反渗透海水淡化水的消毒

反渗透海水淡化是解决水资源危机的一项重要技术。反渗透海水淡化水在饮用水生产过程中需进行消毒以解决微生物污染问题。采用次氯酸钙、二氧化氯和紫外线3种消毒方法,考察了反渗透海水淡化水和自来水的消毒效果,比较了对主要水质指标如pH、硬度和碱度的影响及余氯和余ClO2的衰减性。结果表明,二氧化氯消毒速率最快,次氯酸钙消毒对水质的影响最大,而紫外线的消毒效果最为稳定。可为反渗透海水淡化水的进一步利用提供依据。     

基于人工神经网络的行业无害化生产能力评价模型研究

利用BP前向人工神经网络方法 ,建立了一种全新的基于人工神经网络的行业无害化生产能力评价模型 ,该模型采用动量梯度下降反向传播和学习速率自适应调整相结合的训练学习算法。最后 ,以造纸及纸制品业为例 ,对中国各省市造纸行业的无害化生产能力进行评价 ;并进一步分析中国各省造纸行业无害化生产能力的时空分布规律 ,为我国行业规划、布局决策提供科学依据。     

ATFITUDES TOWARDS ARTIFICIAL WETLANDS IN ONTARIO FOR STORMWATER CONTROL AND WATERFOWL HABITAT1

ABSTRACT: The objective was to survey individuals from different interest groups in Ontario to ascertain attitudes towards the use of artificial wetlands for stormwater management and to provide waterfowl habitat. In total, 166 questionnaires were distributed and 124 cempleted returns were received. The results of the survey show that the provision of wetlands as waterfowl habitats in the context of stormwater impoundments is severely limited and is not considered an important management option. The overwhelming rejection of this integrated approach seems to be due to a lack of understanding and its comparative newness. Some of the perceived problems and concerns could be addressed by improvements in the design of the artificial wetlands. The design and implementation of an artificial wetland prototype as part of an education program in Ontario is recommended. A few research opportunities also are outlined.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

基于神经网络的温度预测

室内温度与诸多影响因素之间的非线性、复杂性等关系 ,给建模、预测带来了难度 ,引入了人工神经网络 ;利用人工神经网络的非线性、并行计算和自学习特性进行建模 ,实现了对温度模拟     

SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.     

长江河口越江工程在上海港建设中的重要作用

探讨了连接上海，崇明和江苏海门的长江河口桥隧工程在连接河口岛屿间交通，发挥崇明的区位优势，促进地区经济发展以及在上海港建设中的作用。详细论述了越江工程完成后上海新港的扩建，根据河口河水岸线的分布，提出了在横沙岛东滩修建人工岛的设想，最后对几种越江方案进行了分析比较，提出了作者的看法。     

三峡茅坪溪沥青混凝土心墙堆石坝位移反演分析与安全预测

本文研究了基于综合应用人工神经网络和演化算法的位移反演分析方法,并将此方法应用于三峡茅坪溪沥青混凝土心墙堆石坝的变形反演分析中。以茅坪溪一期工程原型观测成果为依据,反演出能够正确反映坝体变形特性的邓肯-张EB模型参数,从而预测了施工期末和蓄水期末该坝的变形特性,并基于总应力法研究了心墙水力劈裂破坏发生的可能性,为茅坪溪堆石坝运行期的安全生产提供依据。研究结果表明,茅坪溪心墙堆石坝不会发生水力劈裂破坏,该坝蓄水后虽然水压上升,但心墙仍是安全的。     

固定化小球藻对海水养殖废水氮磷的处理

本研究利用海藻酸钠（SA）作为载体、以氯化钙（CaCl₂）为交联剂,探究小球藻最佳固定化条件及其对海水养殖废水氨氮和磷酸盐的处理效果.通过对比不同浓度SA和CaCl₂对小球藻生长的影响及不同固定化条件的藻球对氨氮、磷酸盐处理效果,确定最佳固定化条件为2.0% SA和2.0% CaCl₂.对比固定化藻球和悬浮小球藻对模拟海水养殖废水氨氮、磷酸盐去除效果,结果表明固定化藻球比悬浮藻液对氮、磷处理效果更好.其中低接种率（1：10）固定化藻球的最大氨氮、磷酸盐去除率分别为63.26%和62.76%.固定化小球藻浓度越高,其净化能力越强,高接种率（1：1）固定化藻球的最大氨氮、磷酸盐去除率分别是85.16%和75.94%.连续流运行下固定化藻球对海水养殖废水氨氮、磷酸盐的平均去除率分别为84.49%和72.17%.小球藻固定化态保留并延长了悬浮态生长活性,提高了对海水养殖废水脱氮除磷效果.     

人工土壤渗滤工艺研究与应用

土地处理系统作为生态学处理方法,实际上是追求土壤、含水层和植物的“处理”与“利用”两个功能的总体实现。人工土壤渗滤工艺正是针对传统土地处理工艺的改进而发展起来的一种技术,它使用人造填料代替土壤,构造人工土壤环境;有效改善了土地处理工艺占地较大和处理负荷低的弊端。并通过对人工土壤渗滤工艺的实际应用,对工艺的处理效果进行了研究,尤其是通过一系列的微生物试验,对工艺中的核心部分——“人工土壤”的生物特性以及构建进行了有益的探索。