On the non-monotonic wind influence on flammable gas cloud from CFD simulations for hazardous area classification

Hazardous areas are defined as a result of a variety of variables as storage temperature, pressure, leak orifice size, physical properties of flammable substance, and wind characteristics. The potential formation of an explosive atmosphere must be accurately assessed to ensure process safety. Therefore, computational fluid dynamics (CFD) arises as an important tool for accurate predictions as recommended by the international standard IEC 60079-10-1 (2015). This study aims to analyze the influence of wind velocity magnitude and direction on the hazardous area classification. The authors evaluated the extent and volume for methane, propane, and hydrogen leakages from a CFD model. For each flammable gas, the wind velocity magnitude and direction were regularly varied. The outcomes show that the behavior of the plume size as the wind varies mainly depends on the gas concentration. Counter-flow wind directions lead to zero relative velocity closer to the release point, which concentrates the gas, and wind in the release direction promotes a higher dilution of the gas cloud increasing the hazardous extent while decreases the volume. As a consequence, the wind also influences the zone type, which was accurately predicted from CFD simulations and significant differences were found when compared to the standard analyses. These differences are, to some extent, related to the consideration of wind velocity effects on the gas jet release.     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.     

The method for leakage detection of urban natural gas pipeline based on the improved ITA and ALO

To solve the problems of the difficulty in early leakage monitoring and larger positioning error for urban hazardous chemicals pipelines, the optimized method based on the improved Inverse Transient Analysis (ITA) and Ant Lion Optimizer (ALO) was proposed. Firstly, based on the obtained experiment's results of leakage of natural gas in the non-metallic pipeline, the segment classification method was incorporated into the pressure gradient calculation. The modified method can adapt to the multi-node characteristics of urban pipe networks and help to obtain the preliminary positioning calculation results after optimization. Then the calculation results were embedded in the ITA calculation model. The input parameters of the gas pipeline such as boundary conditions, leakage rate and friction coefficient were used to establish the characteristic linear equations. Then the objective function of the least-squares criterion was defined, and the improved ITA model suitable for leakage detection of urban natural gas pipeline networks was constructed. Finally, the ALO was used to optimize the calculation process of the improved ITA model, and iteratively optimize the optimal friction coefficient and its corresponding minimum objective function (OF) value. As a result, a more precise location of the leakage source was calculated. The validation of the modified method is conducted by comparing the calculated values with the experiment's results. The results show that the method can accurately predict the location where the pipeline leakage occurs. The minimum error is 3.17%. Compared with the traditional ITA, this method not only accelerates the convergence speed of the objective function, but also improves the accuracy of location calculation.     

海洋平台井喷含硫天然气扩散危险区域研究

针对海洋酸性气田开采过程中含硫天然气井喷失控扩散问题,采用CFD方法建立井喷含硫天然气扩散后果预测与评估模型。综合考虑天然气爆燃与硫化氢毒害风险因素,对不同场景条件下的含硫天然气扩散过程开展数值模拟,研究硫化氢浓度、风向、风速等因素对含硫天然气扩散行为的影响,预测和评估天然气扩散所形成的危险区域和硫化氢气体扩散所形成的毒害范围。研究表明:随着硫化氢浓度的增加,燃爆区域无明显变化,而毒害区域明显增加;船艉来风导致的事故后果最为严重,左、右舷来风有利于危险气体的扩散与消散;风速越大,燃爆区域和毒害区域范围越小,但是在船艏来风且风速较大的工况下,硫化氢气体竖直扩散距离降低且逐渐贴近生活区,容易造成作业人员中毒事故的发生。     

基于多因素的LSTM瓦斯浓度预测模型*

为解决煤矿瓦斯浓度预测问题,提出基于多因素的LSTM瓦斯浓度预测模型。模型首先对煤矿多源监测数据进行数据融合、缺失值处理;其次通过特征衍生、有监督化、无量纲化,融合各环境因素特征和时序数据的时间性特征,且衍生出更多交叉项特征和高次项特征;然后利用经验法和逐步试错法确定隐藏层维度;最后进行模型训练和测试。研究结果表明:基于多因素的LSTM瓦斯浓度预测模型的RMSE仅为0.021,MAE为0.01,比单因素LSTM模型、RNN模型预测效果好。因此,基于多因素的LSTM瓦斯浓度预测模型可更精准地进行瓦斯浓度多步预测,促进煤矿安全生产。     

