Identification of the inflow source in a foul sewer system through techniques of inverse modelling

Abstract

Infiltration and illegal inflow into foul sewer systems can cause different problems such as a decrease in the performance of treatment plants, the surcharge of pipelines and more frequent overflows, which cause negative impacts on the environment. Water companies are increasingly been driven to address these problems by reducing infiltrations and identifying the sources of illegal inflows. Overall, the traditional techniques applied in these cases are expensive and time consuming and many times only partially efficient. Examples are the use of CCTV inspections, smoke tests and the installation of a large set of sensors to collect continuous data such as flow rates, water levels, temperature or concentrations of pollutants. The aim of this study is to apply two types of inverse numerical techniques to identify the source location of illegal inflows into wastewater systems based on information collected at the outlet of the drained basin and a calibrated numerical model of the sewer network. In this work, the numerical model is developed using the Storm Water Management Model (SWMM) software distributed by the Environmental Protection Agency (USA). We considered a realistic foul sewer system with known dimensional and hydrological characteristics. Synthetic case studies are set up to test the inverse approaches. Assuming a hypothetical rainfall event and an illegal inflow released at a certain location in the sewer system, the numerical model is run forward to obtain the flow hydrograph at the network outlet. This information is then used as available observations to perform the inverse modelling. The first investigated technique is an artificial neural network (ANN) of the feed-forward type. It will be trained to recover the inflow source using the simulation results of SWMM driven by a large set of rainfall events and inflows located at different positions in the sewer network. Once trained, the ANN will be used to identify the location of the inflow based the observed flood wave. The second procedure derives from Kalman filter techniques: the Ensemble Smoother with Multiple Data Assimilation (ES-MDA). Also in this case, the method, starting from the known rainfall event and the observed flow hydrograph, is used to locate the inflow source. In addition to the results of the synthetic case obtained by means of the two procedures, the field applicability to real case studies will be discussed.