This paper presents the design and development of a digital twin to diagnose leaks in water distribution networks. The digital twin allows for the remote operation of the hydraulic system’s actuators using embedded microcontrollers integrated with Internet of Things (IoT) capabilities. Pressure head and flow rate measurements are received online in the operator interface, and hydraulic simulations are performed with a well-calibrated EPANET model of the hydraulic system to estimate the pressure head at nodes without sensors. A genetic algorithm was designed to detect and estimate the size of the leaks online. Different experiments were carried out to validate the online application of the method based on the digital twin and under a multi-leak event.

This paper proposes a methodology based on a genetic algorithms (GA) to calibrate the parameters of a chlorine decay model in a water distribution system (WDS). The proposed methodology first contemplates that a GA is implemented using historical measurements of chlorine concentration at some sensed nodes to calibrate the unknown values corresponding to both the bulk and wall reaction coefficients. Once both parameters are estimated, the optimal-fit chlorine decay model is used to predict the decay of chlorine concentration in the water at each node for any concentration input at the pumping station. Then, a second GA-based algorithm is implemented to obtain the minimal chlorine concentration needed at the input to ensure that every node in the system meets the official normativity requirements for free chlorine in a WDS. The proposed methodology performed satisfactorily for a WDS simulated in EPANET with a GA implemented in MATLAB, both for the estimation of the reaction coefficients and the optimization of the required chlorine concentration at the input. Simulation results illustrate the performance of the proposed algorithm.

This paper presents a pressure management technique for the reduction of leaks considering as a case study a branched water distribution system. The proposed technique is based on the detection and location of the leak using a genetic algorithm (GA) and pressure control using a Linear Quadratic Regulator (LQR). The validation of the proposed method uses measured pressure and flow data from a laboratory-scale water distribution system and its dynamic model.

This paper proposes a model-based methodology to estimate multiple nodal demands by using only pressure and flow rate measurements, which should be recorded at the inlet of the distribution system. The algorithm is based on an array of multiple extended Kalman filters (EKFs) in a cascade configuration. Each EKF functions as an unknown input observer and focuses on a section of the pipeline. The pipeline model used to design the filters is an adaptation of the well-known rigid water column model. Simulation and experimental tests on standardized pipeline systems are presented to demonstrate the proposed method’s effectiveness. Finally, for the case of the experimental validation, both steady-state and variable input conditions were considered.