基于光纤分布式测温的污水管道入流识别方法研究

针对排水管网物探检测需断水操作和无法对管道内部实时探查的问题,建立了一种基于光纤分布式测温的污水管道入流诊断方法,实现了无干扰条件下污水管道污水和雨水入流实时监控.采用感温光缆对选择的试验管段开展了时空高精度观测(空间精度1m、时间精度1min),获得了超过118万个管道水温数据.基于解析出的管道水温时空图像,提出了基于水温温差背景噪声值的入流定位方法;确定出无外部入流影响时管道水温空间和时间温差的背景噪声值分别约为±0.2℃和±0.5℃.采用水温图像降噪处理方法,自动识别出污水管道的旱天污水接入和雨天雨水接入事件,与现场调查结果一致.因此,该方法对污水管道入流的动态识别定位具有可靠性.

A method on sewer inflow identification using fiber-optic distributed temperature sensing

To overcome the shortcomings of physical inspection that needs prerequisite pipe draining and also can not afford real-time in-pipe monitoring, a sewer inflow detection method based on fiber-optic distributed temperature sensing (FDTS) was developed in this study, enabling non-disrupted timely monitoring of sewage and rainfall inflow into sewers. A temporal-spatial high-resolution monitoring (i.e., 1m interval in spatial scale and 1min interval in temporal scale) was performed using FDTS for the studied sewer reach, and over 118million in-pipe water temperature data was obtained. With interpreted in-pipe temporal-spatial water temperature mapping, a sewer inflow identification method based on the background noise of water temperature change at spatial and temporal scale was presented, and the determined spatial and temporal background noise levels at a condition free of external sewer inflow disruption were ±0.2℃ and ±0.5℃, respectively. By a denoising treatment method for water temperature mapping, the dry-weather sewage discharge and wet-weather rainfall inflow into sewers were identified automatically, which is in line with on-site investigated results. Therefore, the developed method is reliable for dynamic sewer inflow identification and location.