Inexpensive substrate sampler for macroinvertebrates

Assessment of aquatic macroinvertebrates is a critical component of many watershed monitoring programs and passive samplers are often used to collect long-term site data, especially in environments where active sampling is not possible. However, standard passive samplers can be expensive and lost in extreme conditions. We developed a sampler using plastic soda bottles (PSB) filled with river rock and compared its effectiveness with standard Hester-Dendy samplers in both lotic and lentic environments. Abundance, taxa richness, and macroinvertebrate composition showed no significant differences between sampler types in either habitat type. PSB samplers, which can be constructed for less than one dollar each, collected the same number of organisms and represented the same diversity as Hester-Dendy devices that cost around $38 each. In studies where funds are limited, PSB samplers appear to be suitable for passive monitoring.     

舰船海水管路腐蚀状态评估技术研究

目的研究建立舰船海水管路的腐蚀状态评估指标体系及评估方法。方法通过经验选择影响舰船海水管路腐蚀状态的重要指标,并确定相应评估方法、评价标准及权重。结果选择管材腐蚀程度、阴极保护状态、电绝缘保护状态、海水流速控制状态、密封材料密封状态5项参数组成海水管路系统腐蚀状态评估指标体系。结论可选用层次-模糊法对海水管路腐蚀状态进行评估,其中层次分析法(AHP)用来确定评价指标权重,模糊综合评判法用来处理评价指标值。     

海水重金属监测分析方法研究

总结国内外的海水水质重金属的监测分析方法,调查国内海水重金属分析方法,比较各种分析方法的优缺点.得到以下结论：海水中汞、砷、硒的主要分析方法为原子荧光法,此方法具有操作简单、灵敏度高等优点;铜、铅、锌、镉、镍的主要分析方法为原子吸收法,此方法比极谱法具有更好的灵敏度和重现性;总铬和六价铬的分析方法主要为二苯碳酰二肼分光光度法,方法操作简单、易于推广.     

基于神经网络分类器的城市污水处理厂水力冲击预警系统

提出了一套基于神经网络分类器的城市污水处理厂水力负荷冲击预警系统,以期对进水水量骤增现象进行提前1天的预报,使污水处理厂可根据预报结果提前采取水力冲击防护措施,从而保证各单元的平稳运行.根据进水水量的涨幅将某污水处理厂12年日进水水量监测数据分为"常规"和"冲击"两类,重点对"冲击"数据进行提前1天的预测,并采用冲击漏报率、冲击误报率和报准率对模型的预测精度进行评价;同时,基于同样的建模方法和不同的训练、验证样本建立了N(1)、N(2)和N(3)3个平行模型,以对模型的鲁棒性和建模方法的可重复性进行考察.结果显示,3个模型对2010年、2011年和2012年3年测试样本的预测效果良好,冲击漏报率和报准率两项指标数值均较为稳定,分别在0~0.167和0.981~0.995之间浮动,冲击误报率虽然在数值上的浮动较大,最低为0.143,最高为0.500,平均为0.310,但仍在工程上的可承受范围内.该结果表明,本研究基于神经网络分类器所建立的3个神经网络模型预测精度高、鲁棒性好,显示出良好的性能,有望为污水处理厂水力冲击防护工作提供有力参考.     

秦皇岛洋河-戴河流域浅层地下水咸化程度评价

为了解秦皇岛洋戴河流域浅层地下水咸化程度,开展了野外调查和水样采集,现场测试EC、TDS指标,并对水样进行了室内水化学分析.应用层次分析法结合Arcgis对选取的6个指标(Cl离子、TDS、Li离子、钠吸附比、硝酸盐、潜在盐度)进行权重计算、归一化处理和栅格计算,得出地下水咸化等级分区(未咸化区、轻微咸化区、中等咸化区、严重咸化区).咸化最严重区域位于沿海平原,包括枣园村、王各庄村.中度咸化区部分位于沿海平原,包括都寨村、西陆庄村、蒋营村、樊各庄,主要是由海水入侵导致的;而樊各庄咸化的原因是海水入侵和工业废水的污染;其他中度咸化区位于低山丘陵区,包括大湾子村、兴隆寨村,则是由水岩作用、农业污水、生活污水造成的.轻微咸化区主要分布在中等咸化区周围,未咸化区集中在山前平原区.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

