关于液化石油气码头泄漏火灾应急措施的探讨

分析了液化气泄漏火灾事故的特点，介绍了LPG的理化性质及其危险特性，提出了LPG码头泄漏处置和火灾扑救的方法。     

Off-site emergency scenario, a case study from a LPG Bottling Plant

After Bhopal disaster, emergency planning in an industrial area has become inevitable. The off-site emergency plan is an integral part of any major hazard control system. Boiling Liquid Expanding Vapour Explosion (BLEVE) leads to fatal consequences. This paper highlights some salient features of the emergency scenario, which ultimately leads to fireball with enormous pressure wave all around.     

液化石油气罐区火灾爆炸危险性评价

液化石油气罐区属于重大危险源,一旦发生火灾爆炸事故,后果非常严重.评价其安全性,控制其危险,是重大事故预防的思想,也是国家安全生产法律、法规的强制要求.笔者根据安全工程学的相关原理,综合运用重大危险源评价法和灰色聚类法分别对罐区的固有危险性和现实危险性进行了评价,克服了重大危险性评价法未考虑环境因素这一缺陷,最后得出了其火灾爆炸危险性等级,为政府监管和企业对危险源的监控管理提供了可行的科学依据.     

基于随机抽样推测法的LPG储槽2次爆炸事故的模型研究

在借鉴LPG储槽2次爆炸事故后果不确定分析成果的基础上,对事故过程中的不确定参数进行重新分析与选择,将孔洞上方液位高度h0,气云的TNT当量系数α,泄漏开始到点火源出现之前的持续时间t亦作为不确定分析的参数,并利用随机抽样推测的不确定分析方法,对VCE与BLEVE 2次事故后果进行重新分析,获得了与前人研究成果差异较大的结果,并由此分析这些参数对于事故后果影响的显著性。同时对2次爆炸事故的伤害距离进行了研究与分析,由于LPG的闪蒸以及参数α的影响,本案例中LPG泄漏量为总量的80%-90%以上时,2次事故的死亡半径相等且达到最小。     

LPG混空气工程安全措施探讨

随着LPG混空气技术在工业和日常生活中的大规模使用,LPG储运、空混气的制备与储运以及空混气的使用等各个环节的潜在危险也就越来越明显地暴露出来,一旦发生事故,其后果十分严重. 对LPG混空气工程存在的危险因素进行分析,并提出了有针对性的措施及安全边界条件.     

民用压缩气体或液化气体气瓶库与周围建(构)筑物安全间距探讨

民用压缩气体和液化气体一旦发生爆炸，会造成很大的破坏力。根据压缩气体和液化气体的特性，通过计算，由爆炸中心到能保证人身安全和建筑(构筑)物和设备免遭破坏的最小距离。     

Risk analysis of an LPG storage facility in India

An assignment to carry out a hazard study and risk analysis of a gas processing complex has been described briefly, wherein well known procedures have been used to identify and investigate potential hazards. A method of avoiding unintentional overfilling of LPG storage tanks has been recommended, which utilizes the existing level control instruments.     

破片撞击LPG储罐失效数值模拟

在国内外液化气储罐失效的相关理论基础上,运用ANSYS和ANSYS LS-DYNA软件,对液化石油气储罐的失效过程中破片撞击的影响进行了数值模拟,为储罐的安全管理提供了指导.     

Simulation of scenario characteristics of LPG tank explosion accident and its contribution to emergency planning: A case study

Leakage and explosion of hazardous chemicals during road transportation can cause serious building damage and casualties, and adoption of highly-efficient emergency rescue measures plays a critical role in reducing accidental hazards. Considering a liquefied petroleum gas (LPG) transport tanker explosion accident that occurred in Wenling, Zhejiang Province, China on June 13, 2020 as example, this study proposes a risk assessment framework. This framework recreates the leakage and explosion of the accident process using FLACS v10.9, suggests plans for evacuation, describes the rescue areas of different levels, and explores the influence of environmental factors on the evacuation and rescue areas. The results show that simulated and predicted distributions of fuel vapour cloud concentration and explosion overpressure can provide a reference basis for rapid rescue activities; the characterization of the dynamic effects of wind speed, wind direction, and temperature with respect to the evacuation and rescue areas can be used as theoretical support for on-site adjustment of rescue forces. The role of obstacles can prevent the expansion of the evacuation areas under low wind-speed conditions, and the presence of highly congested obstacles determines the level of the rescue area. The results obtained are important for the risk analysis and the development of emergency rescue measures in case of explosion accidents associated with transportation of hazardous chemicals on high-hazard and high-sensitive road sections.     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.