基于微积分的LPG和CNG喷射火模型建立和三维数值模拟

由液体和气体泄漏引发的喷射火灾事故屡见不鲜,而现有的喷射火模型却有诸多缺陷。将喷射火形状近似为圆锥,鉴于液体和气体的泄漏速度随压强和温度均有变化、点源离地面会有一定高度、风速对火焰倾角也有影响、圆锥喷射中心线上每个点源对目标的热辐射均不同等实际情况,新建液体和气体扩展半径的计算方法,引入风速对火焰高度和火焰偏角的作用,比较AGA法和Thornton模型,发现AGA法更加符合实际,再利用微积分原理建立三维点源喷射火模型,展示火焰形状对目标的影响,从而得出热辐射的最终危险性。最后以LPG和CNG储罐喷射火事故为例进行对比验证,首次模拟出喷射火的形状,更清晰准确地展示了目标入射热辐射通量随目标点位置的变化和危险范围,可为火焰阵面处的消防安全决策提供参考。     

城区天然气管道泄漏数值模拟与爆炸危害分析

在人口密度为三级和四级的城区内,密集的高建筑物对天然气管道泄漏后的扩散和流场形成产生重要影响。本文以某城市的实际情况为例,建立多建筑物的空间几何模型,采用k-ε湍流方程,SIMPLE算法,模拟了在三种不同风流速度、三种不同压力条件下,城区天然气管道泄漏气体在多建筑物地形中的扩散情况。根据模拟结果,依据天然气的爆炸极限,对模拟结果及其火灾爆炸危害的范围进行了对比分析。结果表明,CH4气体的泄漏扩散同时受管道压力、风流速度和周围建筑物的影响;同时受当地风速的影响,泄漏气柱在风流作用下会发生偏折,造成阻挡风流的建筑物内侧危险气体浓度升高,大大增加建筑物周围环境的危险性。研究结果对城区天然气管道的建设具有一定的指导意义。     

高压天然气非恒定速率泄漏扩散数值模拟研究

为保障天然气工业安全生产与运营,以某天然气储配厂为例,采用等效喷嘴和过程模型,利用FLACS软件对罐区高压天然气非恒定速率泄漏扩散进行数值模拟,考察环境风速及泄漏时间对气体泄漏扩散的影响.结果表明:储存压力为1.05 MPa的天然气储罐发生泄漏会产生欠膨胀射流,泄漏初期具有447.44 kJ的高动能,并在近场扩散起主导...     

气体扩散数学模型在安全评价方面的应用

工业生产、储运过程涉及储存大量气态和液态的危险性物质,该类物质大都易燃易爆、有毒有害。民用燃料的输送和储藏,也涉及易燃易爆。气体或液体物质发生泄漏事故,可能对附近居民和环境造成极大的危害。气体物质一旦发生泄漏,很难将其限定在一定范围内,势必造成大面积的危害。因此,事故状态下有毒气体的扩散,是安全评价中灾害后果评价的重要内容。笔者通过梯度输送理论,建立了无风条件下气体扩散的动态模型。在限定容器中气体介质是中性气体的情况下,由于中性气体在扩散过程中各向同性,扩散方程获得了简化。进一步限定容器形状为最常见的圆柱形,确定了模型的边界条件,求出了该条件下扩散方程的解析解。开发的扩散模型建立的理论依据是梯度输送理论即单位时间内物质通过单位面积的输送的通量与浓度梯度成正比。同时对建立的模型在安全评价方面的应用进行了初探,结合实例提出了该模型的应用方向。     

A comprehensive empirical investigation of the relationships among variables of the groupthink model

Although a quarter of a century has passed since Janis proposed his groupthink model in 1972, there is very little consensus among researchers on the validity of the model. This study conducted a comprehensive empirical investigation of Janis's model by including all 24 variables in the research. Data were collected from 64 four‐person ad hoc groups; the group discussion sessions were videotaped and content‐analysed afterwards. Statistical analyses revealed that Janis's predictions about the causal relationship among four groups of variables in the model are only partially correct; when the effects of individual independent variables on the dependent variables were tested, Janis's predictions were confirmed in only two out of 23 cases. Copyright © 2000 John Wiley & Sons, Ltd.     

