A simultaneous removal of ammonium and turbidity via an adsorptive coagulation for drinking water treatment process

Mohtar, Safia Syazana; Sharuddin, Siti Shilatul Najwa; Saman, Norasikin; Lye, Jimmy Wei Ping; Othman, Nurul Sakinah; Mat, Hanapi

doi:10.1007/s11356-019-06507-x

Cited by 6 publications

(2 citation statements)

References 45 publications

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“…In the application of non-artificial intelligence method in flocculant dosing process (Chua et al 2020;Mohtar et al 2020), it is often necessary to model separately according to the situation of water plants, and this model cannot be directly applied to other water plants. The IRF model can adjust parameters automatically and can be directly applied to multiple waterworks.…”

Section: Discussionmentioning

confidence: 99%

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Estimating effluent turbidity in the drinking water flocculation process with an improved random forest model

Wang

Chang

et al. 2021

Water Supply

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During the drinking water treatment, the uncertain changes of raw water quality bring great difficulties to the control of flocculants dosage, especially because the feedback information based on the effluent turbidimeter of sedimentation tank can only be obtained after a long time when the influent water quality changes for the large lag characteristics of flocculation process. The prediction of effluent turbidity of sedimentation tank can effectively solve aforementioned problem. Given that it is difficult for the ordinary random forest (RF) model to accurately predict the effluent turbidity of sedimentation tank for the complicated changes of raw water quality, an improved random forest (IRF) model composed of long-term and short-term parts is proposed, which can capture the periodicity and time-varying characteristics of influent water quality data. The experimental results show that, the root mean square error and mean absolute percentage error of IRF model in Baiyangwan waterworks are improved 67.52% and 67.91% respectively, compared with those of ordinary RF model. The proposed effluent turbidity predictions are also successfully developed in Xujiang waterworks and Xiangcheng waterworks of Suzhou, China. This research provides an effective method for real-time predicting the effluent turbidity of sedimentation tank according to the influent water quality data.

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Section: Discussionmentioning

confidence: 99%

“…The ideal flocculation process should dose appropriate flocculant according to the influent water quality index. (Chua et al 2020;Mohtar et al 2020). The flocculant dosage directly determines the water purification effect (Xia et al 2018).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%