Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

• UV-vis absorption analyzer was applied in drainage type online recognition. • The UV-vis spectrum of four drainage types were collected and evaluated. • A convolutional neural network with multiple derivative inputs was established. • Effects of different network structures and input contents were compared. Optimizing sewage collection is important for water pollution control and wastewater treatment plants quality and efficiency improvement. Currently, the urban drainage pipeline network is upgrading to improve its classification and collection ability. However, there is a lack of efficient online monitoring and identification technology. UV-visible absorption spectrum probe is considered as a potential monitoring method due to its small size, reagent-free and fast detection. Because the performance parameters of probe like optic resolution, dynamic interval and signal-to-noise ratio are weak and high turbidity of sewage raises the noise level, it is necessary to extract shape features from the turbidity disturbed drainage spectrum for classification purposes. In this study, drainage network samples were online collected and tested, and four types were labeled according to sample sites and environment situation. Derivative spectrum were adopted to amplify the shape features, while convolutional neural network algorithm was established to conduct nonlinear spectrum classification. Influence of input and network structure on classification accuracy was compared. Original spectrum, first-order derivative spectrum and a combination of both were set to be three different inputs. Artificial neural network with or without convolutional layer were set be two different network structures. The results revealed a convolutional neural network combined with inputs of first and zero-order derivatives was proposed to have the best classification effect on domestic sewage, mixed rainwater, rainwater and industrial sewage. The recognition rate of industrial wastewater was 100%, and the recognition rate of domestic sewage and rainwater mixing system were over 90%.     

天津市冬季气溶胶吸湿因子的粒径分布特征

气溶胶粒径吸湿增长因子［g（RH）］是影响气溶胶消光和气溶胶辐射强迫的重要因素.利用吸湿性串联差分电迁移率分析仪（HTDMA）观测了天津市冬季不同污染状态下气溶胶粒子g_m（RH）的粒径分布.同时基于水溶性离子的粒径分布,利用κ-Köhler理论获取了较宽粒径范围内（60 nm~9.8 μm）的g_κ（RH）,为环境状态下气溶胶光学参数和直接辐射强迫的模拟提供基础.结果表明,清洁状态下大气光化学反应较为活跃,超细粒子（<100 nm）的g_m（RH）较高,g_m（RH=80%）在1.30以上.重度污染天,气溶胶中水溶性离子的质量分数随粒子粒径增大而增加,导致g_m（RH）随着粒子粒径增大而增大,300 nm粒子的g_m（RH=80%）和g_m（RH=85%）分别可达1.39和1.46.在较宽粒径范围（60 nm~9.8 μm）内,不同模态气溶胶的吸湿性强弱表现为积聚模态>爱根模态>粗模态.大气重污染过程中气溶胶粒径明显增大,积聚模态气溶胶中NO₃^-和SO₄^2-含量较清洁天明显增加,受此影响,污染状态下积聚模态气溶胶的吸湿性较清洁天明显增强,g_κ（RH）达到1.3~1.4,具有强吸湿性的气溶胶粒径范围也同时扩大,在0.18~3.1 μm粒径段均较高,对能见度恶化有重要的贡献.     

石家庄近地层风场辐合与重污染关系研究

石家庄多发生以风场辐合为特征的局地污染事件,客观判识辐合区和辐合过程强弱对重污染预报指示意义重大.为研究石家庄风场辐合特征及其对局地污染的贡献,更好地为区域重污染天气预报预警提供客观指标参考,在引入固定高度层内矢量通风系数物理量的基础上,结合箱体模型提出一种定量计算近地层风场辐合强度系数的方法,并利用ERA-Interim再分析资料计算了京津冀地区矢量通风系数以及辐合强度系数,对2016年9月—2019年3月期间10次典型风场辐合污染过程进行分析,探讨了风场辐合形成的原因和对污染贡献的关系.结果表明:①受地形影响,石家庄易出现近地层风向、风速辐合,均可归结为区域内输入通风量大于输出通风量,西部太行山地形的阻挡加重了其辐合程度.②风场辐合使空气中的水汽和污染物汇聚,为二次反应提供高湿条件,这种正反馈作用促发PM_2.5爆发性增长,期间ρ（PM_2.5）平均每小时可上升25 μg/m3.③石家庄近地层辐合强度系数在26以上时对污染贡献作用明显,辐合区污染强度比周边偏高1~2个等级,ρ（PM_2.5）比区域背景值偏高85%~200%.研究显示,风场辐合对重污染贡献明显,是形成局部污染物浓度偏高、PM_2.5爆发性增长的重要气象指标.      

