Because of the large computational burden associated with the direct assessment of reliability, the indirect indices of reliability have recently received more attention in the framework of water distribution system design. Two new energy-related indices for reliability evaluation (i.e., available power index and pipe hydraulic resilience index) are developed in this paper. The performance of these new indices is evaluated and compared with that of four existing indices (three other energy-related indices—i.e., resilience index, network resilience index, and modified resilience index—and the entropy-based method, i.e., diameter-sensitive flow entropy) according to the following two-step methodology. In the first step, the application of the multiobjective optimization makes it possible to determine optimal network configurations that trade-off the installation cost (to be minimized) against the generic indirect reliability index (to be maximized). In the second step, the performance of the optimal solutions in terms of explicit reliability assessment is examined under conditions in which the original network is perturbed by applying demand variations and random pipe failures to account for future operating uncertainties. The Hanoi and the Fossolo benchmark networks are used as case studies. The results obtained show that energy-based indices yield an overall superior estimate of reliability in comparison with the diameter-sensitive flow entropy. Furthermore, the new indices show some advantages in the evaluations performed under demand and pipe failure uncertainties.

Biological trickling filters are widely used for sewage treatment. This study models a biological trickling filter based on an anaerobic–anoxic–oxic process (A2O–BTF), established by a combination of aerobic and anaerobic technology. The performance and operational parameters were analyzed using Sumo, a commercially available wastewater treatment process (WWTP) simulation software. The wastewater treatment performance of the anaerobic–anoxic–oxic process biological trickling filter (A2O–BTF), the conventional three-stage biological trickling filter (Three-Stage–BTF), and the single-stage biological trickling filter (Single–BTF) was compared, which indicated the higher performance of A2O–BTF in terms of COD, TN, NH₃-N, and TP removal. The operational parameters of A2O–BTF were optimized by Sumo simulation software, and the results showed that the removal efficiency of pollutants was increased by raising the temperature to the range of 13.94–21.60 °C. The dissolved oxygen (DO) in the aerobic reactor enhanced removal efficiency under a saturation concentration of 2.2–2.6 g O₂/m³. In addition, the optimization of the reflux ratio promoted the removal efficiency of the pollutants, indicated by the maximum removal efficiency of COD and TN, achieved at the reflux ratio of 2.25, and that of NH₃-N and TP, achieved at a reflux ratio of 0.75. This study provides a proof-in-concept demonstration that software modeling can be a useful tool for assisting the optimization of the design and operation of sewage treatment processes.

The detection of leaks in water distribution systems (WDS) has always been a major concern for urban water supply companies. However, the performance of traditional leak detection classifiers highly depends on the effectiveness of handcrafted features. An alternative method is to use a convolutional neural network (CNN) to process raw signals directly to obtain deep representations that may ignore prior information about the leakage. The study proposes a novel approach to leak detection in WDS using ground acoustic signals, and demonstrates the effectiveness of combining handcrafted features and deep representations using a pseudo-siamese convolutional neural network (PCNN) model. Mel frequency cepstral coefficient (MFCCs) are selected as additional handcrafted features to traditional time- and frequency-domain (TFD) features. Based on the results of the model performance evaluation, the optimized PCNN model performs better than other methods, with an accuracy of 99.70%. A quantitative analysis of the PCNN demonstrates the effectiveness of handcrafted features and deep representations. Model visualization and interpretation analysis show that feature fusion occurs in the feedforward of the PCNN, hence improving the model’s performance. The present work can effectively support the development of novel intelligent leak detection equipment for WDS.