HNO_3在气相及SiO_2表面的紫外光解反应

利用紫外及红外吸收光谱等分析手段对365 nm光照下HNO3在气相与SiO2表面的光解反应进行了研究.考察了HNO3浓度、光照时间、相对湿度等条件对反应的影响.结果表明:随着HNO3浓度及光照时间的增加,光解产生的NO2和NO浓度均呈指数增加;无水汽情况下,400 Pa的HNO3光解45 min后,产生NO2及NO浓度比气相光解产生的分别高约3及1.7倍.HNO3光解产生的HONO的浓度随相对湿度的增加而呈线性增加,在SiO2颗粒物表面光解产生的NO2浓度随着相对湿度的增加而减少,而NO浓度则随之增大.400 Pa的HNO3光照45 min后,SiO2表面光解产生的HONO浓度是气相光解的3倍、SiO2表面暗反应的约30倍.     

基于放射性碳分析的多环芳烃源解析:技术进展和准确性评估

化石燃料燃烧和生物质燃烧是污染物多环芳烃(polycyclic aromatic hydrocarbon,PAHs)的两大来源.放射性碳(14C)分析近年用于评估这两类源对环境中PAHs的相对贡献.此方法基于化石燃料和生物质的14C含量差异,即化石燃料不含14C,而生物质的14C浓度有一个较稳定值.14C的自然丰度极低(约10-12),因此检测PAHs这样的痕量污染物的14C含量一度极具挑战.1990年代中期,加速器质谱的技术突破使得对环境样品PAHs的14C分析具有实用价值.要准确测出PAHs的14C含量,须先从化学成分复杂的环境样品中分离出高纯度的PAHs.制备气相色谱因其出色的分离能力而成为目前环境样品PAHs14C分析必备的工具.本文意在简介基于14C分析的PAHs源解析的基本原理、技术进展,以及评估该方法获得的PAHs源解析结果的准确性.     

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.     

CFD-based assessment and visualization of the failure consequences of LPG tankers

This paper presents a simulation analysis of the explosions following an LPG leak and visualizes the consequences of the accident to reduce the consequences of the LPG leak explosion. Firstly, this paper proposes a CFD numerical simulation-based method for visualizing the consequences of LPG tanker failure. The method combines satellite maps and CFD numerical simulation data to visualize the consequences of LPG leaks and explosions, taking into account the influence of obstacles on the danger range of leaks and explosions; Secondly, this paper applies the method to a liquefied petroleum gas accident that occurred in the Wenling section of the Shenhai Expressway and performs CFD numerical simulation on the accident process and visualizes the consequences of the accident. Therefore, this method can provide a theoretical reference for the prior prevention of LPG accidents and the analysis of the consequences of accidents, as well as certain practical guidance instructive.     

An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents

Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs.     

Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.     

Dynamic risk assessment for underground gas storage facilities based on Bayesian network

Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage process safety.     

A bayesian network-based safety assessment method for solid propellant granule-casting molding process

The safety of the solid propellant molding process is vital for the stable production of high-quality propellants. Failure events caused by abnormal parameters in the molding process may have catastrophic consequences. In this paper, a Bayesian network (BN) model is proposed to assess the safety of the solid propellant granule-casting molding process. Fault tree analysis (FTA) is developed to construct a causal link between process variables and process failures. Subsequently, expert experience and fuzzy set theory (FST) are used to obtain failure probabilities of the basic events (BEs). Based on the mapping rules, FTA provides BN with reliable prior knowledge and a network structure with interpretability. Finally, when new evidence is obtained, the probability is updated with the diagnostic reasoning capability of BN. The results of the sensitivity analysis and diagnostic inference were combined to identify key parameters in the granule-casting molding process, including curing temperature, vacuum degree, extrusion, calendering roll distance, length setting value, holding time, and polish time. The results of this paper can provide effective supporting information for managers to conduct process safety analysis.     

压缩空气泡沫在长距离管路输送中压降的数值模拟

基于Fluent对压缩空气泡沫在长距离管道中的流动特性进行了数值模拟研究,将压缩空气泡沫近似为弥散流,采用Saplart-Allmaras模型模拟了不同管径下压缩空气泡沫以及不同泡沫原液浓度的AFFF泡沫在长距离管道内的流动及压降变化。模拟结果表明,随着距离变化,各管径管道内压降均呈现线性变化,且随着压缩空气泡沫的流动,压降线性增大。管道管径对管内压降变化具有显著影响,管道直径越小,管道内压降越大;泡沫原液浓度对压降的影响较小,且压缩空气泡沫在长距离输送中的压力随距离线性衰减。将模拟结果与长距离输送的试验结果进行了对比,误差在10%以内。     

基于视频分析的空管员违规行为识别方法*

为利用视频数据对空管员违规行为进行智能化分析,降低不安全事件发生率,提出2阶段的违规行为识别模型（AR-ResNeXt）,基于实地调研构建空管员视频数据集,利用最小化动态多实例学习损失函数和中心损失函数,获得违规行为检测的判别特征表示,结合异常回归网络和ResNeXt网络,完成对空管员违规行为的时序区间检测与动作分类。研究结果表明:AR-ResNeXt模型在自制数据集中,其帧级AUC达到82.9%,分类准确率达到87.8%,可准确识别空管员发生违规行为的时序区间并进行分类,研究结果可为保障空中交通安全奠定基础。