面,有效阻止复燃,效果明显优于传统的ABC类干粉灭火剂.

应用数值模拟的方法研究了室外地上式消火栓在不同出口开关状态下的压降和流动特性,计算了管路内多个消火栓间距不同时的流量特性.结果表明,消火栓出口约19％的流体流速远低于平均流速,无法喷向指定灭火点;打开多个出口会导致每个出口压力降低58.3％～85.7％、流量减小28.3％～62.7％,不利于高层灭火和远距离灭火;打开管路内多个消火栓时,单个消火栓流量减小了13.5％～33.8％,总流量减小了14.6％～27.0％;随着消火栓间距从10 m增大至120 m,位于中下游的消火栓流量会逐渐减小.研究可为消火栓的安装和使用提供指导.