浅议管道第三方破坏因素及预控措施

目前我国油气管道里程超过10万公里,形成连接东西、横穿南北、遍布全国的地下能源大动脉。虽然管道输送油气优势突出,但其存在的安全风险也特别大,仅第三方破坏在我国油气管道事故中就占40％左右的比例,所以防止第三方破坏管道的任务比较严竣。本文重点阐述了目前油气管道第三方破坏的主要因素及采取的预控措施。     

输油气管道安全技术探讨

管道输送是油气运输中最便捷、经济和可靠的方式。随着经济的发展、管道的大量敷设和长期运行,管道事故时有发生。管道的安全技术愈发引起人们的重视。     

基于支持向量机的中国石油安全分析

由于中国未来的石油需求对中国石油安全来说至关重要,而建立起石油安全的评价体系对中国的石油安全极为关键。结合美国、日本、俄罗斯、德国、法国、韩国等6个国家石油方面相关数据及安全类别,按重要性筛选出代表性的核心指标,以支持向量机理论为基础建立了石油安全的评价体系,力图达到定量化评价中国石油安全的目的。     

城市地下燃气管道安全技术研究

随着环境问题以及能源安全越来越被重视,作为清洁能源的城市燃气被越来越广泛使用,而且天然气易爆、易燃,如果发生意外事故,居民的财产甚至生命都会受到威胁,因此进行城市地下燃气管道安全的影响、破坏因素探究,对于保证地下燃气管道的安全使用、维持社会稳定具有重要意义。     

基于地震动信号的管道安全监测系统的研究

针对目前广泛存在的油气管道周边安全问题,提出基于地震波信号检测和处理的管道安全监测方法．地震检波器组及处理单元组成的检测系统采集管道周围地面入侵目标在活动时产生的地震动信号;由于不同目标产生地震波信号的频率、能量等特征不同,通过小波变换的时频分析方法处理这些信号,可以获得不同传感器测量信号中入侵目标特征频率出现的时间差异;根据时间差和传感器阵列与入侵目标的相对位置,实现对入侵目标的空间定位．通过对实验外场实测数据的分析验证了本文提出的方法．     

关于油气管道安全管理的问题探讨

我国油气管道建设正处于蓬勃发展的阶段,但同时也面临着新老管道交替,处于事故多发期的严峻挑战。近年发生的多次管道事故暴露出国内在完整性管理、应急响应和监管体制等方面有待提高。本文主要是针对日常工作讲述了一些安全性的问题,对具有相似工作的人员只具有理论意义,还需要在实际工作中进一步检验。     

油气管道长输安全管理运行探讨

市场经济的飞速发展令我国各项产值迅猛增长,同时对能源的需求也日益庞大。本文就油气能源管道长输过程中如何实旌安全运行管理展开探讨,对确保能源供应的及时高效、科学有序,有积极有效的促进作用。     

基于归一化小波能量系数的管道安全识别算法

针对管道泄漏检测系统存在一定误报率的问题,提出一种基于归一化小波能量系数的油气管道安全识别算法．首先提出最佳母小波选择标准,以现场数据为基础选取最佳母小波,然后提取归一化小波能量系数的最大值作为特征量,最后利用Fisher分类器对压力数据进行识别,判断管道是否处于安全状况．利用现场数据对该方法进行验证分析,结果表明该算法识别正确率较高,实用性好,且可以降低误报率．     

长输油气管道安全运行技术管理探讨

管道输送是油气运输中最便捷、经济和可靠的方式。随着经济的发展、管道的大量敷设和长期运行,管道事故时有发生。管道的安全技术愈发引起人们的重视。本文从几个方面着手,给出了一系列强化长输油气管道安全运行管理工作的有效举措,以求为长输油气管道的安全运行管理提供必要的借鉴与参考。     

美国油气管道安全管理经验及启示

美国拥有全世界最多的油气管道,根据美国中央情报局《世界各国纪实年鉴》的统计,2013年全球在用油气管道共3559186公里,美国达2225032公里,占世界总长度的62.5%,可绕地球赤道55圈。由于内部和外部因素作用,管道在运行过程中时常会发生泄漏事故。美国历史上也发生过严重的管道事故,比如2000年8月19日,美国新墨西哥州一条天然气管道泄漏爆炸,导致12人死亡。根据美国管道办公室（OPS）公布数据,从1993年到2012年的20年间,美国共发生管道泄漏事故10443起,导致死亡377人、受伤1489人,经济损失59.76亿美元,油品泄漏总量达230万桶。2003年至2012年比上个10年的管道事故数量增长了55%,但是事故导致的人员伤亡和财产损失却呈明显下降趋势,这主要得益于管道管理和技术水平的提高。     

Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks

This paper presents the work carried out towards developing a diagnostic system for the identification of accident scenarios in 220 MWe Indian PHWRs. The objective of this study is to develop a methodology based on artificial neural networks (ANNs), which assists in identifying a transient quickly and suggests the operator to initiate the corrective actions during abnormal operations of the reactor. An operator support system, known as symptom-based diagnostic system (SBDS), has been developed using ANN that diagnoses the transients based on reactor process parameters, and continuously displays the status of the reactor. As a pilot study, the large break loss of coolant accident (LOCA) with and without the emergency core cooling system (ECCS) in reactor headers has been considered. Several break scenarios of large break LOCA have been analyzed. The time-dependent transient data have been generated using the RELAP5 thermal hydraulic code assuming an equilibrium core, which conforms to a realistic estimation. The diagnostic results obtained from the ANN study are satisfactory. These results have been incorporated in the SBDS software for operator assistance. A few important outputs of the SBDS have been discussed in this paper.     

Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0

Additive Manufacturing (AM) requires integrated networking, embedded controls and cloud computing technologies to increase their efficiency and resource utilisation. However, currently there is no readily applicable system that can be used for cloud-based AM. The objective of this research is to develop a framework for designing a cyber additive manufacturing system that integrates an expert system with Internet of Things (IoT). An Artificial Neural Network (ANN) based expert system was implemented to classify input part designs based on CAD data and user inputs. Three ANN algorithms were trained on a knowledge base to identify optimal AM processes for different part designs. A two-stage model was used to enhance the prediction accuracy above 90% by increasing the number of input factors and datasets. A cyber interface was developed to query AM machine availability and resource capability using a Node-RED IoT device simulator. The dynamic AM machine identification system developed using an application programme interface (API) that integrates inputs from the smart algorithm and IoT interface for real-time predictions. This research establishes a foundation for the development of a cyber additive design for manufacturing system which can dynamically allocate digital designs to different AM techniques over the cyber network.     

Symptom based diagnostic system for nuclear power plant operations using artificial neural networks

Nuclear power plant experiences a number of transients during its operations. These transients may be due to equipment failure, malfunctioning of process systems and unavailability of safety systems. In such a situation, the plant may result into an abnormal state which is undesired. In case of an undesired plant condition generally known as an initiating event (IE), the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the IEs at the earliest stages of their developments. These abnormal plant conditions must be diagnosed and identified through the process instrument readings. A symptom based diagnostic system has been developed to investigate the IEs. The event identification is carried out by using resilient back propagation neural network algorithm. Whenever an event is detected, the system will display the necessary operator actions in addition to the type of IE. The system will also show the graphical trend of relevant parameters. The developed system is able to identify the eight IEs of Narora Atomic Power Station. This paper describes the features of the diagnostic system taking one of the IEs as a case study.     

基于BP神经网络的舰炮武器系统综合保障性能评价

通过对舰炮武器系统的分析，建立舰炮武器系统综合保障性能评价的指标体系。把神经网络的相关知识应用到舰炮武器系统综合保障性能评价中，得到了相应的BP神经网络评价模型。通过一些舰炮武器系统的实例计算，验证了模型的正确性，为舰炮武器系统的研制和改进提供了一定的参考依据。     

An artificial neural network-based expert system for the appraisal of two-car crash accidents

This paper employs artificial neural network (ANN) to develop an accident appraisal expert system. Two ANN models-- party-based and case-based-- with different hidden neurons are trained and validated by k-fold (k=3) cross validation method. A total of 537 two-car crash accidents (1074 parties involved) are randomly and equally divided into three subsets. For the comparison, a discrimination analysis (DA) model is also calibrated. The results show that the ANN model can achieve a high correctness rate of 85.72% in training and 77.91% in validation and a low Schwarz's Bayesian information criterion (SBC) of -0.82 in training and 0.13 in validation, which indicates that the ANN model is suitable for accident appraisal. Furthermore, in order to measure the importance of each explanatory variable, a general influence (GI) index is computed based on the trained weights of ANN. It is found that the most influential variable is right-of-way, followed by location and alcoholic use. This finding concurs with the prior knowledge in accident appraisal. Thus, for the fair assessment of accident liabilities the correctness of these three key variables is of critical importance to police investigation reports.     

Smart connected electronic gastroscope system for gastric cancer screening using multi-column convolutional neural networks

Gastroscopy is a widely adopted method for gastric cancer screening and early diagnosis. Clinical studies show that it can effectively prolong patient life and maximise therapeutic effect. However, it is difficult for doctors to identify and detect lesions in real time, which manifests as the major challenge in gastroscopy. In this paper, we propose SCEG, a smart connected electronic gastroscopy system that performs dynamic cancer screening in gastroscopy. By integrating electronic gastroscopy with cloud-based medical image analysis service, we develop an AdaBoost-based multi-column convolutional neural network (MCNN) for enhancing gastric cancer screening. Experimental results show that the proposed MCNN approach significantly outperforms other competing approaches.     

Modelling of a thermal insulation system based on the coldest temperature conditions by using artificial neural networks to determine performance of building for wall types in Turkey

In formation of building external envelope, as two important criteria, climatic data and wall types must be taken into consideration. In the selection of wall type, the thickness of thermal insulation layer (d_i) must be calculated. As a new approach, this study proposes determining the thermal insulation layer by using artificial neural network (ANN) technique. In this technique five different wall types in four different climatic regions in Turkey have been selected. The ANN was trained and tested by using MATLAB toolbox on a personal computer. As ANN input parameters, U_w, T_e,Met, T_e,TSE, R_wt, and q_TSE were used, while d_i was the output parameter. It was found that the maximum mean absolute percentage error (MRE, %) is less than 7.658%. R² (%) for the training data were found ranging about from 99.68 to 99.98 and R² for the testing data varied between 97.55 and 99.96. These results show that ANN model can be used as a reliable modeling method of d_i studies.