颗粒煤的多扩散系数瓦斯解吸模型及扩散参数反演研究

为研究颗粒煤瓦斯解吸规律,基于Fick定律建立了颗粒煤的多扩散系数瓦斯解吸 模型,完成了颗粒煤瓦斯解吸模型的数值试验。引入了非负约束最小二乘法反演算法（ NNLS）,通过试验数据反演得出颗粒煤的扩散参数的B谱,从而确定出颗粒煤瓦斯扩散 系数D的准确范围。研究结果表明:颗粒煤瓦斯解吸符合Fick扩散定律,颗粒煤的多扩 散系数瓦斯解吸模型能很好地解决单一扩散系数模型的扩散系数随时间衰减的问题,准 确反映了颗粒煤瓦斯解吸规律,单一扩散系数瓦斯解吸模型只是多扩散系数瓦斯解吸模 型的一个特例;NNLS是一种有效的反演算法,利用NNLS方法可以准确反演出颗粒煤瓦斯 解吸过程中的扩散参数的B谱,通过B谱可方便计算出颗粒煤的瓦斯扩散系数。     

A systems-based approach for modeling of microbiologically influenced corrosion implemented using static and dynamic Bayesian networks

Microbiologically influenced corrosion (MIC) is a microbial community assisted degradation of materials affecting chemical processing and oil and gas industries. MIC has been implicated in incidents involving loss of containment of hazardous hydrocarbons which have led to fires and explosions, economic and environmental impact. The interplay between abiotic environmental factors and dynamic biotic factors in MIC are poorly understood. There is a lack of mechanistic understanding of MIC and very few models are available to predict or assess MIC threat. Here we report on the development of a model to assess the susceptibility to MIC. The high-resolution model utilizes 60 independent nodes, including operational and historical failure analysis data, and is built by combining empirical relationships between the abiotic and biotic variables impacting MIC. Both static and dynamic Bayesian-network (BN) approaches were used to combine heuristic and quantitative states of variables to ultimately yield a susceptibility measure for MIC. A confidence-in-information metric was generated to reflect the amount of data used in the estimation. A susceptibility to MIC of 45%–60% was estimated by the model for ten different scenarios simulated using case-studies from literature. The susceptibility to MIC estimated by these scenarios was further interpreted in the context of these cases. This systems-based MIC model can be utilized as an independent estimator of susceptibility or can be incorporated as a sub-model within comprehensive safety threat assessment models currently utilized in industry.     

物流基地危险货物储区外部安全防护距离研究

在物流基地规划建设中,危险货物储区外部安全防护距离的设定是很重要的一个组成部分。针对危险货物储区化学爆炸与毒气泄漏事故影响范围大、后果严重等特点,先应用鱼刺图事故分析原理分析危险货物储区危险因素,再建立化学爆炸与毒气泄漏危害距离计算模型,最后以冲击波伤害与毒气中毒有效剂量标准为依据,根据已建模型计算化学爆炸与主、次导风向毒气泄漏伤害距离。结果表明在设定安全防护距离时,结合化学爆炸、毒气泄漏危害距离模型,能得到合理的危险货物储区周边安全防护距离,具有实际应用价值。     

基于Spark Streaming流回归的煤矿瓦斯浓度实时预测

为了实时分析瓦斯监测流数据并对瓦斯浓度进行准确预测以实现瓦斯灾害实时预警,以实时流数据处理框架Spark Streaming构建基于流回归的瓦斯浓度实时预测系统。系统采用分布式流处理技术,可使基于回归算法的瓦斯浓度预测模型更新周期达到秒级,提高了瓦斯浓度预测精度,满足流式大数据处理的实时性要求。实验表明:应用Spark Streaming流回归预测系统在采样周期为5 s的瓦斯监测数据流上进行实时预测时,预测平均均方根误差随模型更新周期的缩短而减小,模型更新周期可达15 s,且更新周期为45 s时预测总均方根误差最小,既能保证预测精度,又能提高瓦斯灾害预警时效。     

