凌庄子水厂保水堰流量系数试验

凌庄子水厂蓄水池进水口处有一保水堰,为非标准薄壁堰,不能使用已有堰流公式对其过流量进行准确计算。为了得出较为精确的过流流量,按照重力相似准则制作几何比尺为1∶5的模型进行试验研究。在已有自由出流公式的基础上,对自由出流流量系数进行修正并对淹没情况下流量变化过程进行研究。对该非标准堰自由出流流量系数的实测值与经验值进行分析比较,发现堰板槽降低了实际自由出流过流能力。淹没出流的流量系数主要与下游尾水位有关,试验中形成的淹没式堰流受实际堰型尺寸影响,下游尾水位和堰上水位近似相等,不完全适用已有淹没出流流量公式,通过试验给出了修正淹没系数随h/p的变化关系。结果表明利用堰前、堰后水位初步计算过流流量是可行的,可为该工程提供参考,也可为实际工程中非标准矩形堰的流量计算提供思路。     

“灰绿”协同措施对银川市合流制溢流污染的影响EI北大核心CSCD

针对合流制管网系统在雨天溢流污染严重,造成城市水体黑臭现象的问题,以银川市某高密度城区合流制管网系统为例,基于SWMM模型,在短历时设计降雨和长历时设计降雨两种条件下,模拟分析了合流制溢流(CSO)调蓄池、雨污管道混错接改造、绿化带海绵化改造等“灰绿”协同措施对CSO污染的影响。结果表明:CSO调蓄池、雨污管道混错接改造、绿化带海绵化改造及“灰绿”措施结合4种方案在短历时、长历时设计降雨条件下,随着降水量的增加,溢流水量及溢流污染物负荷均增加,溢流削减率均逐渐减小,其中“灰绿”措施结合方案对溢流污染的削减效果最为显著;重现期小于5 a时,溢流水量削减率与溢流污染物负荷削减率基本达到80%;降雨条件为中雨时,污染物负荷削减率基本达到75%;重现期为20 a时,溢流水量削减率及TSS、COD、TP、NH^(+)_(4)-N负荷削减率分别达到64%、70%、70%、70%、70%;降雨条件为大雨时,溢流水量削减率及TSS、COD、TP、NH^(+)_(4)-N负荷削减率分别达到28%、32%、26%、31%、33%。     

阶梯溢流坝面坡度对一体化消能工水力特性的影响

为探求高水头、大单宽流量下坝面坡度对一体化消能工水力特性的影响,以阿海水电站为原型,采用三维k-ε双方程紊流模型,引入水气两相流VOF计算方法,利用几何重建格式来迭代生成自由水面,对1∶0.80、1∶0.75、1∶0.65三种阶梯面坡比进行数值模拟研究。结果表明:①最大负压值均位于首级阶梯立面凸角下1/4附近,并随坡度增加而增大。坡度为56.98°时,最大负压值为61.02 kPa,超过了6×9.81 kPa。②水流空化数在宽尾墩水舌出口位置出现最小值,空化数随坡度变陡而减小。坡度为56.98°时,空化数最小为0.358。坡度为51.34°时,空化数最大,为0.381。③随着阶梯溢流坝坝面坡度变缓,消力池最大临底流速增大。当坡度为51.34°时,消力池最大临底流速最大,达到26.84 m/s,超过了25 m/s,易发生冲磨破坏。当坡度为56.98°时,消力池最大临底流速最小,为24.00 m/s。消力池尾坎前最大临底流速随坡度增加而减小,坡度为56.98°时最小,为9.63 m/s;坡度为51.34°时,消力池尾坎前最大临底流速最大,为9.96 m/s。④坡度的变化对一体化消能工消能率的影响不大,坡度从51.34°增加到56.98°,消能率只提升0.15%。     

Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

Information and communication technologies combined with in-situ sensors are increasingly being used in the management of urban drainage systems. The large amount of data collected in these systems can be used to train a data-driven soft sensor, which can supplement the physical sensor. Artificial Neural Networks have long been used for time series forecasting given their ability to recognize patterns in the data. Long Short-Term Memory (LSTM) neural networks are equipped with memory gates to help them learn time dependencies in a data series and have been proven to outperform other type of networks in predicting water levels in urban drainage systems. When used for soft sensing, neural networks typically receive antecedent observations as input, as these are good predictors of the current value. However, the antecedent observations may be missing due to transmission errors or deemed anomalous due to errors that are not easily explained. This study quantifies and compares the predictive accuracy of LSTM networks in scenarios of limited or missing antecedent observations. We applied these scenarios to an 11-month observation series from a combined sewer overflow chamber in Copenhagen, Denmark. We observed that i) LSTM predictions generally displayed large variability across training runs, which may be reduced by improving the selection of hyperparameters (non-trainable parameters); ii) when the most recent observations were known, adding information on the past did not improve the prediction accuracy; iii) when gaps were introduced in the antecedent water depth observations, LSTM networks were capable of compensating for the missing information with the other available input features (time of the day and rainfall intensity); iv) LSTM networks trained without antecedent water depth observations yielded larger prediction errors, but still comparable with other scenarios and captured both dry and wet weather behaviors. Therefore, we concluded that LSTM neural network may be trained to act as soft sensors in urban drainage systems even when observations from the physical sensors are missing.     

袁河新泉乡段梯级溢流堰水动力模拟及侵蚀分析

为了分析梯级溢流堰对袁河新泉乡段河道行洪及河床抗冲的影响,优化河道治理工程中溢流堰布置,建立了将拦河溢流堰作为闭边界条件的二维水动力数值模型,采用非结构化网格的有限体积法,对比分析了十年一遇洪水情况下梯级溢流堰建设前后河道水动力、河道水位、流场沿程变化特征,讨论了溢流堰布置对河道防洪及冲刷的影响。结果表明,在十年一遇洪水情况下溢流堰会抬升河道水位,平均增加0.49 m,满足河道行洪要求;梯级溢流堰使河道整体形成人工“阶梯深潭”体系,河道流速在空间上发生明显的异质性,堰上流速下降幅度较堰下流速大;水流发生急流与缓流之间交替,水流紊动强烈,加剧了水流能量损失,有效降低流速,减轻了河道冲刷,有利于稳定河岸结构。     

煤泥水浓度偏高原因分析及处理技术

煤泥水浓度是影响脱介效果和精煤产品质量的重要因素,在指导分选生产中起着重要作用。以山西焦煤西山煤电屯兰选煤厂煤泥水浓度偏高情况为例,分析了高频筛回收效果差、絮凝剂添加不科学、浓缩机溢流大、重介系统筛板、筛篮跑粗等因素是影响煤泥水浓度偏大的主要原因。针对上述问题提出控制浓缩机溢流电流在280~310 A,絮凝剂配制成0.1%溶液;降低设备底煤厚度等技术方案。改造后,选煤厂煤泥水质量浓度低于13 g/L,提高产品产率,实现节能降耗,提高选煤厂的效率。