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This research explores the strategic optimization of secondary chlorination in water distribution systems (WDSs), in order to enhance the efficiency of disinfection while mitigating odor and operational costs and promoting sustainability in water quality management. The methodology integrates EPANET simulations for water hydraulic and quality modeling with a deep belief network (DBN) within the deep learning framework for accurate chloric odor prediction. Utilizing the non-dominated sorting genetic algorithm-II (NSGA-II), this methodology systematically balances the objectives of chloride dosage and chloramine formation. It combines a chloric odor intensity assessment, a multi-component kinetic model, and dual-objective optimization to conduct a comparative analysis of case studies on secondary chlorination strategies. The optimal configuration with five secondary chlorination stations reduced chloric odor intensity to 1.20 at a cost of USD 40,020.77 per year in Network A while, with eight stations, chloric odor intensity was reduced to 0.88 at a cost of USD 71,405.38 per year in Network B. The results demonstrate a balanced trade-off between odor intensity and operational cost on one hand and sustainability on the other hand, highlighting the importance of precise chlorine management to improve both the sensory and safety qualities of drinking water while ensuring the sustainable use and management of water resources. [ABSTRACT FROM AUTHOR]

This study introduces a novel deep learning framework for detecting leakage in water distribution systems (WDSs). The key innovation lies in a two-step process: First, the WDS is partitioned using a K-means clustering algorithm based on pressure sensitivity analysis. Then, an encoder–decoder neural network (EDNN) model is employed to extract and process the pressure and flow sensitivities. The core of the framework is the PP-LCNetV2 architecture that ensures the model's lightweight, which is optimized for CPU devices. This combination ensures rapid, accurate leakage detection. Three cases are employed to evaluate the method. By applying data augmentation techniques, including the demand and measurement noises, the framework demonstrates robustness across different noise levels. Compared with other methods, the results show this method can efficiently detect over 90% of leakage across different operating conditions while maintaining a higher recognition of the magnitude of leakages. This research offers a significant improvement in computational efficiency and detection accuracy over existing approaches. [ABSTRACT FROM AUTHOR]

This study introduces an innovative approach for leak detection in water distribution systems (WDSs), integrating three-order embedding, k-means clustering, and long short-term memory (LSTM) networks, with pressure-sensitive analysis techniques. This comprehensive methodology segments the network into distinct partitions, utilizes simulated leak events to train the deep learning networks, and establishes a sophisticated model for accurately identifying leak partitions. This approach generates a leak dataset by adjusting water demands, which could effectively pinpoint the leaks in a specific partition by leveraging both the pressure sensitivity and spatial coordinates of nodes, allowing for the elimination of the need for manual work and precise identification of leaks in targeted areas. Through the analysis of two case studies, the model demonstrates its ability to effectively pinpoint potential leak partitions, significantly enhancing operational efficiency and reliability in managing the complex problems of urban water resource management. This approach not only optimizes leak detection but also paves the way for advanced, data-driven strategies in WDSs, ensuring sustainable and secure water distribution in urban settings. [ABSTRACT FROM AUTHOR]

In view of the needs of secondary water quality evaluation and intelligent operation and maintenance management, the data quality evaluation of two major secondary water supply online monitoring indicators (residual chlorine and turbidity) was carried out, and the variance analysis and covariance analysis of the impact factors of the water quality of the four major secondary water supply were proposed, and the water quality evaluation method of secondary water supply online monitoring based on K-means clustering method and entropy value method was proposed, which was applied in the secondary water supply management system of L City, and the evaluation results provided an important reference for the secondary water supply manager. Evaluation methods can be widely applied in the water quality management of secondary water supply. [ABSTRACT FROM AUTHOR]

The increasing production of excess sludge has become a serious problem that plagues the development of Chinese sewage treatment industry. Ultrasonic technology (high-intensity ultrasound) has attracted the attention of researchers because of its advantage of reducing sludge production from the source. The principle and main influencing factors of ultrasonic sludge process reduction technology are introduced. The current research status of improving the sludge process-reduction rate and reducing energy consumption was reviewed, including the combination of ultrasound and physicochemical method or traditional sewage biological treatment process. There is a contradiction between the sludge reduction and deterioration of effluent quality in the combination of ultrasound and traditional wastewater biological treatment process. Combining the advantages of low-intensity ultrasound to increase the biological activity of sludge and promote the shortcut nitrification process, a method of new wastewater biological treatment process (partial nitrification-denitrification (PND) and partial nitrification/partial denitrification-anammox (PN/PD-A)) coupled with different intensity ultrasound was summarized and its technical advantages were discussed. [ABSTRACT FROM AUTHOR]

In this work, the degradation performance of Fe2+/PAA/H2O2 on three typical pollutants (reactive black 5, ANL, and PVA) in textile wastewater was investigated in comparison with Fe2+/H2O2. Therein, Fe2+/PAA/H2O2 had a high removal on RB5 (99%) mainly owing to the contribution of peroxyl radicals and/or Fe(IV). Fe2+/H2O2 showed a relatively high removal on PVA (28%) mainly resulting from ·OH. Fe2+/PAA/H2O2 and Fe2+/H2O2 showed comparative removals on ANL. Additionally, Fe2+/PAA/H2O2 was more sensitive to pH than Fe2+/H2O2. The coexisting anions (20–2000 mg/L) showed inhibition on their removals and followed an order of HCO3− > SO42− > Cl−. Humic acid (5 and 10 mg C/L) posed notable inhibition on their removals following an order of reactive black 5 (RB5) > ANL > PVA. In practical wastewater effluent, PVA removal was dramatically inhibited by 88%. Bioluminescent bacteria test results suggested that the toxicity of Fe2+/PAA/H2O2 treated systems was lower than that of Fe2+/H2O2. RB5 degradation had three possible pathways with the proposed mechanisms of hydroxylation, dehydrogenation, and demethylation. The results may favor the performance evaluation of Fe2+/PAA/H2O2 in the advanced treatment of textile wastewater. [ABSTRACT FROM AUTHOR]