间歇曝气潜流人工湿地的污水脱氮效果

采用间歇曝气运行方式,提升潜流人工湿地生活污水处理系统中的溶解氧浓度,强化脱氮效果。结果表明,间歇曝气运行方式有效提高了湿地内部溶解氧水平,曝气时溶解氧浓度可达6~9 mg/L,停止曝气后,溶解氧浓度迅速下降至0.5 mg/L以下,在湿地内部营造了一种交替的好氧和缺氧环境,分别促进好氧硝化和缺氧反硝化作用。在水力停留时间为3 d的情况下,间歇曝气潜流人工湿地系统对氨氮、总氮和COD的去除率分别可达到98.0%、87.6%和96.3%,较常规潜流人工湿地系统分别提高了74.1%、56.4%和18.1%,实现了氨氮、总氮和COD的同步高效去除。     

两级序批式MBR膜污染特性研究

采用两级SBR与膜法相结合的两级序批式MBR工艺处理生活污水,考察两级序批式运行模式对膜污染特性的影响.对运行111 d后的平板膜污染阻力分布进行了测算,并对两级序批式MBR与连续流单级好氧MBR的平板膜污染速率进行对比试验.结果表明: 两级序批式MBR的平板膜污染阻力以凝胶极化阻力为主,占总阻力的43%,沉积阻力较低,仅占总阻力的10%;与连续流单级好氧MBR相比,两级序批式MBR可以在更高的膜通量下运行,保持更低的膜污染速率,在运行42 d后,过膜流量从100 L/h下降至85 L/h,过膜压差从1.52×10~4 Pa增加至2.22×10~4 Pa;而连续流好氧MBR系统,过膜流量从61 L/h降低至39 L/h,过膜压差却从1.01×10~4 Pa增加至2.63×10~4 Pa.研究表明,两级序批式MBR工艺可以改善膜过滤过程的水力条件,从而有效地减缓浓差极化,降低沉积污染及凝胶层污染,序批式、膜间歇运行与空曝的结合起到了关键性作用.     

人工浮床对汾江河水质净化的研究

对佛山市汾江河内布置浮床的水域的7个断面进行采样监测,分析了布置浮床侧与未布置浮床侧各水质指标的差异,以考察人工浮床对汾江河水质的净化效果。同时比较了圆币草、水罂粟、梭鱼草、狐尾草、美人蕉和鸢尾6种浮床植物的生物量和氮磷吸收量。结果表明,布置人工浮床一侧的水质TN、TP、COD和NH4＋-N含量均显著低于未布置浮床一侧的水质,表明人工浮床对水体水质的有净化效果。总面积约4 900 m2的浮床植物经过3个月的生长,从水体中共吸收了192.5 kg的氮和76.1 kg的磷。6种浮床植物中,狐尾草的净增生物量最高,达到64.2 kg/m2;圆币草次之,为62.2 kg/m2。狐尾草和圆币草对氮磷的吸收能力在6种植物中处于较高水平,每平方米狐尾草和圆币草从水体中分别吸收了51.61g氮、19.79 g磷和46.90 g氮、22.93 g磷。综合比较得出,狐尾草和圆币草在生物量和氮磷吸收量上均保持在较高水平,是较好的浮床植物。本研究为人工浮床在南方类似河流中的应用及植物选择提供了参考依据。     

响应面法和神经网络优化Acinetobactersp．DNS32发酵基质

为了提高阿特拉津降解菌Acinetobactersp．DNS32的产量，分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分（玉米粉、豆饼粉、K：HPO。）进行优化研究。响应曲面法确定3种成分的含量为玉米粉39．494g／L，豆饼粉25．638g／L和K。HPO。3．265g／L时，预测发酵活菌最大生物量为7．079×10^8CFU／mL，实测量为7．194×10^8CFU／mL；人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39．650g／L，豆饼粉为25．500g／L，K2HPO4为2．624g／L时，预测最大值为7．199×10^8CFU／mL，实测量为7．244×10。CFU／mL；最终确定培养基配方：玉米粉为39．650g／L，豆饼粉为25．500g／L，K2HPO4为2．624g／L，CaCO3为3．000g／L，MgSO4·7H2O和NaCl均为0．200g／L；优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36．6％。结果表明，在阿特拉津降解菌DNS32发酵培养基组分优化方面，响应面法和基于人工神经网络的遗传算法都是可行的，基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。     

基于人工免疫原理的事故预防研究

介绍生物免疫识别模式,分析事故预防过程的实质是抑制潜在危险转化成事故的控制过程,分析比较事故预防与生物免疫识别模式在作用、生存环境、动作触发源等的共性,构建了基于免疫识别模式的事故预防系统并建立了事故预防数学模型;定义事故预防系统的识别率、失效率、误判率并给出了数学表达式,指出监测生产系统各环节状态信息是否符合系统的安全要求就是识别潜在危险的过程,是事故预防的关键,基于人工免疫原理的事故预防数学模型具有较强的健壮性、自适应性和动态防护性等特点。     

曝气生物流化床处理高氨氮粪便污水

应用好氧曝气生物流化床反应器处理动车集便器粪便污水,研究反应器同步硝化反硝化脱氮及去除COD效能,以及DO对处理效能的影响,通过镜检观察反应器内微生物特性,探究反应器同步硝化反硝化脱氮机理。结果表明,反应器维持DO在2.5 mg/L左右时,对粪便污水中氨氮、TN和COD的去除率分别达99.8%、84.1%和95.5%,在好氧曝气生物流化床反应器中,实现同步硝化反硝化脱氮并去除有机物。分析认为,反硝化脱氮主要发生在生物膜内的厌氧微环境,反硝化反应主要由厌氧反硝化菌完成,曝气生物流化床反应器同步硝化反硝化脱氮机理主要从微环境理论解释。     

Simultaneous determination of three herbicides by differential pulse voltammetry and chemometrics

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D – first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.     

基于人工蜂群算法与BP神经网络的水质评价模型

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。     

高浓度氨氮废水好氧反硝化反应的基本条件

文章通过室内实验,对高浓度氨氮废水(垃圾渗滤液)间歇曝气,在只存在有机碳、无机氮的条件下进行好氧反硝化脱氮研究。实验结果表明:垃圾渗滤液中存在好氧反硝化土著微生物菌落;发生好氧反硝化的基本条件为在溶解氧充足的条件下间歇曝气;碳源不仅是厌氧反硝化所必须的,同样也是好氧反硝化的必要条件。     

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.