A bayesian network-based safety assessment method for solid propellant granule-casting molding process

The safety of the solid propellant molding process is vital for the stable production of high-quality propellants. Failure events caused by abnormal parameters in the molding process may have catastrophic consequences. In this paper, a Bayesian network (BN) model is proposed to assess the safety of the solid propellant granule-casting molding process. Fault tree analysis (FTA) is developed to construct a causal link between process variables and process failures. Subsequently, expert experience and fuzzy set theory (FST) are used to obtain failure probabilities of the basic events (BEs). Based on the mapping rules, FTA provides BN with reliable prior knowledge and a network structure with interpretability. Finally, when new evidence is obtained, the probability is updated with the diagnostic reasoning capability of BN. The results of the sensitivity analysis and diagnostic inference were combined to identify key parameters in the granule-casting molding process, including curing temperature, vacuum degree, extrusion, calendering roll distance, length setting value, holding time, and polish time. The results of this paper can provide effective supporting information for managers to conduct process safety analysis.     

基于视频分析的空管员违规行为识别方法*

为利用视频数据对空管员违规行为进行智能化分析,降低不安全事件发生率,提出2阶段的违规行为识别模型（AR-ResNeXt）,基于实地调研构建空管员视频数据集,利用最小化动态多实例学习损失函数和中心损失函数,获得违规行为检测的判别特征表示,结合异常回归网络和ResNeXt网络,完成对空管员违规行为的时序区间检测与动作分类。研究结果表明:AR-ResNeXt模型在自制数据集中,其帧级AUC达到82.9%,分类准确率达到87.8%,可准确识别空管员发生违规行为的时序区间并进行分类,研究结果可为保障空中交通安全奠定基础。     

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.     

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.     

新乡市工业“三废”污染及治理措施

以新乡市2006—2012年工业"三废"排放监测数据为依据,通过对城市废水、废气及工业固体废弃物排放情况的分析,找出主要污染特征并提出相关治理对策。     

循环经济工业园区规划环评研究

概括了循环经济工业园区规划环境影响评价的框架思路。归纳总结了规划环评的主要内容,并以山西省长治市襄垣富阳循环经济工业园区为例,对规划环评指标体系的构建进行探讨。     

电化学法处理工业废水的现状与发展研究

随着工业的快速发展,如何对工业废水进行高效处理是我们需要解决的问题。电化学方法反应装置简单,反应效率高,不会产生二次污染。对中国工业废水目前常用的电化学处理技术的进展和应用进行了综述,包括电化学氧化、电化学还原、电絮凝、电渗析等内容,对国内外的研究成果的总结可知,电化学法对工业废水的处理效率较高,有一定的应用前景。同时对电化学法将来的重点研究方向提出了展望。     

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.