燃气输配管网供气可靠性评价方法

燃气管网系统是一种可修复的系统,对这种系统需要研究其可靠性问题。燃气管网发生故障使系统的完整性状态发生变化,可以看作齐次马尔柯夫过程。考虑管网的维修性,结合关于管网阀门规则配置的研究成果,提出基于水力工况的燃气管网供气可靠度计算方法,使燃气管网可靠性评价实用化。     

第三方施工影响油气和城镇燃气管道安全评估

针对石油天然气管道、城镇燃气管道涉及第三方施工的安全保护,介绍浙江省石油天然气管道、城镇燃气管道保护安全评估工作的开展情况。对安全评估咨询报告编制要点(管道的精准探测、法律标准符合性评估、危险和风险因素辨识及评估、安全对策措施制定)进行了探讨。     

模糊神经网络在燃气风险评价的应用

提出了基于故障树信息、专家评价的模糊神经网络的风险评价方法,以焦炉煤气储配站系统为研究对象,对其进行了风险评价。     

A Wavelet Support Vector Machine‐Based Neural Network Metamodel for Structural Reliability Assessment

Wavelet neural network (WNN) has been widely used in the field of civil engineering. However, WNN can only effectively handle problems of small dimensions as the computational cost for constructing wavelets of large dimensions is prohibitive. To expand the application of WNN to higher dimensions, this article develops a new wavelet support vector machine (SVM)‐based neural network metamodel for reliability analysis. The method first develops an autocorrelation wavelet kernel SVM and then uses a set of wavelet SVMs with different resolution as the activation function of WNN. The output of network is obtained through aggregating outputs of different wavelet SVMs. The method takes advantage of the excellent capacities of SVM to handle high‐dimensional problems and of the attractive properties of wavelet to represent complex functions. Four examples are given to demonstrate the application and effectiveness of the proposed method.     

城市燃气管道环境风险评价和管理探讨

城市燃气输配管线使用年限过长,事故频发,安全隐患多,需对城市燃气管道进行环境风险评价。文章通过事故树分析,找出导致城市燃气管网泄漏的基本事件,介绍城市燃气管道环境风险识别、源项分析、后果计算、风险评价、风险管理等各个环节的内容和方法,提出了燃气管道在设计、建设及运行阶段加强环境风险管理的具体措施。该分析方法能够发现影响城市燃气管道安全的因素,解决存在的隐患问题,加强城市燃气管线的管理,对城市燃气管道环境风险评估和管理具有指导作用。     

油气长输管道风险分析及管道保护措施

分析油气长输管道危险性和风险,采用风险矩阵方法分析管道失效可能性和失效后果。结合管道完整性管理要求、管道保护存在的主要问题,提出管道保护改进措施。     

SELMA燃气管网检测车技术及应用

1 引言 燃气管网的安全运营是非常重要的,如何来保障安全运营,都采用那些设备和方法技术,目前是燃气公司的当务之急.我国近几年燃气管网发展速度很快,一般地级以上城市的管网长度均达数千公里以上,仅靠人工巡检是很难完成和做好的,因为这里存在着很多人为因素和客观现实条件的限制.     

城市燃气输配管网可靠性模拟分析

对在役管网的故障数据和历史资料进行统计处理和故障模式分析,建立了管网故障树,用蒙特卡罗法求解出管网的可靠度及模式重要度,计算了模拟误差。     

鱼类越冬池底泥若干特性的研究

对进行生物增氧的鱼类越冬池底泥低温（４℃）状态下的研究表明：（１）底泥吸附性氨态氮含量平均为０．１９４ｍｇ／ｇ（烘干重，下同），有效磷平均为０．０３９４ｍｇ／ｇ，有机物平均为２５．７ｍｇ／ｇ；（２）间隙水中氨态氮（ＮＨ＋４－Ｎ）含量为５．３１～３８．４ｍｇ／Ｌ，平均１４．０ｍｇ／Ｌ，占无机氮总量的９９．１％。活性磷酸盐（ＰＯ４－Ｐ）含量为０．１１１～０．６３２ｍｇ／Ｌ，平均０．３０１ｍｇ／Ｌ；（３）在无扰动条件下ＮＨ＋４－Ｎ的释放速率为３２．０～８８．４ｍｇ／（ｍ２·ｄ），平均５０．４ｍｇ／（ｍ２·ｄ），ＰＯ４－Ｐ不释放或微量释放。在微流水条件下ＮＨ＋４－Ｎ和ＰＯ４－Ｐ的释放速率显著提高，其平均值依次为８４．４ｍｇ／（ｍ２·ｄ）和４．３１ｍｇ／（ｍ２·ｄ）；（４）越冬中期底泥耗氧率平均为４３４Ｏ２ｍｇ／（ｍ２·ｄ）。研究表明在补施磷肥的情况下，适量的底泥对鱼类在进行生物增氧的越冬池中安全越冬是有好处的。     

