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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]

Highlights: MPUV/chlorine increased the chlorine demand and DBPFP of benzoic acid and nitrobenzene. MPUV/chlorine showed no further activation on active DBP precursors. MPUV/chlorine at pH 6 showed greater impact on precursors' DBPFP than at pH 8. MPUV/chlorine increased the BSF of THMs in the presence of bromine. Linear correlation was observed between changes in precursors' chlorine demand and DBPFP. UV/chlorine advanced oxidation process (AOP), as a potential alternative to UV/H2O2 in water treatment, may pose a potential risk of increased disinfection by-product (DBP) formation and is of great concern. In this paper, seven benzene derivatives, containing two chlorine-inert and five chlorine-active compounds, were selected as typical model DBP precursors, and the effects of medium pressure UV/chlorine (MPUV/chlorine) on their chlorine demand and DBP formation potential (DBPFP) were evaluated. The results showed that MPUV/chlorine could significantly increase the chlorine demand and DBPFP of the two inert precursors. For the four slow but active DBP precursors, MPUV/chlorine may accelerate their short-term DBP formation, whereas it showed an insignificant effect or even reduced their chlorine demand and DBPFP. For the only fast and active DBP precursor, MPUV/chlorine showed an insignificant effect on its short-term DBP formation or DBPFP. The overall effect of MPUV/chlorine was more significant at pH 6 than at pH 8. In the presence of Br−, MPUV/chlorine significantly increased the bromine substitution factors of THMs. In addition, linear fitting results indicated that the UV/chlorine-induced change in overall chlorine demand may be considered as a potential indicator for the prediction of DBPFP alteration. [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]

The ability of persulfate (PS) and peroxymonosulfate (PMS) activated by micron or nanoscale zero-valent copper (ZVC or nZVC) to degrade 2,4-dichlorophenol (2,4-DCP) was quantified under various conditions. Mechanism investigation revealed that PS and PMS accelerated the corrosion of ZVC or nZVC to release Cu + under acidic conditions. The in-situ generated Cu + further decomposed PS or PMS to produce SO 4 − and OH, which then dramatically degraded 2,4-DCP. The k obs for 2,4-DCP removal followed pseudo-first-order kinetics, k obs of ZVC/PMS and nZVC/PMS systems were 10 ∼ 30 times greater than these in ZVC/PS and nZVC/PS systems. The nZVC/PMS system was most effective to remove 2,4-DCP which even did better than the nZVI/PMS system, with rate constant values ranging from 0.041 to 1.855 min −1 . At higher pH ZVC is ineffective, but nZVC can activate PS and PMS to significantly degrade 2,4-DCP at pH up to 7.3. The 2,4-DCP degradation pathway was found to involve dechloridation, dehydrogenation, hydroxylation, ring open and mineralization. 56.7% and 45.3% of TOC removals were respectively obtained in the ZVC/PMS and nZVC/PMS systems within 120 min. This study helps to comprehend the application of zero-valent metals in reactive radicals-based oxidation processes and the reactivity of Cu + as an activator of PS and PMS. [ABSTRACT FROM AUTHOR]

As a new type of potentially threatening pollutant, microplastics are widely distributed in water and may come into contact with the humans through tap water. The removal behaviors of microplastics in water treatment plants coagulation are not completely clear. In this paper, the removal performance and mechanism of polystyrene (PS) and polyethylene (PE) microplastics using PAC and FeCl 3 coagulation were studied. Results showed that PAC was better than FeCl 3 in removal efficiency of PS and PE microplastics. Charge neutralization occurred in the coagulation process. The figures of scanning electron microscope (SEM) illustrate that agglomeration adsorption occurred in PS system, and the Fourier transform infrared spectroscope (FTIR) spectra demonstrates that new bonds were formed during the interaction between PS microplastics and coagulants. In addition, the hydrolysis products of coagulants played a major role rather than the hydrolysis process in both PS system and PE system. The removal efficiency of microplastics in alkaline conditions was higher than that in acidic conditions. Cl− had little effect on the removal efficiency of microplastics, while SO 4 2− and CO 3 2− had inhibitory and promoting effects respectively. The increase of stirring speed could improve the removal efficiency of microplastics. This paper can provide a reference for the study of microplastics treated by coagulation. Unlabelled Image • PAC was better than FeCl 3 in removal of PS and PE microplastics. • The charge neutralization occurred in the coagulation process. • Al O and Fe O were formed during the coagulation. • The hydrolysis products of coagulants played a major role in coagulation. [ABSTRACT FROM AUTHOR]