悬浮铝粉尘的爆轰波结构

使用两相流模型,对管中的悬浮铝粉尘的爆轰波结构进行了理论分析.铝粉尘的爆轰波模型中,气体和颗粒具有不同的速度及温度,并考虑了动量变化及管壁损耗对稳定结构的影响.本文研究了颗粒直径对铝颗粒的点火延迟、爆轰波压力以及温度的影响,得到了爆轰波中各物理量分布,确定了空气中悬浮铝粉尘发生爆轰的临界颗粒尺寸为14μm.     

高应变率下HRB600高强钢筋力学性能试验研究

钢筋混凝土结构抗震分析中应该采用钢筋材料的动态塑性本构模型,HRB600高强钢筋作为新一代建筑钢材,尚无其在高应变率下的力学性能研究。首先进行了HRB600高强钢筋拉伸力学性能试验,得到了不同应变率下的HRB600高强钢筋力学性能数据,屈服强度最大提高11.5%,极限强度最大提高8.9%,然后拟合得到HRB600高强钢筋在高应变率下的强度提高系数表达式。研究结果表明:随着应变率的增大,钢筋的屈服强度和极限强度均得到提高;在相同应变率条件下,钢筋屈服强度动力提高系数大于极限强度动力提高系数;相比较低强度钢筋,应变率敏感性明显低于HPB235、HRB335及HRB400,但与HRB500钢筋相差较小,而极限强度应变率敏感性明显高于低强度钢筋;基于试验数据得到的HRB600高强钢筋在高应变率下的动态塑性本构模型,与试验值吻合较好。研究成果可作为HRB600高强钢筋混凝土结构抗震评估的依据。     

Uncertainty assessment of the model RICEWQ in northern Italy

Model predictions are often seriously affected by uncertainties arising from many sources. Ignoring the uncertainty associated with model predictions may result in misleading interpretations when the model is used by a decision-maker for risk assessment. In this paper, an analysis of uncertainty was performed to estimate the uncertainty of model predictions and to screen out crucial variables using a Monte Carlo stochastic approach and a number of statistical methods, including ANOVA and stepwise multiple regression. The model studied was RICEWQ (Version 1.6.1), which was used to forecast pesticide fate in paddy fields. The results demonstrated that the paddy runoff concentration predicted by RICEWQ was in agreement with field measurements and the model can be applied to simulate pesticide fate at field scale. Model uncertainty was acceptable, runoff predictions conformed to a log-normal distribution with a short right tail, and predictions were reliable at field scale due to the narrow spread of uncertainty distribution. The main contribution of input variables to model uncertainty resulted from spatial (sediment-water partition coefficient and mixing depth to allow direct partitioning to bed) and management (time and rate of application) parameters, and weather conditions. Therefore, these crucial parameters should be carefully parameterized or precisely determined in each site-specific paddy field before the application of the model, since small errors of these parameters may induce large uncertainty of model outputs.     

难降解氯碱污水的预处理试验研究

采用混凝、内电解、水解酸化等方法对氯碱污水进行预处理，可以有效去除部分CDD，提高了污水的BOD/COD比率，改善了污水可生化性，为污水的生化处理创造了条件。     

石油化工生产中使用可燃性气体检测报警仪表若干问题的探讨

介绍了可燃性气体检测的基本原理、检测探头应具备的抗干扰能力，并探讨了探头的布点方法和注意事项、仪表的标定及报警点设置、管理维护等问题。     

基于χ~2优化方法的铸钢侧架原始裂纹尺寸分布研究

将铸钢侧架清砂后未经修复的原始裂纹尺寸进行了统计 ,综合运用参数的点估计方法和区间估计方法 ,以置信区间作为优化的约束条件 ,提出了对检验值进行最优化处理的方法 ,并运用二参数约束非线性优化方法的直接解法 ,搜索出最佳点估计值。经过对裂纹尺寸分布参数的计算证明 ,其结果符合对数正态分布     

石油加工与石油化工废料应用于钻井液

石油加工与石油化工废料，包括废白土、酸渣、碱渣、渣油、油泥、生产润滑油添加剂的废料、废催化剂、环烷酸釜残等，在钻井液中的用途较广，可以分别用作钻井液原始组分和润滑剂、消泡剂、多效防塌剂、抑制剂、稳定剂、废钻井液固化剂以及制备这些处理剂的基础材料。文中介绍了前苏联及独联体等在这方面的研究成果和现场应用情况。