铝盐对厌氧微生物产甲烷活性的影响研究

废水中铝盐含量增高以及铝盐的生物毒性使其对废水生物处理工艺中微生物产生的危害引起了人们的关注。本文以产甲烷活性为指标，通过投加氯化铝测定其对颗粒污泥的产甲烷毒性来研究铝盐对厌氧菌的危害。结果表明，铝盐使厌氧颗粒污泥的活性明显降低，但其毒性可以驯化，且清除铝盐后厌氧颗粒污泥的活性可以恢复。     

厌氧悬浮颗粒污泥床同时反硝化产甲烷研究

利用厌氧悬浮颗粒污泥床反应器，以自配水为基质，通过微生物的反硝化作用和产甲烷作用成功实现了在单级反应器中去除硝酸盐和水中有机质的目的。反应器开始接种的污泥是产甲烷颗粒污泥，通过不断提高进水中硝酸盐的浓度，使厌氧颗粒污泥逐渐适应水中的硝酸盐，反硝化剩余的有机碳源转化为甲烷气体。在硝酸盐负荷为0.75kgN03^- -N·m^-3d^-1和COD负荷为14.1kgCOD·m^-3d^-1的稳态下，硝酸盐和有机碳的去除率分别为99.5％和90.1％以上。对反应器产生的气体所进行的气体组成测试表明，加入的硝酸盐全部转化为氮气，这一结果表明发生了真正的反硝化反应。     

膨胀颗粒污泥床（EGSB）反应器处理高浓度自配水的试验研究

本文对EGSB反应器处理高浓度自配有机废水进行了较系统地研究，研究表明中温条件下(31～35℃)，进水COD浓度为8200～9000mg     

基于粒子群优化算法与混合罚函数

粒子群算法(PSO)具有简单易实现，可调参数少的优点。将其用于最优潮流的求解，结合混合罚函数来限制最优潮流的约束条件，使粒子群算法的寻优速度加快，迭代次数减少。通过在IEEE9节点和IEEE30节点上的仿真计算表明，该算法在优迭代速度和收敛精度上都取得了较好的效果。     

二相UASB反应器中产酸颗粒污泥和产甲烷颗粒污泥的物理性质和化学成分

本文对二相UASB反应器中产酸和产甲烷颗粒污泥的物理性质和化学成分进行了初步的研究。结果表明:(1)二种颗粒污泥均具有良好的沉降性能,并且产酸颗粒污泥的湿密度、强度和稳定性均优于产甲烷颗粒污泥。(2)二种颗粒污泥的表面元素基本相同,但内部Ca,Si含量相差较大,并且均未发现有FeS存在。(3)产酸和产甲烷颗粒污泥中胞外多聚物(ECP)的含量分别为0.57％和1.46％(以污泥干重计)。ECP中的主要成分为糖和糖醛酸。     

水泵水轮机流动可视化图像解析系

主要介绍了自行开发的水泵水轮机流动可视化图像的解析原理，并就示踪物的追踪与最佳相关域的确定问题进行了深入的分析，介绍了图像解析的程序设计的思想与功能。试验与计算结果表明本套软件适合于旋转的流体机械的流场图像的解析计算。     

基于WEB农业物料特性信息管理系统的开发

以农业物料特性的公式、线图、数据表及相关信息为对象，利用ASP技术，采用B／S架构结构，建立基于WEB农业物料特性信息管理系统。系统的建立为农业物料信息提供了有效的网络知识库平台，实现了数据的共享，符合我国农业信息化的发展方向。     

改进粒子群优化（MPSO）算法在动

基于在水资源不充足的情况下，对都江堰灌区六大渠干水资源的合理分配，使农业效益达到最大。首先建立灌区优化配水模型，并将粒子群优化算法（PSO）及其改进的算法应用于该模型。分别对标准PSO、两种改进PSO（MPSO）算法与遗传算法进行仿真对比，结果显示采用PSO算法及其MPSO在农业经济效益上可获得更好的寻优效果，提高了水资源的利用率。     

基于粒子群算法优化BP神经网络的土壤含水量短期预测模型

为提高土壤含水量预测精度,基于物联网监测数据,提出了粒子群算法(PSO)优化BP神经网络的土壤含水量预测方法。首先应用主成分分析法筛选出影响土壤含水量的关键影响因子,然后构建8-5-1的BP神经网络拓扑结构,应用粒子群算法优化BP神经网络的初始权值和阈值。结果表明:与传统BP神经网络相比,新模型优化了网络结构,避免了陷入局部最优解,具有良好的预测效果;模型的评价指标平均绝对误差、平均绝对百分误差、误差均方根分别为0.259 2、0.010 5和0.135 6,与单一BP神经网络相比,预测精度更高,可满足实际的土壤含水量预测的需要。     

Precipitation forecasting by large-scale climate indices and machine learning techniques

Global warming is one of the most complicated challenges of our time causing considerable tension on our societies and on the environment. The impacts of global warming are felt unprecedentedly in a wide variety of ways from shifting weather patterns that threatens food production, to rising sea levels that deteriorates the risk of catastrophic flooding. Among all aspects related to global warming, there is a growing concern on water resource management. This field is targeted at preventing future water crisis threatening human beings. The very first stage in such management is to recognize the prospective climate parameters influencing the future water resource conditions. Numerous prediction models, methods and tools, in this case, have been developed and applied so far. In line with trend, the current study intends to compare three optimization algorithms on the platform of a multilayer perceptron (MLP) network to explore any meaningful connection between large-scale climate indices (LSCIs) and precipitation in the capital of Iran, a country which is located in an arid and semi-arid region and suffers from severe water scarcity caused by mismanagement over years and intensified by global warming. This situation has propelled a great deal of population to immigrate towards more developed cities within the country especially towards Tehran. Therefore, the current and future environmental conditions of this city especially its water supply conditions are of great importance. To tackle this complication an outlook for the future precipitation should be provided and appropriate forecasting trajectories compatible with this region's characteristics should be developed. To this end, the present study investigates three training methods namely backpropagation (BP), genetic algorithms (GAs), and particle swarm optimization (PSO) algorithms on a MLP platform. Two frameworks distinguished by their input compositions are denoted in this study: Concurrent Model Framework (CMF) and Integrated Model Framework (IMF). Through these two frameworks, 13 cases are generated: 12 cases within CMF, each of which contains all selected LSCIs in the same lead-times, and one case within IMF that is constituted from the combination of the most correlated LSCIs with Tehran precipitation in each lead-time. Following the evaluation of all model performances through related statistical tests, Taylor diagram is implemented to make comparison among the final selected models in all three optimization algorithms, the best of which is found to be MLP-PSO in IMF.