天然气门站工艺管道安装技术

天然气门站通常有计量、加臭、过滤等功能,自动化程度较高。工艺流程中包括配管区、进站与出站阀门井等,其中不乏埋地敷管及地面工艺管道。以西安市天然气门站为例,天然气由进站经过三条汇管及其配备的仪表、设备、阀门后出站。文章叙述了门站工艺施工中的材料选用以及设备、管道安装、焊接、法兰安装、钢支座的施工与探伤、吹扫、强度试验、气密性试验、防腐等后续工序。     

Early Warning Fire Detection System Using a Probabilistic Neural Network

The Navy program, Damage Control-Automation for Reduced Manning is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desired to increase detection sensitivity and,more importantly increase the reliability of the detection system through improved nuisance alarm immunity. Improved reliability is needed, such that fire detection systems can automatically control fire suppression systems. The use of multi-criteria based detection technology continues to offer the most promising means to achieve both improved sensitivity to real fires,and reduced susceptibility to nuisance alarm sources. A multi-criteria early warning fire detection system, has been developed to provide reliable warning of actual fire conditions, in less time, with fewer nuisance alarms,than can be achieved with commercially available smoke detection systems. In this study a four-sensor array and a Probabilistic Neural Network have been used to produce an early warning fire detection system. A prototype early warning fire detector was built and tested in a shipboard environment. The current alarm algorithm resulted in better overall performance than the commercial smoke detectors, by providing both improved nuisance source immunity with generally equivalent or faster response times.     

基于RBF神经网络的边坡稳定可靠度分析

RBF神经网络具有网络训练速度快、可以避免局部极小等优点。文章基于RBF神经网络理论,采用蒙特卡罗模拟法来进行结构的可靠度分析,研究了样本点的生成方法及样本数对可靠度分析结果的影响,并将基于RBF神经网络的蒙特卡罗模拟法应用于边坡的可靠度分析。计算表明:基于RBF神经网络的蒙特卡罗模拟法具有较好的计算效率和计算精度;样本点的生成方法和样本数对计算结果影响较大。与随机取样法相比,均匀设计法生成的样本点分布更均匀,由此样本点集训练生成的神经网络能更好的代替原功能函数,在相同的样本数时具有更高的计算精度;当计算精度相同时,均匀设计法比随机取样法需要生成的样本点少,计算效率高。     

An Adaptive Wavelet Frame Neural Network Method for Efficient Reliability Analysis

Artificial neural networks (ANNs) method is widely used in reliability analysis. However, the performance of ANNs cannot be guaranteed due to the fitting problems because there is no efficient constructive method for choosing the structure and the learning parameters of the network. To mitigate these difficulties, this article presents a new adaptive wavelet frame neural network method for reliability analysis of structures. The new method uses the single‐scaling multidimensional wavelet frame as the activation function in the network to deal with the multidimensional problems in reliability analysis. Because the wavelet frame is highly redundant, the time–frequency localization and matching pursuit algorithm are respectively utilized to eliminate the superfluous wavelets, thus the obtained wavelet frame neural network can be implemented efficiently. Five examples are given to demonstrate the application and effectiveness of the proposed method. Comparisons of the new method and the classical radial basis function network method are made.     

