内电解组合工艺处理重度富营养化水体的初步中试试验

采用催化铁内电解-沸石床-生物活性炭组合工艺进行了处理重度富营养化水的中试试验.通过对进水与出水浊度、TP、CODMn、UV254、总铁、NH3-N、和NO2--N的监测,研究了本组合工艺对重度富营养化水体的处理性能.试验期间,水温约在10℃.初步结果表明,本组合工艺对浊度、TP、CODMn和UV254均有较好的去除作用,平均去除率达到50%以上,出水中铁含量均低于进水.由于水温过低,未观察到氨氮和亚硝酸盐氮的明显变化.     

环境经济损益分析在矿区环评中的应用

开展环境经济损益分析,将环境影响货币化是环评工作难点之一.结合矿区环评实例,探讨了指数计算法在矿区环境经济损益分析中的应用.结果表明该方法客观全面,简单实用,可较好地应用于矿区环评的环境经济损益分析并对相关建设项目的环境经济损益分析具有借鉴意义.     

稳高压消防水系统的应用

武汉石油化工厂针对原消防水系统存在的问题,投资新建了稳高压消防水系统.本文对系统的内容、试运中存在的问题以及解决办法和测试情况进行了分析、讨论.     

小康居住区的水环境与水质净化工程

分析了小康居住区水环境的含义，组成，并对居住区水质净化工程中3个热点问题（优质饮水，污水处理和中水回用等）进行了较深入的分析，建议小康居住区优质饮水供应以小区纯水站净化，管理输送至用户方式为主；不居住区的污水处理应分3种情况区别对待，在居住区污水处理达到排放标准的基础上，进一步处理回用，是经济合理的。     

用神经网络辨识化学强化一级处理系统

运用实验室得到的数据为样本 ,选取了CODCr和絮凝剂投加量为指标 ,采用三层结构的神经网络 ,利用Matlab的神经网络工具箱中的批处理梯度下降法对CEPT系统经行辨识。辨识结果表明 :模型的预测值与实测值的平均误差在 3 .7%左右 ,具有较高的精度。同时 ,该模型还具有适应性强 ,使用方便 ,高效的特点 ,为CEPT系统的在线实时控制提供了一条有效的途径。     

上海合流污水深海排放强化一级预处理的研究

复合式厌氧污泥床反应器是一种改进的厌氧处理工艺 ,可有效去除城市生活污水中的污染物 ,并提高了污水的可生化性 ,是一种较理想的城市污水强化一级处理工艺 ,为上海合流污水深海排放的实施提供了保障。本文对该工艺的装置特点和处理效果进行了分析研究 ,并对其应用前景进行了展望。     

森林平均覆盖率与公路水毁因子的关系

通过对公路水毁因子数据和森林平均覆盖率数据的分析 ,研究公路水毁因子和森林平均覆盖率的相关关系 ,求出公路水毁因子和森林平均覆盖率的回归方程 ,从而为公路建设、养护、营运中的问题提供了定量分析的理论依据     

Production of laccase by Coriolus versicolor and its application in decolorization of dyestuffs : (Ⅰ) Production of laccase by batch and repeated-batch processes

The production of laccase by Coriolus versicolor was studied.The effect of cultivation on laccase production by Coriolus versicolor was examined to obtain optimal medium and cultivation conditions.Both batch and repeated-batch processes were performed for laccase production.In repeated-batch fermentation with self-immobilized mycelia, total of 14 cycles were performed with laccase activity in the range between 3.4 and 14.8U/ml.     

沪蓉国道恩施付家坡滑坡稳定性评价研究

沪蓉国道是我国东西向一条重要的主干线,是国家的重点建设工程之一,对西部开发与经济建设起着重要的作用。该主干线恩施付家坡滑坡与付家坡隧道相邻,对沪蓉国道的施工与安全运营存在着安全与质量隐患。本文采用推力传递系数法和有限元法,对沪蓉国道付家坡滑坡的稳定性进行了综合评价研究,并提出了合理有效的滑坡治理措施。     

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake

Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.