基于防突工作流程的突出预测敏感指标体系构建方法

针对现有突出预测敏感指标确定方法存在的问题,基于瓦斯压力和瓦斯含量的联系,考察了临界压力和含量的对应关系,确定了瓦斯压力为杨柳煤矿10#煤层区域预测敏感指标,临界值为0.74MPa;采用实验室分析和现场验证相结合的手段,通过引入相对误差和迫近度等分析方法,确立了钻屑解吸指标Δh2为10#煤层局部预测敏感指标,临界值为180 Pa;结合我国目前防突工作基本流程,确定了突出预测敏感指标体系的构建方法,构建了杨柳煤矿10#煤层突出预测敏感指标体系。     

大水矿山DGME系统动态随机模型及风险预测

大水矿山由于含水层通常难以疏干,矿山生产常采取边疏干含水层边开采的带压开采方式进行,此类矿山实际开采环境是由排水系统及地下水系统组成,即排水-地下水的开采环境(DrainingGroundwater Mining Environment,DGME)系统。为此,根据系统论的原理,构建了大水矿山排水-地下水开采环境系统动态随机模型,并以模型为基础分析了系统水位随机变化的影响因素。结果表明:1)DGME系统动态随机模型可反映出矿山地下水系统与排水系统之间的相互关系;2)矿山地下水位变化是Wiener过程,臼水位分布符合正态分布,其数字特征取决于系统输入输出流量差的均值及方差;3)系统当前风险状态受短期内系统水位变化影响,超出一定时域,系统风险状态不可预知。     

供电作业人身事故危害多元线性回归预测

为了定量评估电网作业和运行的风险,提出了一种基于5M因素分析法和多元线性回归的供电作业人身事故危害预测方法。借鉴安全行为学、安全心理学和事故致因理论,首先将供电作业中影响人身安全的因素分成5个大类,即人、设备、材料、方法和环境。然后分析所有因素间的相关性,将相关性较强的影响因素中对人身事故危害程度弱的一方剔除,并采用南方某电网公司人身伤亡事故数据为例进行分析,删除了两个人身安全影响因素:工器具配置和作业性质。继而,采用多元线性回归法建立供电作业人身事故危害值与各人身安全影响因素间的关系,对供电作业人身事故危害值进行预测。最后,以南方该电网公司2003~2013年发生的64起供电作业事故验证所提方法的有效性和准确性。     

GM（1， 1）正弦模型修补气温监测缺失数据的探讨

目的建立预测精度较好的大气温度监测缺失数据修补的方法和模型。方法采用正弦函数对GM(1,1)标准模型进行修正,建立分段的GM(1,1)正弦修正模型对缺失数据进行修补。以万宁试验站某天监测的温度日记时值数据为试验数据,同时建立GM(1,1)标准模型、GM(1,1)时序修正模型和GM(1,1)正弦修正模型,对比分析各模型的修补误差,确立较好的修补模型。结果从模型的拟合效果分析,GM(1,1)标准模型和GM(1,1)正弦修正模型的拟合性最好,GM(1,1)时序修正模型的拟合性相对较差一些;从预测精度上分析,GM(1,1)标准模型和GM(1,1)时序修正模型预测修补效果差,平均相对误差分别达到22.54%和17.70%,而GM(1,1)正弦修正模型预测修补的平均误差仅为3.14%,得到了较大的改进,预测效果好。结论正弦修正模型能很好地修补缺失数据,其修补效果比时序修正模型和标准模型都要好。     

多轴蠕变-疲劳寿命预测系统软件开发

目的开发多轴-蠕变疲劳寿命预测系统软件。方法采用Visual Studio 2010,编程语言用C++/CLI,Win Form开发方式,通过编制三维载荷下的临界面确定,多轴循环计数,多轴蠕变损伤,多轴疲劳损伤,蠕变-疲劳交互作用计算等各种程序,开发高温环境下的多轴蠕变-疲劳寿命预测系统软件。结果所开发的专用寿命预测软件,程序运行环境为Windows操作系统,需要NET4.0环境支持。数据采用Micro Soft Access2010,数据库管理采用了ADO.NET技术。结论软件系统适用于随机多轴载荷下高温环境中热端机械零部件的损伤状态评估和寿命预测。     

A hybrid machine learning model for predicting crater width formed by explosions of natural gas pipelines

Despite the existence of industry models for estimating the crater width formed by the explosion of natural gas pipelines, their applicability is still limited since the complex formation mechanisms. In this work, a novel hybrid model was developed to predict crater width formed by explosions of natural gas pipelines, using artificial neural networks (ANN) as the fundamental predictor. Based on the historical accident records, the proposed hybrid model was trained by the pipeline parameter, the operating condition, the installation parameter, and the crater width. A novel nature-inspired optimization algorithm, i.e., the Lévy-Weighted Quantum particle swarm optimization (LWQPSO) algorithm, was proposed to optimize the ANN model's parameters. Three machine learning models were developed for comparative reasons to predict the crater width. The use of precision and error analysis indicators assesses prediction performance. The results show that the proposed hybrid model (LWQPSO-ANN) has high prediction accuracy and stability, which outperforms QPSO-ANN-based benchmark hybrid models and the model without an optimizer (Support Vector Machine, SVM). The parameter sensitivities of the proposed algorithm, including the maximum number of iterations, population size and contraction-expansion coefficient, were determined. The proposed hybrid model is expected to support the quantitative risk assessment (QRA), Right-of-Way (ROW) definition and the inherently safer design of the underground parallel pipelines.     