基于无线传感器网络的气体泄漏源快速定位方法研究

为及时发现大气环境中危险化学品泄漏事故,快速准确判断泄漏源位置,实现有效的监测监控,开发出集气体质量浓度信息采集、时间校准、无线收发等功能于一体的集成探测模块。在此基础上,建立基于无线传感器网络(WSN)的气体泄漏实时监测平台,提出实时监测数据和高斯扩散模型相结合的气体泄漏源快速定位方法。通过开展实际场地泄漏试验,实现气体泄漏源的快速定位,并分析试验系统的敏感度。结果表明:气体泄漏扩散受风力影响很大;合理布局探测器,能有效提高泄漏源定位精度,为危险气体泄漏事故的应急决策提供决策参考。     

Towards more detailed determination of third party impact on risk on natural gas pipelines: Influence of population density

The paper presents a refined way to quantify the effects of third party interference on risk that is posed on people by transmission pipelines for natural gas. The main focus is set on the influence of population density on risk. Using the interdisciplinary approach, the presented study combines the knowledge from relevant risk assessment recommendations, physical consequences of hazardous events, existing history databases of hazardous event frequencies and urban planning. A quantitative boundary between two most populated types of area was established. A flexible risk coefficient was determined for a suburban type of populated area that is dependent on average population density. Consequently, a new approach for determination of a hazard distance from the pipeline and area boundaries for calculation of average population density was presented. This differs from the established methods described in some guidelines, but is based on results of applied quantitative risk assessment. The final result is more accurate determination of risk levels in suburban areas. Described methods may serve as a supplement to the existing models for quantitative risk assessment on pipelines used in natural gas transportation and may be used by pipeline operators as well as policy- and decision makers.     

仓面浇筑立体空间冲突致灾过程建模与伤害评价*

为定量评价大坝浇筑过程空间冲突致灾事故后果,通过对缆机运输典型情景进行危害能量运动分解,确定在水平和竖直方向运动方式,阐述危害能量在流动路径相互转化形式;根据能量守恒定律,分析危害能量在吊罐与承灾体碰撞接触之间转移规律,将吊罐与人头部接触过程抽象为带强阻尼的弹簧振子系统,建立危害能量与碰撞冲量相等的动量方程,量化空间冲突致灾后果。结果表明:吊罐坠落危害能量极大,产生的碰撞冲击力与吊罐的运输速度、高度呈正相关,与阻力系数呈负相关,风速、载重对其影响较大。对比2种不同碰撞情景发现,机械碰撞因顶部承载力缓冲,对人头部产生伤害远小于直接碰撞,并对碰撞结果分级评价,研究结果可为大坝浇筑交叉作业空间冲突致灾风险评价提供参考。     

Numerical analysis of toxic cloud generation and dispersion: A case study of the ethylene oxide spill

The present study examined the accidental spill of ethylene oxide, and a sensitivity analysis of the corresponding consequences was conducted using computational fluid dynamics (CFD). A validation of the gas dispersion CFD model against the experimental data sets included in the model evaluation protocol (MEP) was performed. The effect of the variability of the wind velocity on the extension of the hazardous areas and pool evaporation characteristics was evaluated. Additionally, the mitigation effects of the dike walls surrounding a spill were discussed. CFD simulation results have shown that the mitigation effect of dike walls is determined by their influence on both gas dispersion and pool evaporation and depends strongly on wind velocity in terms of toxic impact distances.     

危险化学品仓储事故演化规律及消防力量需求研究

危险化学品仓储火灾事故复杂,处置难度大,易引发事故多米诺效应,对人民的生命和财产造成严重威胁。本文分析了危化品仓储火灾爆炸事故的演化规律和事故风险,结合事故案例剖析危化品仓储火灾爆炸事故后果及对周边区域的影响。针对危险化学品仓储火灾爆炸事故,建立危化品仓储火灾扑救泡沫需求评价二级指标体系,采用模糊层次分析法建立了泡沫灭火剂用量评价数学模型,并根据救援力量类别需求、各种应急救援装备与作战人员需求建立危化品火灾消防力量需求预测模型,准确预测危化品仓储事故消防力量需求。