Root causes of hydrocarbon leaks on offshore petroleum installations

Hydrocarbon leaks on offshore installations may result in severe consequences to personnel, to the environment and to assets. In order to prevent such leaks, it is crucial to understand their root causes. The objective of this paper is to study the circumstances of hydrocarbon leaks on the Norwegian continental shelf (NCS). In the study, all reported hydrocarbon leaks from process inventories on all offshore installations on the NCS, with an initial leak rate higher than 0.1 kg/s in the period 2008–2014, have been considered. This includes 78 hydrocarbon leaks, of which about 60% have occurred during manual intervention on normally pressurized systems. The dominating activity when leaks occur is preventive maintenance. A significant fraction of the leaks occur during the preparation for maintenance; such a preparation is typically carried out during the night shift. About half of the leaks are associated with wellhead area and manifolds, separation and compression systems. A substantial fraction of the leaks can be associated with verification faults, dominated by the failure to comply with procedural requirements that are needed to carry out independent verification.     

基于民航安全信息文本挖掘的进近着陆致险因素分析

为有效识别飞行进近着陆阶段致险因素,以进近着陆不安全事件为对象,利用Python语言和文本挖掘方法对文本进行预处理,获取包含关键特征值和特征权重的高维稀疏原始特征向量集合;利用卡方统计对致险因素原始特征项进行降维,得到30个维度文本特征项,借助词云图将文本挖掘结果可视化;通过人为因素、飞机故障、环境因素和管理因素4个层...     

基于地铁运营日志文本挖掘的危险源辨识算法研究*

为探究地铁运营过程中致使险性事件发生的关键因素,识别关键危险源,加强对危险事件的预防管控,研究基于地铁运营日志文本挖掘的地铁运营危险源辨识算法,通过引入自定义专业术语词典和自定义停用词典,删除干扰数据并进行数据分词,实现对地铁运营日志的预处理,进一步构建进行危险源辨识的AFP-tree算法,实现对日志中关键危险源的挖掘。研究结果表明:AFP-tree算法能够显著提升计算效率,通过分析共挖掘出25类有效关键危险源,研究结果可为地铁运营单位实现“事前”的事故预防提供重要依据。     

基于文本挖掘和复杂网络的事故致因重要度评估方法——以房屋市政较大以上事故为例

为充分挖掘事故调查报告中的有效信息,明确安全管理工作的内容.首先,利用文本挖掘分析事故调查报告,采用最小词频阈值文档频改进信息增益评估函数对分词结果降噪,通过回溯特征项在报告中的具体表述,提取事故致因,再构建同义词词库.然后,引入复杂网络以改进TF-IDF,综合事故致因因素的关联特征评估其重要度.最后,以房屋市政较大及...     

基于文本挖掘的建筑工人不安全行为及其影响因素研究

人的不安全行为和物的不安全状态是引发建筑施工安全事故的直接原因,其中人的不安全行为占主导作用.识别工人不安全行为表现及其影响因素规律,是采取针对性安全管理措施的前提.基于文本挖掘技术和R语言,对334份建筑施工安全事故报告进行分词处理和特征项降维,识别出8项建筑工人不安全行为及其27项影响因素;通过构建共现网络和中心性...     

An evaluation of risk assessment framework for industrial accidents in India

Due to rapid industrialization, with high population density and constraints of land, it is expected that level of risks arising from the hazardous industries will increase in India in the coming decades. However, 30 years after the Bhopal accident (1984), except a few discrete regulations, there is as yet no integrated system for assessing and managing risks arising out of these hazardous industries in India. The gravity of aspects related to the management of industrial risk still remains crucially important. In particular, there is no standard guideline on risk analysis methodology, acceptability or tolerability criteria, nor is there an accident database or a risk reduction strategy for the areas where risk levels are already high. On top of this, there are technical and legislative gaps in the institutional framework to implement any of the above mentioned issues. With the backdrop of the Bhopal gas tragedy, the objective of this paper is therefore to evaluate the effectiveness of a comprehensive risk assessment framework for the emerging economy of India, in order to control and/or to reduce the risk level that exists. In this context, regulations and policies pertaining to industrial risk assessment were reviewed.     

