基于BP神经网络的矿车运行时矿井摩擦阻力的预测*

为了快速有效地确定矿车等运输设备在巷道内运行时矿井摩擦阻力的变化情况,克服模拟软件计算量和现场实测工作量大的问题,以巷道风流速度、矿车运行速度、阻塞比、矿车长度4个矿车运行时巷道摩擦阻力的影响因素作为切入点,采用动网格技术模拟得到矿车在巷道内运行时有关矿井摩擦阻力的数据,以此为样本构建基于BP神经网络的矿井摩擦阻力预测模型,运用MATLAB软件进行网络训练,并将BP神经网络预测值与FLUENT模拟值进行对比。研究结果表明:BP神经网络结构比较简单,能以较快速度收敛,预测值与模拟值最大误差在7%以内,该神经网络模型用于求解矿车等运输设备在巷道内运行时摩擦阻力的变化情况是可行的。     

基于Logistic回归的燃气管网风险因素重要度分析方法研究*

为确定燃气管网风险评估的关键风险因素,以A省各地市燃气管网为研究对象,基于燃气专家经验确定燃气管线风险等级,提出基于Logistic回归的燃气管网风险因素重要度分析方法。采用样本增强及随机抽样的方式,选取400个均衡样本作为管网评估数据输入,通过因子分析方法对其进行降维,得到3个公共因子并作为一级指标反向构建燃气管网风险评估指标体系;利用有序多分类Logistic回归方法,根据回归系数绝对值大小对风险因素进行重要度排序。研究结果表明:外界环境对燃气管网风险的贡献程度相对较高,管道自身因素和巡检养护次之。研究结果可为城市燃气风险防控提供理论依据和方法参考。     

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.     

Propagation and intensity of blast wave from hydrogen pipe rupture due to internal detonation

The coupled fluid-structure-rupture model was developed to study the propagation and intensity of blast wave from hydrogen pipe rupture due to internal detonation. The dynamic rupture of pipe and propagation of blast wave were well coupled together in every timestep during the simulation. The numerical model was validated with experiments in terms of both typical rupture profiles and blast overpressures. Results reveal that crack branching of pipe can dramatically increase the rupture opening rate which controls the intensity and shape of the resultant blast wave. Due to the process of crack initiation and extension, the blast wave out of the pipe first forms and then is strengthened by the subsequent compression waves. This makes the maximum peak overpressure appears at a certain standoff distance above the rupture. Despite consuming some percentages of energy, the dynamic rupture of pipe generally presents positive effects (up to 2–3 times) on the blast wave intensity along the jetting direction due to the convergence effect of rupture opening on the release of internal high-pressure gas. Finally, through defining normalized overpressure and impulse based on the same hydrogen detonation in open spaces, the quantitative influences of pipe rupture on the blast wave intensity in cases of different detonation pressures and standoff distances are clarified.     

Dynamic risk assessment for underground gas storage facilities based on Bayesian network

Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage process safety.     

基于MATLAB的BP神经网络模型在漠阳江水质评价中的应用研究

为提高水质评价的准确性,基于MATLAB及人工神经网络理论,采用误差反向传播的BP算法建立漠阳江水质评价模型,充分利用神经网络的非线性映射特性,取7项常规地表水水质评价指标对漠阳江水质进行评价,并将BP神经网络评价结果与单因子评价法及综合指数法的评价结果进行比较,网络运行结果表明一致效果良好.同时较传统的水质评价方法,该网络具有较高的识别精度,提高了水质评价等级的准确性,使评价的结果更具有科学性.     

基于百度指数的长江中游城市群城市网络特征研究

随着地理研究迈入大数据时代,运用互联网展开城市网络结构研究逐渐成为经理地理研究的新思路。以长江中游城市群为研究对象,借助百度指数,获取2011年~2014年百度用户关注度,构建百度指数城市信息流网络,从大城市群视角和所辖三大子城市群视角,分别探讨城市网络格局的时空变化。研究发现:长江中游城市群在一体化进程中城市等级日益明晰,差距拉大。三大子城市群表现出极化效应与扩散效应并存,区域非均衡性突显;互联网的普及在一定程度上重塑和改造着城市群,但非完全颠覆传统城市网络格局,地理区位对城市网络格局和城市影响力的影响仍不容忽视。     

BP 神经网络模型在环境气象灾害预测中的应用研究--以湖南省为例

依据环境气象数据与自然灾害统计数据,建立BP神经网络模型,对湖南主要气象灾害（洪灾、旱灾、冰冻灾）及受灾经济损失进行实例预测,将在MATLAB7软件中的仿真结果与传统的多元线性回归模型分析结果进行比较和误差分析。结果表明,BP神经网络模型在洪灾、旱灾受灾率方面的预测效果和精度优于多元回归模型,而由于冰灾训练样本不足及经济损失与输入因子的线性相关程度高,在冰灾与受灾经济损失率方面稍逊于多元回归模型。     

基于BP神经网络优化制备Cu-Ce/TiO_2及其光催化活性研究

采用Cu和Ce对TiO2进行改性,基于正交实验设计和BP神经网络研究了Cu-Ce/TiO2中Cu-Ce对TiO2的摩尔百分数、Cu-Ce/TiO2中CuCe摩尔比及Cu-Ce/TiO2烧结温度对Cu-Ce/TiO2光催化降解甲醛溶液性能的影响.同时,对Cu-Ce/TiO2制备方案进行了优化,并运用X射线衍射仪、扫描电子显微镜和紫外-可见分光光度计对最佳条件下制备的Cu-Ce/TiO2进行表征.结果表明,优化的制备条件为Cu-Ce/TiO2中Cu-Ce对TiO2的摩尔百分数为2.88%,Cu-Ce/TiO2中Cu-Ce的摩尔比为1∶1,Cu-Ce/TiO2的烧结温度为570℃.共掺杂Cu离子和Ce离子能有效避免掺杂TiO2晶格内部表层和近表层产生较多的位错,从而抑制晶格畸变增大;诱导TiO2中锐钛矿型晶体向金红石型晶体转变的能力增强,有效抑制电子-空穴对的复合,产生介电局域效应.     

基于前向神经网络的广义环境系统评价普适模型

为了建立由水环境、空气环境、生态环境、水资源环境、灾害环境、遥感环境、社会经济环境等不同环境系统组成的广义环境系统评价都能普适、通用的神经网络模型,针对BP神经网络因收敛速度慢、易于陷入局部极值而使实用性受限的缺陷,提出以双极性sigmoid函数作为网络隐层节点(神经元)的激活函数,而网络输出为所有隐层节点输出的线性求和的前向神经网络的广义环境系统评价模型.在设置广义环境系统指标参照值和指标值规范变换式,并对指标值进行规范变换的基础上,分别构建了适用于广义环境系统评价的任意2个指标规范值的前向神经网模型(NV-FNN(2)结构)和任意3个指标规范值的前向神经网模型(NV-FNN(3)结构).而对于指标较多的广义环境系统评价,只要将多指标分解为以上2个指标和3个指标的两种简单结构的前向神经网络的广义环境系统评价模型的组合表示即可.理论分析和实例检验结果表明:该模型对任意广义环境系统的规范指标值皆普适、通用,因而使不同环境系统的评价变得简洁、统一.规范变换和优化算法相结合的建模思想和方法对简化广义环境系统评价的多元回归、投影寻踪回归、回归支持向量机和径向基神经网络建模亦有借鉴和启迪作用.