Browsing by Autor "Daniele Laucelli"

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Effects of Minimum Cost Optimization in Water Distribution Networks on Residual Chlorine and Trihalomethanes Dynamics
(American Society of Civil Engineers, 2025) Laura González; Sergio Martín Vicente Serrano; Valeria Rodríguez; Daniel Álvarez; María Alejandra González; Jaime Plazas‐Tuttle; Daniele Laucelli; Juan Saldarriaga
Water distribution networks (WDNs) are designed under parameters and restrictions that guarantee compliance with hydraulic and water quality conditions in the system. The optimized design of WDNs ensures a lower cost for the network, maintaining an adequate supply of the demand. The optimization process leads to the reduction of some diameters compared with those in nonoptimal networks, thus changing the hydraulic behavior and hence affecting the water quality performance. This study aims to evaluate the relationship between optimal and nonoptimal WDN designs concerning residual chlorine levels and the formation of disinfection byproducts, specifically total trihalomethanes (TTHMs). Seventeen networks were optimized using the optimal power use surface, and for each one of them four nonoptimal alternatives were generated using genetic algorithms. Residual chlorine and TTHM concentrations were analyzed in the different configurations of the networks according to their geometrical characteristics. Results indicate that optimized networks exhibit reduced chlorine consumption, consequently leading to lower TTHM formation. In particular, the optimized design achieved a reduction of up to 20.6% in chlorine consumption compared to the more expensive alternative. Future work will focus on evaluating water quality dynamics, considering event-based operational changes.
Using Symbolic Machine Learning to Assess and Model Substance Transport and Decay in Water Distribution Networks
(Research Square (United States), 2023) Daniele Laucelli; Laura Enríquez; Juan Saldarriaga; Orazio Giustolisi
Abstract The evaluation of the substance concentration at each node of a water distribution network can be performed by integrating the advective diffusion in the network domain using a decay formulation and a Lagrangian scheme for differential equations. The kinetics of the substance decay can be formulated using a specific reaction order. The first order corresponds to a decay rate with a constant reaction rate, while higher order captures the reaction rate’s dependency on substance concentration. The aim of the present work was to discover the intrinsic mechanism of the substance transport in water distribution networks using the symbolic machine learning. We used the strategy named Evolutionary Polynomial Regression. To this purpose, we consider the chlorine transport, without imparing the generality of the procedure, assuming the first or second order for kinetic model. We demonstrated, using one real network and two test networks, that the concentration at each node of the network mainly depends on the substance decay along the shortest path(s) between the source and each node. Additionally, the symbolic machine learning allowed discovering the relationship between the source concentration and the residual one at each node of the network with a unique formula based on kinetic reaction model structure and its water age, possibly surrogated by travel time in the shortest path(s) to make the prediction faster.