应用ANFIS预测蠕虫反应器的污泥减量速率并优化其运行条件

利用水生蠕虫的捕食作用可以有效地实现污泥减量.为了研究环境条件波动对蠕虫捕食污泥减量效率的影响,应用自适应神经模糊推理系统(ANFIS)和人工神经网络(ANN)模型分别预测蠕虫反应器的污泥减量速率.结果表明,溶解氧浓度(D0)、温度(T)、蠕虫密度和污泥负荷是蠕虫捕食过程的主要影响因素,通过性能比较得出ANFIS模型预测值与实验测定值间具有更好的一致性,其相关系数(r)为0.82,绝对平均误差百分比(MAPE)为71.5％,均方根误差(RMSE)为16.7.根据ANFIS模型的预测结果,得出蠕虫反应器的最适运行条件为:DO 1.8 ～3.1mg·L-1,温度18.4～21.7℃,蠕虫密度低于1.7 g·cm-2(以湿重计),污泥负荷563～734 mg·g-1(以TSS计),在此操作条件下获得的污泥减量速率均高于100 mg· g-1·d-1.     

Application of social network theory to prioritizing Oil & Gas industries protection in a networked critical infrastructure system

As a typical process industry, the Oil & Gas industries play a key role within a networked critical infrastructure system in terms of their interconnection and interdependency. While the tight coupling of infrastructures increases the efficiency of infrastructure operations, interdependency between infrastructures may cause cascading failure of infrastructures. The interdependency between critical infrastructures gives rise to an infrastructure network. In this paper, we apply social network analysis, an analytical tool used by social scientists, to study human interactions and to analyze characteristics of the critical infrastructure network. We identify Oil & Gas, Information & Communication Technologies (ICT), and Electricity as three infrastructures that are most relied upon by other infrastructures, thus these may cause the greatest cascading failure of the infrastructures. Among the three, we further determine that Oil & Gas and Electricity are the more vulnerable infrastructures. As a result, priority toward critical infrastructure protection should be given to the Oil & Gas and Electricity infrastructures since they are most relied upon but at the same time depend more on other infrastructures.     

基于BP神经网络的矿井火灾安全评价方法

在对国内煤矿调研的基础上,建立了包括内因火灾、外因火灾、管理和救灾3个子系统的评价指标体系;通过模糊层次法,求解了各指标权重;结 合目前比较成熟的BP神经网络方法,研究了一种新的矿井火灾安全评价方法,并编制了界面友好的软件,在矿井实际应用中取得较为满意的评价结果.     

基于概率神经网络的液氨汽车罐车复合故障诊断

故障诊断在保证危险化学品汽车罐车运输安全方面具有重要意义。从国内交通运输安全的实际要求出发,依据液氨汽车罐车的结构特点及国家法律法规的要求,比较全面、系统地分析了液氨汽车罐车故障特征的相关参数,并将其作为概率神经网络的输入结点。根据实际可能发生的故障分类模式,考虑到故障诊断的容错能力和自适应能力,提出了基于概率神经网络的复合故障诊断模型。利用指标参数作为网络训练样本,对未知故障模式进行诊断,并以广西地区压力容器检验所液氨检测数据为例进行说明。理论分析和实例计算表明,该模型物理概念清晰,计算结果合理,精度较高,在危险化学品汽车罐车故障诊断中有很好的适用性。该项工作可为我国危险化学品汽车罐车故障智能诊断的深入开展提供参考依据。     

南京城市轨道交通网络化运营安全管理对策研究

城市轨道交通网络化运营在更加显现便捷性,导致客流大幅度增加的同时,也给运营安全带来新的严峻挑战。本文分析了网络化运营给安全管理带来的新矛盾和新问题,研究提出了相关对策并在实践中应用,确保了南京城市轨道交通网络化运营安全生产总体形势持续平稳。     

移动应急友邻互视技术

针对应急管理中对信息共享、团队协作和移动指挥的应用需求,研究了移动应急终端间的位置共享与信息交互方法和技术框架,初步构建了移动应急位置服务网。移动应急位置服务网包括移动应急终端、无线通信网络、中心服务器、监控终端和管理终端等组成部分。定义了位置、信息与指令传输格式,以实现移动应急终端和中心服务器之间的实时通信。制定了移动应急终端组网规则,以实现位置和信息在移动应急位置服务网内的交互。研发了友邻位置与信息交互技术,重点研究了终端位置报告、终端节电、北斗终端接入、友邻位置表达、信息推送等技术。针对具体应用需求,开发了原型系统,试验表明移动应急友邻互视技术可提高事发现场应急处置团队的协作能力和应急处置效率,具有实用性。     

Incorporation of neural network analysis into a technique for automatically sorting lightweight metal scrap generated by ELV shredder facilities

In an attempt to improve the technique of automatic sorting of lightweight metal scrap by sensing apparent density and three-dimensional shape, realized by the combination of a three-dimensional (3D) imaging camera and a meter to weigh a moving object on a conveyor belt, neural network analysis was integrated into the scrap identification algorithm, and its effect on the sorting accuracy of this technique was examined using approximately 1750 pieces of scrap sampled at three different end-of-life vehicle (ELV) shredder facilities. As a result, the newly developed algorithm, in which an unknown fragment is identified by passing through two discriminant analyses and one neural network analysis, was demonstrated to greatly decrease the time required for data analysis to prepare the identification algorithm without reducing the sorting accuracy. The average sorting accuracy for a mixture of three types of lightweight metal fragments was found to be 85%, based on the fact that the fist-sized fragments of cast aluminum, wrought aluminum, and magnesium sampled at the three ELV shredder facilities had similar apparent densities and 3D shapes. It was also suggested that still higher sorting performance is possible by repeating the procedure of modifying the database and re-learning of the neural network in the identification algorithm.     

基于AE的燃气管网事故分析模块设计与实现

随着城市燃气管网的飞速发展,在役燃气管道的安全问题日益突出,针对燃气事故致因及后果,基于ArcGIS网络模型,运用GIS网络拓扑分析、空间数据库等技术,设计利用管网网络模型实现管网事故分析方法,并结合C#+ArcEngine编程技术实现燃气管网事故分析模块,该模块实现爆管分析、燃气泄漏扩散范围分析、最优路径分析、连通性...     

基于贝叶斯网络的尾流事故机理分析

针对事故树分析法的局限性,在尾流事故树的基础上,建立贝叶斯网络(BN)。运用推理运算对BN进行定量分析,得出:空中交通密度太大、空中交通管制(ATC)间隔判断错误和短期冲突告警(STCA)被忽略是事故的关键致因。将针对致因提出的改进措施引入到BN中,评价相关措施的有效性。应用BN进行尾流事故的机理分析,能够以比逻辑门更好的形式表达变量间的不确定性关系,从而更加方便地找到导致事故发生的关键因素。