大数据解析技术在大气环境监测中的应用研究

针对近年来新兴的大数据及挖掘、分析技术,对大数据解析技术在环境科学研究中的应用进行了分析和探析。以城市局部地区大气细颗粒污染物PM2.5浓度计算为例,筛选了PM2.5浓度历史数据、气象条件、交通状况、人群活动情况、网格道路状况等数据为影响特征量,分析了用神经网络法进行大数据解析和推演的原理和数学过程,是用数学、计算机、统计等方法研究解决多元、非线性复杂环境问题的一种新的探索。     

京津冀地区一次污染过程的星载地基激光雷达联合观测分析

针对2018年3月9—15日京津冀地区的一次空气重污染过程,进行了基于地基颗粒物激光雷达组网的星载-地基联合观测分析。颗粒物激光雷达观测到污染前期为局地污染累积过程,中期有明显的污染物区域传输过程,北京受太行山沿线城市污染输送影响较大。风廓线激光雷达观测结果表明:此次污染过程近地面主要为偏南风且风力较弱,冷空气到来时风向转为较强东北风,导致污染消散。微波辐射计观测到保定在污染过程中出现持续6 d的逆温层,同时在污染过程中近地面相对湿度较高,逆温层被打破后污染开始消散。在污染过程的各个阶段中,污染团的空间分布与变化特征均被很好地反映出来,可见地天联合观测对污染物的累积与输送研究有较大的意义,能对京津冀及周边地区的大气污染联防联控提供有力支持。     

走滑断层作用下X80管道设计应变规律和神经网络预测

走滑断层是埋地管道常见的地质灾害威胁，断层作用下管道会发生较大的拉压应变而失效。为得到X80管道的设计应变，基于有限元方法建立了走滑断层作用下管道的应变响应数值计算模型，模型使用壳单元模拟管道，非线性弹簧单元模拟土壤约束，采用西二线实际工程的管道应变影响参数范围，计算了管道的设计应变；为预测管道的设计应变值，基于以上参数化分析得到的4 817组设计应变结果，采用人工神经网络建立了管道设计应变预测模型。结果表明:该神经网络模型预测结果的最大相对误差小于10%，预测准确性良好，且该方法具有较高的计算效率，可以为断层作用下埋地管道的应变设计与评估提供参考。     

供水管网震后流量监测点的动态分级优化布局研究

为了提高管网地震监测点布局的准确性和合理性，基于管网微观水力计算模型和动态分级法，提出供水管网震后流量监测点的动态分级优化布局模型。首先，利用管网微观水力计算模型计算管段流量的影响系数，构建管段的影响系数矩阵，并利用信息熵确定管段权重；其次，标准化处理影响系数矩阵，通过聚类迭代提出供水管网地震流量监测点优化布局的动态分级方法，对供水管网震后流量监测点进行优化布置分级评定；最后，根据工程实例进行方法实践，结果表明:供水管网中的管线分类较为科学合理，地震监测点在供水管网上分布也比较均匀，而且该模型在一定程度上消除了人为因素的影响，保障了震时管网的监控效果和日常建设的合理性。     

Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks

A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH₄⁺-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m³/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH₄⁺-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH₄⁺-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH₄⁺-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R²) and lower root mean squared errors (RMSE) values.     

A hybrid model for human factor analysis in process accidents: FBN-HFACS

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.     

基于BP神经网络的受限空间火灾联合探测方法

针对航空受限空间火灾探测高误报的问题，在现有技术成果基础上对多种火灾探测方式进行研讨，并提出1种基于BP神经网络技术的飞机机身内部受限空间火灾联合探测报警方法。该方法结合现有烟雾感应、气体传感器探测等常用火灾探测技术，以红外热成像探测为辅助手段，采用神经网络实现数据融合，对模拟实验舱火灾烟雾进行联合探测，在单一火灾探测方式基础上提高了探测准确率。     

隧道救援站通风网络优化设计及人员疏散研究*

为确定铁路隧道救援站最佳通风结构，对烟道布置提出合理建议，采用计算流体力学（CFD）的方法，对铁路隧道救援站通风网络进行优化设计。同时，采用模拟计算的方法对救援站人员疏散进行模拟分析，确定救援横通道布置及防护门开度设置的合理性。研究结果表明:在同等通风参数工况设置下，采用多节点排烟竖井结构后各救援横通道流量分配更均匀，救援站压力平衡性更好，通风效率可提升15%；通过疏散模拟证实，在长560 m的紧急救援站范围内设置10条疏散横通道，横通道设1.7 m宽的逃生门能够满足疏散要求。     

基于统计学方法的南方某市管网水水质特征研究

为综合解析南方某市管网末梢水水质的时空变化特征、识别主要响应指标及指标间相关性,以该市近4年供水管网末梢水水质监测数据为研究对象,利用主成分分析法和对应分析法对管网末梢水质进行了特征研究。主成分分析法评价结果表明:该市管网末梢水年度总体水质存在差异,其中以2016年为最佳;夏秋季节更易发生水质异常现象,5~9月应加强管网末梢水水质管控;区域上看已完成深度处理改造的梅林水厂供水覆盖区管网末梢水水质最优,南山水厂供水覆盖区管网末梢水水质最差;水质指标硫酸盐、氯化物、硬度之间,Fe、浊度、总氯之间具有相对其他水质指标更高的相关性。对应分析法结果表明:管网末梢水水质主要响应指标可重点关注Fe、浊度、耗氧量、总氯和硫酸盐;其中Fe、浊度、耗氧量呈正相关性强,上述指标与硫酸盐、总氯呈负相关性。     

A Bayesian network-based approach for the assessment and management of ageing in major hazard establishments

Currently, there is an increasing attention towards ageing of industrial equipment, as the phenomenon has been recognised as a cause of severe accidents, recorded in the last years in many process establishments. Recent studies described ageing through a number of key-factors affecting the phenomenon by accelerating or slowing it down. The Italian Competent Authority for the prevention of chemical accidents (Seveso III Directive) adopted a short-cut method, accounting for the assessment of these factors, to evaluate the adequateness of ageing management during inspections at Seveso sites. In this paper, a Bayesian Network was developed, by using the data gathered during the first application of the short-cut method, with the aim to verify the robustness of the approach for ageing assessment and the validity of the a priori assumptions used in assessing the key-factors. The structure of the Bayesian network was established by using experts’ knowledge, whereas the Counting Learning algorithm was adopted to execute the parameter learning by means of the software Netica. The results showed that this network could effectively explore the complex logical and uncertain relationships amongst factors affecting equipment ageing. Results of the present study were exploited to improve the short-cut method.