Cristian Cappello

Computational Fluid Dynamics (CFD) was applied to an intermittent anoxic/aerobic Moving Bed Biofilm Reactor (MBBR) operated under six different aeration intermittency cycles and dissolved oxygen concentration levels. Experimental results showed that most aeration cycles did not provide a sufficiently long anoxic phase to sustain effective denitrification, thereby limiting NOx removal efficiency. This behavior was not adequately captured by simulations performed using conventional biological models (BioWin), which rely on the assumption of complete mixing. In contrast, the CFD model implemented in ANSYS Fluent 2024 R2 enabled a detailed characterization of reactor hydrodynamics and the identification of several inefficiencies, including short-circuiting, back-mixing, and the presence of dead zones. Notably, the simulations revealed a pronounced asymmetric distribution of carriers within the reactor, with the majority accumulating along one side, leaving a significant fraction of the reactor volume largely unoccupied. Further analysis indicated that this phenomenon was caused by a design flaw—specifically, the asymmetric placement of the aerators—combined with an excessively high air injection flow rate.

Short-term water demand forecasting is a key issue for the management of smart water networks, particularly in the context of remote control and active regulation. This study analyses a real-world dataset of water demand coefficients, collected at 15 min intervals, from a municipality in Southern Italy serving approximately 73,000 inhabitants. The proposed model, based on Gaussian Process Regression (GPR) with a Rational Quadratic kernel (RQ), is compared with a statistical benchmark constructed using average patterns for each time slot by the application of the Gauss Distribution. The results show a reduction in RMSE and MAE and a better ability to track the daily dynamics of demand using the GPR approach.

Water Distribution System (WDS) pipe failures are one of the most critical issues in WDS management. In order to identify them, a machine learning approach was applied to eight years of geolocated data on pipe failures to establish priorities for WDS rehabilitation. District-level characteristics, such as network length, pressures, materials, population density, and temperature, were combined with a specific failure rate to account for differences in network size. A cost-sensitive classification approach minimized false negatives, ensuring that high-risk areas were correctly flagged. Among all models analyzed the best performance was achieved by Naive Bayes, which reliably predicted priority districts for proactive maintenance, supporting pipeline renewal strategies.

The residential minimum water demand characterisation is of fundamental importance for water distribution system management. During the minimum consumption, indeed, maximum pressures are on network pipes, and at the same time, tank levels rise. The water consumption analysis during the period of low demand and high pressure is thus of great interest for leakage estimation due to the increase in water loss with pressure. In order to contribute to the study of and forecast the daily minimum residential water demand, some probability distributions were tested by means of statistical inferences on a data set collected from different District Metering Areas (DMAs), showing that the stochastic minimum flow demand is defined by the Log-Normal (LN), Gumbel (Gu) and Log-Logistic (LL) distributions, as an extreme minimum value. With reference to the analysed DMAs, the parameters of such statistical distributions were estimated and the relationships are provided as a function only of the supplied users for different DMAs. The data were analysed with 1 h intervals of discretisation, with the aim of providing a useful guide to water utilities, which usually manage water distribution system data with such a resolution time. Indeed, once the minimum residential flow consumption at a 1 h interval was estimated as a function of the user number, by subtracting it to the inflow measured, it is possible to estimate the leakages rate at the DMA.

The residential minimum water demand characterization is of fundamental importance for water distribution systems management. During the minimum consumption, indeed, maximum pressures are on network pipes and at the same time tank levels rise. The legitimate water consumption analysis during period of low demand and high pressure is thus of great interest for leakages estimation due to the increase of the water loss with the pressure. Previous studies have tackled this issue with deterministic approaches, by considering the legitimate consumption as a percentage of the night measured inflow. In order to contributing to the legitimate minimum residential water demand characterization, probabilistic models have been developed to study and forecast the daily minimum residential water demand. Some probability distributions have been tested by means of statistical inferences on data set collected from different District Metering Areas (DMAs), showing the stochastic minimum flow demand is described by the log-Normal (LN), Gumbel (Gu) and Log-Logistic (LL) distributions, as an extreme minimum value. The latter is of particular interest being the LL probability density function analytically integrated. With reference to the analysed DMAs, parameters of such statistical distributions have been estimated and relationship are provided in function only of the supplied users for different DMAs. Data have been analysed with 1 h interval time of discretization, with the aim of providing an useful guide to the Water Utilities which usually are managing water distribution systems data with such resolution time. Indeed, once the legitimate minimum residential flow consumption at 1h interval time is estimated in function of the users number, by subtracting it to the inflow measured, it is possible to estimate leakages rate at the DMA.