A CFD based explosion risk analysis methodology using time varying release rates in dispersion simulations

A full probabilistic Explosion Risk Analysis (ERA) is commonly used to establish overpressure exceedance curves for offshore facilities. This involves modelling a large number of gas dispersion and explosion scenarios. Capturing the time dependant build up and decay of a flammable gas cloud size along with its shape and location are important parameters that can govern the results of an ERA. Dispersion simulations using Computational Fluid Dynamics (CFD) are generally carried out in detailed ERA studies to obtain these pieces of information. However, these dispersion simulations are typically modelled with constant release rates leading to steady state results. The basic assumption used here is that the flammable gas cloud build up rate from these constant release rate dispersion simulations would mimic the actual transient cloud build up rate from a time varying release rate. This assumption does not correctly capture the physical phenomena of transient gas releases and their subsequent dispersion and may lead to very conservative results. This in turn results in potential over design of facilities with implications on time, materials and cost of a project.In the current work, an ERA methodology is proposed that uses time varying release rates as an input in the CFD dispersion simulations to obtain the fully transient flammable gas cloud build-up and decay, while ensuring the total time required to perform the ERA study is also reduced. It was found that the proposed ERA methodology leads to improved accuracy in dispersion results, steeper overpressure exceedance curves and a significant reduction in the Design Accidental Load (DAL) values whilst still maintaining some conservatism and also reducing the total time required to perform an ERA study.     

基于政策工具的中国夜间经济政策研究

夜间经济的可持续发展依赖于相关政策工具的合理选择、科学设计与有效实施。采用内容分析法对我国105份夜间经济政策文本进行了单元划分、条文编码和频数统计,进而从政策工具和发展系统两个维度对我国夜间经济政策进行了深入剖析。结果发现:①我国夜间经济供给型政策工具应用不足。②我国夜间经济环境型政策工具使用过溢。③我国夜间经济需求型政策工具内部结构不均。最后,从平衡政策工具应用、科学配套系统要素、精准匹配发展机制3个方面提出建议,以期为未来我国夜间经济政策的制定与完善提供理论参考。     

理解生态文明:从哲学思想到国家发展战略

系统易懂地阐释生态文明概念,对于帮助国际社会清晰地理解和认识中国的生态文明,以及在全球视野下推进生态文明建设至关重要。中国政府提出的生态文明建设已经从过去传统的哲学思想上升为国家的发展战略,是具体的、可理解的、可操作的国家治理概念和术语。理解生态文明建设的要点包括:建设生态文明是中国国家发展总体战略的核心构成,是中国国家治国理念和发展战略的转变,是国家环境与发展转型的整体性战略,其路径是将生态文明融入到经济、政治、社会和文化等领域建设的全过程和各方面,其关键手段和工具是大幅提高经济绿色化程度,其主阵地和根本措施是加强环境保护。     

Using machine learning to disentangle homonyms in large text corpora

Systematic reviews are an increasingly popular decision‐making tool that provides an unbiased summary of evidence to support conservation action. These reviews bridge the gap between researchers and managers by presenting a comprehensive overview of all studies relating to a particular topic and identify specifically where and under which conditions an effect is present. However, several technical challenges can severely hinder the feasibility and applicability of systematic reviews, for example, homonyms (terms that share spelling but differ in meaning). Homonyms add noise to search results and cannot be easily identified or removed. We developed a semiautomated approach that can aid in the classification of homonyms among narratives. We used a combination of automated content analysis and artificial neural networks to quickly and accurately sift through large corpora of academic texts and classify them to distinct topics. As an example, we explored the use of the word reintroduction in academic texts. Reintroduction is used within the conservation context to indicate the release of organisms to their former native habitat; however, a Web of Science search for this word returned thousands of publications in which the term has other meanings and contexts. Using our method, we automatically classified a sample of 3000 of these publications with over 99% accuracy, relative to a manual classification. Our approach can be used easily with other homonyms and can greatly facilitate systematic reviews or similar work in which homonyms hinder the harnessing of large text corpora. Beyond homonyms we see great promise in combining automated content analysis and machine‐learning methods to handle and screen big data for relevant information in conservation science.