Study on layout optimization of sewage outfalls: a case study of wastewater treatment plants in Xiamen

Wang, Cui; Guo, Zhouhua; Li, Qingsheng; Fang, Jing

doi:10.1038/s41598-021-97756-9

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…In recent years, the industrialization and urbanization of coastal areas have experienced increasing population pressures [1][2][3][4]. A significant volume of wastewater generated by local residents is often discharged into the sea after undergoing rudimentary water treatment [5][6][7]. The discharge of sewage into the receiving water body will significantly increase turbidity and organic and inorganic substances, thereby changing the living environment of marine organisms [8].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Yan,

Zhang,

et al. 2024

JMSE

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Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Yan,

Zhang,

et al. 2024

JMSE

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“…When sewage is discharged into the sea, the kinetic energy of the jet is dissipated in its surrounding seawater, and due to the interaction of the turbulence of the discharged jet and the natural turbulence in the ocean, the discharged sewage will lose its primary concentration in which after the initial jet-mixing, the sewage flows gently with the ocean as if it was part of it [1]. Among others, Chow et al, [2], Kim et al, [3], Bricker, et al, [4], Zhao, et al, [5], Inan, et al, [6], Wang et al, [7], Ho et al, [8], Panseriya et al, [9], and Santavy et al, [10] gave important contributions to investigate the marine outfall sewage effluent dispersion phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Diffusion of a Surface Marine Sewage Effluent

Sahameddin Mahmoudi Kurdistani,

Gianluca Cosimo Perrone

2023

CFDL

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Any scalar concentration discharging to the sea through a freshwater release is a thermodynamic two-phase flow including different densities and different temperatures. Because of Reynolds similarity, physical models are not feasible to use and numerical models such as CFD-RANS are the solutions. A series of numerical experiments have been conducted to derive an empirical formula that determines the length of the 95% scalar concentration reduction, useful for primary and emergency engineering-environmental decisions. Results show that the scalar concentration intrusion length is a function of seawater density-temperature, freshwater density-temperature, sea current Froude number, effluent jet Froude number, and the scalar concentration in which among those, the sea current Froude number has the most effective role in which as an example of the lowest and highest tested sea current velocities, an effluent discharge of 0.052 m3/s with a concentration of 1 Kg/m3 and effluent jet velocity 0.245 m/s in 3.2 meters from the effluent source reaches to the 95% scalar concentration reduction when there is a high-velocity sea current (0.5 m/s) while in the presence of a low-velocity sea current (0.05 m/s), the same effluent discharge with the same scalar concentration and the same effluent jet velocity, the length of the 95% scalar concentration reduction increase to 42.8 meters from the effluent source.

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“…EFDC model can be adopted to simulate the influence of wastewater treatment plant effluent on the water quality of receiving water body, which provides a basis for the selection of effluent discharge path of wastewater treatment plant and the design of water environmental quality compliance scheme. Fan Qiaoming et al (Fan and Ding, 2016) established a hydrodynamic and pollutant transport and diffusion model based on EFDC, and quantitatively evaluated the impact of tailwater on the water environment in the port at different discharge outlet locations and under different discharge standards. Liu Cheng et al (Cheng et al, 2003) used mathematical models to simulate the hydrodynamics of the Yangtze River, and analyzed the dilution and diffusion field of pollutants discharged from the tailwater of wastewater treatment plants.…”

Section: Introductionmentioning

confidence: 99%

Effect of Sewage Treatment Plant Effluent on Water Quality of Zhangze Reservoir Based on EFDC Model

Yang¹,

Wei

et al. 2022

Front. Environ. Sci.

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Understanding how external water affects the water quality of target water bodies is an important prerequisite for predicting water quality changes and formulating pollutant reduction plans. Using the EFDC modeling system platform, this study constructed the hydrodynamic and pollutant dispersion and diffusion models of Zhangze Reservoir, and by using the hydrology and water quality data from 2013 to 2018, this study examines the different water quality responses of Zhangze Reservoir under different inflow water control scenarios. The results show that the simulated value of the hydrological station is basically consistent with the measured value of the hydrological station. Under the current Class III water quality requirements for Zhangze Reservoir, direct inflow of effluent from the wastewater treatment plant of the main urban area into the reservoir will have a significant impact on the water quality. When the effluent is purified through constructed wetlands, it significantly improves the water quality of the reservoir, but the concentration of TN and TP still cannot reach the Class III standard in the short term,. When all rivers entering the reservoir meet the standard and the effluent from the wastewater treatment plant in the main urban area does not enter the reservoir, the content of COD, ammonia nitrogen and TP in water will reach the Class III standard, but reducing the TN content to meet the standard requirement will be difficult within 3 years.

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Study on layout optimization of sewage outfalls: a case study of wastewater treatment plants in Xiamen

Cited by 4 publications

References 17 publications

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Diffusion of a Surface Marine Sewage Effluent

Effect of Sewage Treatment Plant Effluent on Water Quality of Zhangze Reservoir Based on EFDC Model

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