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| Quantitative identification of inflow and infiltration of sanitary sewer system based on coupling simulation |
Hong-hao HU1( ),Xiu-juan LI2,Jun-feng YU2,Qing-zhou ZHANG2,Jing-qing LIU1,*() |
1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2. Binhai Industrial Technology Research Institute , Zhejiang University, Tianjin 300457 |
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Abstract A recognition model based on the calibration of soil moisture content, runoff-yield and instantaneous unit hydrograph was proposed to quantify rainfall derived infiltration and inflow (RDII) accurately and effectively in sanitary sewer networks. The effects of different rainfall intensity conditions on the calibration of model parameters were also considered. The model was applied to the load analysis of the infiltration process in the upstream of two sections of the sewage pipe network in a rural area of H city. The exponential moving average (EMA) was used to process the calibration results of 15 rainfall events. The optimal parameter sets for heavy, medium and light rainfall events were obtained. The other 6 rainfall events were used to validate model with Nash efficiency coefficients from 0.65 to 0.86. The results showed that the RDII accounts for 5%~22% of the precipitation in the study area, of which the inflow accounts for 3~9% and the infiltration accounts for 1%~13%. As the rainfall intensity getting high, the proportion of infiltration increased gradually. Different from high cost pipeline exploration, the proposed identification model makes full use of rainfall data can be used for quantitaive identification of RDII in sanitary sewer networks.
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Received: 08 December 2021
Published: 02 December 2022
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Corresponding Authors:
Jing-qing LIU
E-mail: johnkeatinghhh@zju.edu.cn;liujingqing@zju.edu.cn
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基于耦合模拟的污水管网入流入渗定量识别
为了准确有效地识别降雨引起的污水管网入流和入渗，提出了一个基于土壤蓄水量、产流过程和单位过程线耦合率定的识别模型，并且考虑到不同降雨强度工况对模型参数率定的影响. 模型被应用于H市乡镇地区污水管网2个断面上游的入流入渗过程的负荷分析，采用指数移动平均法（EMA）对15场降雨事件的校准结果进行处理，得到大、中、小3类降雨的最优参数集，用另外6场降雨来验证模型的有效性，纳什效率系数为0.65~0.86. 结果表明：研究区域内入流入渗占区域总降雨量的5%~22%，其中入流量占比为3%~9%，入渗量占比为1%~13%，随着降雨量的增大，入渗占比逐渐增加. 不同于成本较高的管道勘探，所提出的识别模型充分利用降雨数据和管网水力监测数据，可用于污水管网入流入渗的定量识别.
关键词：
污水管网,
入流入渗,
定量识别,
耦合模型,
遗传算法
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