基于人工神经网络建模的PTC功率智能检测技术

PTC是家用空调器中主要器件之一,具有耗电量大、非线性的特征。目前PTC常用的功率检测方法是通过"专用电量计量芯片结合电压、电流采样电路"来实现,存在成本高和可靠性低问题。提出一种基于人工神经网络的PTC功率纯软件计算方法,无需增加任何硬件成本。通过实验验证,精度达±3%,满足产品化需求,成熟可靠。     

Formation and speciation of disinfection byproducts during chlor(am)ination of aquarium seawater

The chemistry associated with the disinfection of aquarium seawater is more complicated than that of freshwater, therefore limited information is available on the formation and speciation of disinfection byproducts(DBPs) in marine aquaria. In this study, the effects of organic precursors, bromide(Br-) and pre-ozonation on the formation and speciation of several typical classes of DBPs, including trihalomethanes(THM4), haloacetic acids(HAAs),iodinated trihalomethanes(I-THMs), and haloacetamides(HAc Ams), were investigated during the chlorination/chloramination of aquarium seawater. Results indicate that with an increase in dissolved organic carbon concentration from 4.5 to 9.4 mg/L, the concentrations of THM4 and HAAs increased by 3.2–7.8 times under chlorination and by 1.1–2.3 times under chloramination. An increase in Br-concentration from 3 to 68 mg/L generally enhanced the formation of THM4, I-THMs and HAc Ams and increased the bromine substitution factors of all studied DBPs as well, whereas it impacted insignificantly on the yield of HAAs. Pre-ozonation with 1 mg/L O3 dose substantially reduced the formation of all studied DBPs in the subsequent chlorination and I-THMs in the subsequent chloramination. Because chloramination produces much lower amounts of DBPs than chlorination, it tends to be more suitable for disinfection of aquarium seawater.     

无上部抽吸装置储罐区人工龙卷风风场模拟

为了减少因储罐泄漏位置不确定造成的人员伤亡,指导应急疏散,提出利用人工龙卷风定向控制气体流动方向的模型。首先基于Fluent软件建立了储罐区域人工龙卷风数值模型,分析切向速度和压强沿径向变化规律,发现与经典Rankine涡模型切向速度沿径向分布规律一致,证实可在储罐区以射流相切的方式形成人工龙卷风风场。其次研究了涡流比和进风量对风场控制气流特性的影响,即分析形成的龙卷风风场最大切向速度、压强差变化规律,结果表明,涡流比越大,形成的龙卷风风场中切向速度和压强差越小,即气流向中心汇聚能力越弱;进风量越大,形成的龙卷风风场中切向速度和压强差越大,即气流向中心汇聚能力越强。研究表明,用人工龙卷风控制储罐泄漏气流方向是可行的。     

基于人工神经网络的气体泄爆最大超压预测研究

为解决传统经验公式在预测气体泄爆中最大超压出现时的较大偏差或过于保守的问题，提出使用人工神经网络预测气体泄爆最大超压。基于124组实验数据，采用BP与RBF神经网络，通过优化算法计算与迭代循环对泄爆样本中的影响因素进行降维与选择，并确定2类神经网络本身在学习与计算气体泄爆样本时的相关参数。结果表明:PCA（主成分分析法）在当前样本条件下的降维效果较差，而通过迭代对比确认气体泄爆样本中的5类特征全部保留时神经网络的训练模拟效果最好；通过对124组实验数据进行随机挑选训练集与测试集的训练模拟结果发现，神经网络对气体泄爆中最大超压的预测效果较好；通过对比Molkov提出的和经Fakandu等改进的NFPA 68经验公式以及2类神经网络的预测结果表明，神经网络相比于传统气体泄爆经验公式具有明显优势。