酒店通风系统噪声治理及降噪效果模拟预测

本文结合某酒店通风系统噪声治理实例,根据进、排风口空间分布,气流噪声频谱特性等参数,采用Cadna/A软件对噪声源进行了建模,预测了不同楼层进、排风口安装具有不同消声量的消声器后关心点处降噪量。结果表明,酒店通风系统室外进、排风口消声器的消声量可根据各风口噪声对关心点的噪声贡献值大小合理确定消声量,在此基础上根据进、排风口气流噪声频谱等参数设计消声器;只要声学建模合理(特别是声源声功率级等参数取值),借助Cadna/A可较为有效地进行声源识别,确定各进、排风口对各关心点的噪声贡献值大小。     

基于GF-LSTM和GAN网络的小样本集人工水体溶解氧浓度预测

采用推流方式改善人工水体溶解氧分布不均衡以防止富营养化时,需要对其分布进行预测来提高推流效率,为此构建了基于生成式对抗网络(GAN,Generative Adversarial Networks)和长短期记忆神经网络(LSTM,Long-Short Term Memory Network)的溶解氧浓度预测模型。以广西大学镜湖35 m2的一片水体区域为研究对象,采用不同电压直流水泵推流,用无人船搭载在线检测仪在一段时间内定时定点采集水体溶解氧浓度数据作为原始数据样本,并采用GAN扩充数据样本。利用遗传算法和改进的一阶滤波算法进行溶解氧的噪声数据处理,结合LSTM网络构建溶解氧浓度预测模型GF-LSTM(Genetic And Filtering Algorithm-Long Short Term Memory Network)。结果表明:相比常用的BP网络,GF-LSTM网络预测的平均误差降低了62%,均方误差降低了75%;相比传统的LSTM网络,GF-LSTM网络预测的平均误差降低了22%,均方误差降低了50%。     

煤体电荷感应信号评价冲击危险性试验研究

为完善冲击地压矿井的冲击危险性评价方法,提高冲击危险性预测的准确率,应用自主研制的煤岩电荷监测系统,选择典型冲击地压矿井的煤样,开展了煤体单轴压缩冲击危险性测试与电荷感应监测试验研究。基于冲击地压扰动响应失稳理论,将应变软化阶段产生的电荷信号变化作为预测冲击地压发生的前兆信息,得到了煤体冲击危险性指标的临界软化系数Kρ、临界应力系数Kp及其冲击危险的等级分类标准,分析了煤体电荷感应信号的电荷事件数CSJ和电荷事件的平均幅值CFZ参量与冲击危险性指标Kρ和Kp之间的量化关系。结果表明:煤样破裂应力峰后,冲击倾向性K与电荷事件数CSJ呈指数递减关系,与CFZ呈指数递增关系;随着Kρ或Kp的增大,煤样应力峰后CSJ呈幂函数关系递增,应力峰后电荷平均幅值CFZ呈一次函数关系递减,以此可预测煤体的冲击危险程度,最后得到了河南某矿煤层冲击危险性的应力峰后冲击危险等级的CSJ和CFZ判据与划分标准。煤体冲击危险性电荷感应信号评价方法为冲击地压矿井冲击危险性评价提供了一种新的方向,对现场煤层冲击危险性评价具有指导作用,但也还需要开展大量现场试验对其进行不断修正和完善。     

Evaluation on consequences prediction of fire accident in emergency processes for oil-gas storage and transportation by scenario deduction

This paper takes the safety in emergency processes as the starting point, from the perspective of scenario deduction, to study the consequences of fire accidents for oil-gas storage and transportation. Through the statistical analysis of actual accident cases, 19 frequently occurring basic scenarios in emergency processes are summarized. The scenario evolution paths of fire accidents for oil-gas storage and transportation are given by analyzing the evolution law of the accident development. Fuzzy numbers are introduced to express experts' qualitative judgment on accident scenarios. The empirical probabilities of scenario nodes are obtained by defuzzification calculation, and the state probability of each scenario node is calculated by using the dynamic Bayesian network joint probability formula. Under the comprehensive consideration about the probability statistics of actual accident cases, the critical scenario nodes on the evolution path and their final scenario probabilities are jointly determined to realize the optimization of the scenario evolution path. By constructing the correlation between the optimized scenario evolution path and the accident consequences, an accident consequence prediction model is established. The occurrence probability of accident consequences is calculated by the defuzzification method and dynamic Bayesian network. The accuracy of the consequence prediction model is verified by the July 16 Dalian's Xingang Harbor oil pipeline explosion accident. The research results provide scientific basis for helping decision makers to make the effective emergency measures that are most conducive to the rapid elimination of accidents and reducing the severity of accident consequences.