Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.     

洪湖沉积物碳氮磷分布特征及污染评价

洪湖是中国第七大淡水湖,富营养化问题日益突出。全面了解洪湖沉积物氮、磷、有机质的含量及分布特征,对掌握其富营养化现状与氮磷污染生态风险具有重要意义。在湖区布设了8个采样点,2019年10月采集50 cm柱状沉积物,分别测定不同深度沉积物总氮(TN)、总磷(TP)以及有机质(OM)含量,分析了TN、TP 和OM 含量的空间分布特征及相关性,并运用综合污染指数法评价其对应的污染程度。结果表明:洪湖沉积物TN含量在467.8~8 454.5 mg/kg之间,平均值2 167 mg/kg,为重度污染,其中近一半的采样点表层沉积物TN含量>5 000 mg/kg;TP含量在502.7~1 252.4 mg/kg之间,平均值693.8 mg/kg,除杨柴湖为重度污染外,其他大部分地区为中度污染;有机质含量占比在5.0%~24.9%之间,平均值9.6%,为重度污染。在垂直空间上,TN、TP 和OM 均在沉积物表层垂向深度0~20 cm存在明显的积累,其含量随垂向深度的增加而迅速降低。研究成果可为洪湖富营养化的控制与治理提供科学数据支撑。     

基于SWMM与NSGA-Ⅱ耦合的城区雨水泵站多目标优化调控

针对城区复杂水系统中雨水泵站的调控系统性弱和优化时效性慢的问题,将SWMM的水量水质过程模拟与NSGA-Ⅱ的快速非支配排序进行深度耦合,从减缓城区内涝、控制面源污染负荷输出与减低耗电量的角度,建立了多线程并行计算和带有决策者偏好的城区雨水泵站多目标优化调控模型。对天津空港经济区的雨水泵站优化调度进行实例研究,结果表明,与常规调度方式相比,在典型设计降水情景下,该优化模型在不显著增加节点积水累计时间的前提下,减少输出了1383.34 kg的污染负荷,节约了621.92 kW·h电能,求解速率可提升至单线程串行计算的4.2倍。该优化方案充分利用了泵站调蓄池的库容缓冲和水质稀释作用,减缓了场次降水对于城区水环境的瞬时冲击影响,降低了运行成本。     

土壤—植物系统净化地表径流非点源 污染物实验研究

利用自行设计的6 m土槽构建土壤—植物系统,以无植被土槽为空白对照,设置不同的污染物进水浓度,采用模拟径流方式进行非点源污染净化实验。结果表明：（1）植被的存在能有效滞缓径流和提高污染物去除率,其中土壤—高羊茅系统的净化效果略优于土壤—紫花苜蓿系统,其SS、NO-3-N、NH3-N、TDP、PP去除率分别达到86.61%、25.83%、52.03%、26.53%及76.59%;（2）在本研究的进水浓度范围内,浓度变化对处理系统净化效果的影响与污染物存在状态有关,进水浓度增加后颗粒态污染物去除率无显著变化而溶解态氮去除率均出现明显下降;（3）以污染物出水浓度随径流长度的变化表征污染物截留特征,SS和PP出水浓度均随径流长度增加而呈指数降低,NO-3-N、NH3-N和TDP出水浓度随径流长度增加而呈线性降低,植被及进水浓度条件对污染物截留特征无明显影响。     

污染土壤/底泥电动修复研究进展

修复污染土壤/底泥对改善生态环境质量极为重要,通过对污染土壤/底泥施加电场,可使污染物定向迁移、富集并去除.综述了电动修复的发展历程,比较了重金属、有机污染物及复合污染物的电动修复机理及其限制因素,分析了土壤预处理(加入酸、氧化剂、共溶剂、表面活性剂,搅拌,升温等)、电极优化(交换电极、阳极逼近、电极矩阵等)、多技术(...     

基于EF-AHP法的坝式小水电站生态环境影响评价

坝式小水电在发挥“以电代柴”效益的同时,也对水资源及水生态环境产生了一定的负面影响。开展坝式小水电站生态环境影响评价研究,对于合理开发水资源,协调水电站开发与流域自然、社会、经济之间的关系具有重要意义。根据坝式小水电站的生态环境影响特点,应用层次分析法建立了指标评分体系,结合生态足迹法的思想来赋予各指标权重,构建了基于EF-AHP法的坝式小水电站综合评价模型,选取西北地区某坝式电站进行了案例应用分析,探讨了评价体系的适用性。结果表明,该模型提高了不同电站之间生态环境影响评价结果的可比性、客观性、实用性。该指标体系为客观评价坝式小水电生态环境影响提供了新的科学手段,对掌握小水电生态环境效应具有重要意义。     

镁矿渣酸溶释出镁离子的特征及影响因素研究

为评估镁矿渣用于人工湿地填料回收磷的潜力,分析了镁矿渣酸溶释出镁离子的特征及影响因素。结果表明:当镁矿渣颗粒直径 <150 μm(100目)时,颗粒浓度为5 g/L时,随着浸出时间的延长,浸出液中镁离子浓度逐渐增加,浸提5 h后镁矿渣中镁离子达到浸出平衡,浸出量不再发生变化;浸出溶液中镁离子的浓度随着浸提液中盐酸浓度的降低而逐渐下降。当镁矿渣颗粒直径 <150 μm时,盐酸浓度为1 mol/L,浸出时间为5 h时,增大浸出液中镁矿渣浓度,镁离子的浸提效率反而降低。当浸提盐酸浓度为1 mol/L,浸出液中镁矿渣浓度为5 g/L时,镁离子的浸出量达到最大值,为117.70 mg/kg。通过对比浸出前后镁矿渣中镁形态发现,在最佳浸提条件下,浸出的镁几乎全部为酸可溶态(占比达92.00%)。浸提后镁矿渣粒径减小,比表面积增大,其矿物晶格受到破坏,对溶液中的磷吸附能力得到强化。理论上对污水中磷的去除能力可达810 mg/kg,是一种极具潜力的磷回收材料。     

Application of a bi-directional associative memory (BAM) network in computer assisted learning in chemistry

A computer assisted learning software based on a bi-directional associative memory (BAM) network was developed. The software was implemented to assist students in associating the names of the elements in the periodic table with their chemical symbols. The use of the BAM facilitates the analysis and interpretation of students' responses. The software package can be modified easily as an educational tool for other disciplines.