Establishing an Experimental and Simulation Interface for Online Monitoring and Modeling of Bacterial Growth in Water Distribution Systems
World Environmental and Water Resources Congress : Adaptive Planning and Design in an Age of Risk and Uncertainty (2022)
Water distribution systems (WDSs) function to deliver high-quality water in major quantities. While standard water quality parameters are monitored at waterworks, it is still a challenge to monitor water quality in the WDS network itself. Only hydraulic parameters are frequently monitored and modeled in drinking water networks in Germany. Moreover, the majority of German drinking water utilities does not disinfect when the product leaves the waterworks. This is also common practice in Northern European countries. It is thus important to monitor specific organic and bacteriological water quality parameters which define the system state. This study develops an experimental and simulation integrated framework for continuously monitoring and simulating selected organic and bacteriological water quality parameters, so abnormal deviations in water quality behavior can be detected and responded to in real time. A simple reproducible bacteria regrowth model was taken to initialize the validation of experimental values in a water quality model simulation. Batch experiment measurements from a flow cytometer are analyzed to establish an interface between laboratory values and water quality simulations. Monod kinetics are utilized to describe the bacterial growth rate according to the respective substrate for modeling the bulk species in the water network. Experimental values are incorporated in the simulations for validation. The simulation of bacterial growth is conducted firstly on a network model of a real-life test bed and on various selected distribution system models of different sizes and complexities. First results of the water quality simulations show a successful transition of experimental analysis into water quality simulations and give a promising outlook for developing an online-monitoring and prediction methodology for detecting water quality anomalies efficiently in real time.