Graduate Research Assistant Marquette University, Wisconsin, United States
Abstract Submission: Sanitary sewer systems are subject to infiltration and inflow (I/I) from rainfall events where unwanted stormwater flows into the sewer network. This can cause sewage backups and overload at wastewater treatment plants, resulting in sanitary sewer overflows or basement backups that pose serious risks to human and environmental health. Determining sources of I/I requires extensive monitoring that is either spatially limited to discrete points that must infer upstream processes or subject to dry weather methods (CCTV, smoke testing, etc.) that cannot capture I/I during peak events. This study aims to overcome these shortcomings through a novel approach to monitoring the volume of I/I entering sewer systems through Distributed Temperature Sensing (DTS) that can estimate flow rates at 1-m increments throughout a sewer system. In addition, this study seeks to improve the accuracy of previous approaches by optimizing the I/I temperatures values to best fit the observed hydrograph downstream of the monitored sewer. This was done through a mathematical optimization model created using the scipy library. The results produced an RMSE of 12 gallons per minute between the modeled DTS I/I hydrograph compared to an observed I/I hydrograph. In addition, the optimized I/I temperature exhibited a similar behavior to observed temperatures of infiltrated stormwater in other studies that have comparable characteristics to the monitored site. Overall, the outcomes of this approach may be able to capture sewer flows at unmatched spatial and temporal scales, thereby improving accuracy and reducing costs of determining I/I sources.
