Department Urban Water Management

Collecting precise real-time information on urban drainage system performance is essential to identify, predict and manage critical loading situations, such as urban flash floods and sewer overflows. Although emerging low-power wireless communication techniques allow efficient data transfers with great above-ground performance, for underground or indoor applications a large coverage range is difficult to achieve due to physical and topological limitations, particularly in dense urban areas. In this paper, we first discuss range limitations of the LoRaWAN standard based on a systematic evaluation of a long-term operation of a sensor network monitoring in-sewer process dynamics. Analyses reveal an –. on average – five-fold higher data packet loss for sub-surface nodes, which steadily grows with increasing distance to the gateway. Secondly, we present a novel LPWAN concept based on the LoRa. technology that i) enhances transmission reliability, efficiency and flexibility in range-critical situations through meshed multi-hop routing, and ii) ensures a precise time-synchronization through optional GPS or DCF77 long-wave time signaling. We thirdly illustrate the usefulness of the newly developed concept by evaluating the radio transmission performance for two independent full-scale field tests. Test results show that the synchronous LoRa mesh network approach clearly outperforms the standard LoRaWAN technique with regard to the reliability of packet delivery when transmitting from range-critical locations. Hence, the approach is expected to generally ease data collection from difficult-to-access locations, such as underground areas.

Mit der fortschreitenden Digitalisierung gilt es heute als Stand der Technik, Messdaten über das Abwassersystem zu erheben, auszuwerten und zu archivieren. Wie der Wert von Messdaten für Betrieb, Planung und Leistungsüberprüfung ausgeschöpft werden kann, ist jedoch unklar. Um einen einheitlichen Einsatz von Messtechnik, z.B. an Regenüberlaufbecken, sowie einen guten Umgang mit erfassten Messdaten in der Schweizer Praxis zu schaffen, sind Anpassungen auf organisatorischer Ebene notwendig.

We present a novel stochastic model for quantifying gross solids (GS) physical disintegration under varying turbulent flow conditions and used a unique experimental setup for model calibration and validation. The stochastic deterioration model predicts faeces size evolution over time. It conceptually entails the two main processes of solid fragmentation, namely breakage and erosion. Model parameters were calibrated on synthetic faeces and validated with real human ones. A cylindrical reactor was used, where turbulent flow was forced by an array of water jets and the physical disintegration of the faeces was monitored using a high speed camera. Image analysis of breakage experiments obtained under backlight illumination allowed determination of the evolution of the solids’ size over time. The flow field in the reactor was characterised by particle image velocimetry (PIV) using tracer particles seeded into the water. We found different disintegration behaviours depending on turbulence intensity and water content of the solid. In conditions of low shear stress, dense solids hardly disintegrated. Generally, the model predictions mirrored the broad range in the solids disintegration rate imparted by the high variability in flow conditions and in solids characteristics. It is expected that, similar to our experiments, also in real sewer systems both flow conditions and solid characteristics are highly variable and the stochastic model can be tailored to capture this variability. We thus anticipate that the model can be integrated into existing sewer models predicting sewer flows and solids’ movement. From these, shear stress, flow velocities and transport of individual solids can be inferred. The integration of the present solids disintegration model may provide better predictions of hot-spots for solids accumulation and blockages in sewers.