城市燃气输配管网的可靠性评价

分析了管网失效的早期失效期、偶然故障期和耗损失效期的特点,探讨了偶然故障期管网系统的可靠性评价方法。     

Highway and Road Probabilistic Safety Assessment Based on Bayesian Network Models

A Bayesian network model is developed, in which all the items or safety related elements encountered when traveling along a highway or road, such as terrain, infrastructure, light signals, speed limit signs, intersections, roundabouts, curves, tunnels, viaducts, and any other safety relevant elements are reproduced. Since human error is the main cause of accidents, special attention is given to modeling the driver behavior variables (driver's tiredness and attention) and to how they evolve with time or travel length. The sets of conditional probabilities of variables given their parents, which permit to quantify the Bayesian network joint probability, are obtained and written as closed formulas, which allow us to identify the particular contribution of each variable to safety and facilitate the computer implementation of the proposed method. In particular, the probabilities of incidents affecting safety are calculated so that a probabilistic safety assessment of the road can be done and its most critical elements can be identified and sorted by importance. This permits the improvement of road safety making adequate corrections to save time and money in the maintenance program by concentrating on the most critical elements and effective investments. Some real examples of a Spanish highway and a conventional road are provided to illustrate the proposed methodology and show its advantages and performance.     

海外油气业务HSSE绩效考核体系建设与实践

基于相关系数的灰度匹配是以相关系数作为相似性测度的灰度匹配。相关系数匹配是众多匹配方法中最简单,速度最快也是最成熟的方法。本文介绍了相关系数影像匹配方法的原理、算法的设计与实现,利用核线影像对作为数据源,进行了影像匹配试验,提出了简单易行的分区匹配方法,并进行了比较分析,得出了有益的结论。     

城镇中低压钢质燃气管道腐蚀泄漏风险评估

城镇中低压埋地钢质燃气管道采用指标法(肯特法)进行风险评估研究较为合适。依次按照风险因素识别、评估单元划分、评估模型建立、评估准则确定、风险矩阵配套、管控措施选用等步骤,逐步开展全过程风险评估。在对管道腐蚀泄漏风险因素分析的基础上,通过指标打分法,从防腐层种类、防腐层性能、土壤电阻率、杂散电流、阴极保护、管道壁厚和运行年限7个方面构建失效可能性模型,从人员伤害及财产损失、供气中断居民用户影响、供气中断工商用户影响3个维度建立失效后果模型。按照风险值公式,计算出各评估单元风险值后,采用风险矩阵图,确定各评估单元的风险等级。按照风险分级管控原则,结合企业安全生产实际,根据每项风险因素,制定有针对性的管控措施。     

城市埋地燃气管网安全性模糊综合评价

根据模糊数学理论,建立城市埋地燃气管网安全性模糊综合评价体系,通过确定因素集、确定评价等级集、确定因素权重集、确定因素评价矩阵、模糊综合评价及评价指标处理等过程,得出具体分数,客观地评价城市埋地燃气管网的安全性。     

城市天然气管道占压隐患的模糊综合评价

通过对多种风险评价方法的比较,确定采用模糊综合评价法对城市天然气管道占压隐患进行风险评价。应用模糊数学方法建立了城市天然气管道占压隐患的模糊综合评价模型。结合实例,编制模糊综合评价软件对北京市城市天然气管道占压隐患进行评价。     

改进的模糊Modular神经网络在既有建筑可靠性鉴定中的应用

在Takagi-Sugeno模糊逻辑系统的基础上,提出了改进的模糊Modular神经网络模型(IFMNN),并将该模型应用于既有建筑的可靠性鉴定.改进的模型是将传统的模糊Modular神经网络模型中的单输出改进为多输出.这种改进的多输入多输出的模糊Modular神经网络模型具有预测性能好、训练学习速度快的优点,它的系统门网络采用模糊C均值聚类算法代替K-means算法,专家网络的训练中引进了先进的Levenberg-Marquardt算法.在应用改进的模糊Modular神经网络模型对既有建筑进行可靠性鉴定的过程中,综合考虑了各主要因素对既有建筑可靠性鉴定等级的影响,并将经量化处理的影响因素作为网络的外部输入,将网络计算得到的4个输出值分别作为样本对应于不同可靠性等级的隶属度,建筑可靠性鉴定的最终评判等级为最大隶属度所对应的等级.训练和预测样本的计算结果证明了改进的模糊Modular神经网络模型在既有建筑可靠性鉴定中应用的可行性和有效性.