Assessment of detection limits of fiber-optic distributed temperature sensing for detection of illicit connections

Abstract

Distributed Temperature Sensing (DTS) with fiber-optic cables is a powerful tool to detect illicit connections in storm sewer systems. High frequency temperature measurements along the in-sewer cable create a detailed representation of temperature anomalies due to illicit discharges. The detection limits of the monitoring equipment itself are well-known, but there is little information available on detection limits for the discovery of illicit connections, as in sewers mixing and attenuation also plays an important role. This paper describes the results of full-scale experiments aiming to quantify the detection limits for illicit connections under various sewer conditions. Based on the results, a new monitoring setup for (partially) filled sewer conduits has been proposed.

Using Distributed Temperature Sensing (DTS) for Locating and Characterising Infiltration and Inflow into Foul Sewers before, during and after Snowmelt Period

“…A study focused on the detection limits of DTS [28] found that due to the noise of the measurements, direct inflows with smaller volumes and higher temperature differences were more difficult to detect than inflows with larger volumes and smaller temperature differences. The temperature of I/I is affected by a number of factors such as air, soil, ground surface and roof temperature as well as the length of tributary pipes [28,29].…”

“…A study focused on the detection limits of DTS [28] found that due to the noise of the measurements, direct inflows with smaller volumes and higher temperature differences were more difficult to detect than inflows with larger volumes and smaller temperature differences. The temperature of I/I is affected by a number of factors such as air, soil, ground surface and roof temperature as well as the length of tributary pipes [28,29]. Even within the duration of the shortest rain event (#14) of 2.45 h, the air temperature changed from 21.2 • C to 14.9 • C, while the wastewater temperature was relatively stable: 7.6 ± 0.2 • C in the upstream end and 8.4 ± 0.2 • C in the downstream end of the main sewer section.…”

Sewer asset management – state of the art and research needs

“…Recent research focuses on the application and further development of Distributed Temperature Sensing (DTS), tracer methods and electro tomography. DTS can be used to detect and locate infiltration (Hoes et al 2009;Nienhuis et al 2013), providing, however, only a rough estimate. The placement of a fibre-optic cable into the pipeline bed to monitor high-frequency temperature differences because of exfiltration is yet to be tested in urban drainage.…”

Performances and Uncertainty of Temperature Methods for Illicit Infiltrations and Inflows Assessment in Stormwater Sewers

“…At first, unknown variables are estimated from measured data through eqns (2) and 3for Case A1, eqns (7) and (8) for Case B1 and eqns (13) and (14) for Case C1:…”

“…The first step consist, most of the times, in the analysis of the night-time minimum of dry weather flows; after that, a number of different methodologies and technologies can be applied for the next level of the survey, depending on site conditions and resources availability: visual inspections and progressive sampling at manholes [11], smoke test and dye test [12], closed-circuit television camera (CCTV) inspections, Infra-Red camera [13], stable isotopes, polluting flows analysis, punctual measures of temperatures. In last decades, Distributed Time Sensing (DTS) technique has been successfully applied to locate I&I in sewers [14], [15]; it is based, too, on temperature measurements, but continuous in time and space; it allows to identify illicit flows for large distances without requiring access to private properties. Each one of these methodologies present some drawbacks: DTS requires cable installation into the sewer, dye and smoke tests are time consuming, visual inspections are -by definition -sensitive to human subjectivity, while methods based on sample analysis may be quite costly [16]; sometimes, a solution to improve the cost-effectiveness of a survey turns out to be the joint use of different technologies [17].…”