Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors DOI
Mohamed Sherif Zaghloul, Oliver Terna Iorhemen, Rania Hamza

et al.

Water Research, Journal Year: 2020, Volume and Issue: 189, P. 116657 - 116657

Published: Nov. 19, 2020

Language: Английский

Machine Learning-Assisted Optimization of Mixed Carbon Source Compositions for High-Performance Denitrification DOI
Yuan Pan, Tian-Wei Hua,

Rui-Zhe Sun

et al.

Environmental Science & Technology, Journal Year: 2024, Volume and Issue: 58(28), P. 12498 - 12508

Published: June 20, 2024

Appropriate mixed carbon sources have great potential to enhance denitrification efficiency and reduce operational costs in municipal wastewater treatment plants (WWTPs). However, traditional methods struggle efficiently select the optimal mixture due variety of compositions. Herein, we developed a machine learning-assisted high-throughput method enabling WWTPs rapidly identify optimize sources. Taking local WWTP as an example, source data set was established via employed train learning model. The composition types inoculated sludge served input variables. XGBoost algorithm predict total nitrogen removal rate microbial growth, thereby aiding assessment potential. predicted exhibited enhanced over single both kinetic experiments long-term reactor operations. Model feature analysis shows that cumulative effect interaction among individual significantly overall Metagenomic reveals increased diversity complexity denitrifying bacterial ecological networks WWTPs. This work offers efficient for compositions provides new insights into mechanism behind under supply multiple

Language: Английский

Citations

15
Iron-based materials for nitrogen and phosphorus removal from wastewater: A review DOI

Boyun Zhu,

Rongfang Yuan, Shaona Wang

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 59, P. 104952 - 104952

Published: Feb. 20, 2024

Language: Английский

Citations

13
Microplastics enhance the denitrification of glycogen-accumulating organisms by regulating electronic transport in carbon-nitrogen coupling DOI
Yuchao Liu,

Jinrui Cao,

Sheng Li

et al.

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: 489, P. 137627 - 137627

Published: Feb. 21, 2025

Language: Английский

Citations

1
Microbial community and function evaluation in the start-up period of bioaugmented SBR fed with aniline wastewater DOI
Wenli Zhang, Qian Zhang, Meng Li

et al.

Bioresource Technology, Journal Year: 2020, Volume and Issue: 319, P. 124148 - 124148

Published: Sept. 21, 2020

Language: Английский

Citations

56
Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors DOI
Mohamed Sherif Zaghloul, Oliver Terna Iorhemen, Rania Hamza

et al.

Water Research, Journal Year: 2020, Volume and Issue: 189, P. 116657 - 116657

Published: Nov. 19, 2020

Language: Английский

Citations

51