Prediction of the optimal dose of coagulant for various potable water treatment processes through artificial neural network | Citedby Results | Journal of Hydroinformatics | IWA Publishing

Skip to Main Content

Skip Nav Destination

Prediction of the optimal dose of coagulant for various potable water treatment processes through artificial neural network

Journal of Hydroinformatics (2018) 20 (6): 1215–1226.

This article has been cited by the following articles in journals that are participating in CrossRef Cited-by Linking.

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities

Shakhawat Chowdhury
Tanju Karanfil

Chemosphere (2024) 356: 141958.

Deep learning-based coagulant dosage prediction for extreme events leveraging large-scale data

Jiwoong Kim
Chuanbo Hua
Subin Lin
Seoktae Kang
Joo-Hyon Kang
Mi-Hyun Park

Journal of Water Process Engineering (2024) 66: 105934.

Data-driven modeling techniques for prediction of settled water turbidity in drinking water treatment

Sean McKelvey
Amirhassan Abassi
C. Nataraj
Metin Duran

Frontiers in Environmental Engineering (2024) 3

Research on concentration dosing control based on the ISSA-LSTM model and dynamic concentration detection

Yuping Fan
Yanguo Yuan
Yangyang Zhang
Hailong Du
Xianshu Dong
Gu Gong
Xiaomin Ma

International Journal of Coal Preparation and Utilization 1.

Determination of coagulant dosages for process control using online UV-vis spectra of raw water

Zhining Shi
Christopher W.K. Chow
Rolando Fabris
Jixue Liu
Emma Sawade
Bo Jin

Journal of Water Process Engineering (2022) 45: 102526.

A data-driven optimization model for coagulant dosage decision in industrial wastewater treatment

Kung-Jeng Wang
Pei-Shan Wang
Hong-Phuc Nguyen

Computers & Chemical Engineering (2021) 152: 107383.