The Integration of Artificial Intelligence in Advanced Wastewater Treatment Systems DOI
Manoj Chandra Garg, Sheetal Kumari, Smriti Agarwal

et al.

Springer water, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 27

Published: Jan. 1, 2024

Language: Английский

Dynamic microbiome disassembly and evolution induced by antimicrobial methylisothiazolinone in sludge anaerobic fermentation for volatile fatty acids generation DOI
Shiyu Fang, Jiashun Cao, Qian Wu

et al.

Water Research, Journal Year: 2024, Volume and Issue: 251, P. 121139 - 121139

Published: Jan. 15, 2024

Language: Английский

Citations

32
Applications of machine learning tools for biological treatment of organic wastes: Perspectives and challenges DOI Creative Commons
Long Chen, Pinjing He, Hua Zhang

et al.

Circular Economy, Journal Year: 2024, Volume and Issue: 3(2), P. 100088 - 100088

Published: May 31, 2024

Biological treatment technologies (such as anaerobic digestion, composting, and insect farming) have been extensively employed to handle various degradable organic wastes. However, the inherent complexity instability of biological processes adversely affect production renewable energy nutrient-rich products. To ensure stable consistent product quality, researchers invested heavily in control strategies for treatment, with machine learning (ML) recently proving effective optimizing predicting parameters, detecting disturbances, enabling real-time monitoring. This review critically assesses application ML providing an in-depth evaluation key algorithms. study reveals that artificial neural networks, tree-based models, support vector machines, genetic algorithms are leading treatment. A thorough investigation applications farming underscores its remarkable capacity predict products, optimize processes, perform monitoring, mitigate pollution emissions. Furthermore, this outlines challenges prospects encountered applying highlighting crucial directions future research area.

Language: Английский

Citations

8
Understanding Anaerobic Co-digestion of Organic Wastes through Meta-Analysis DOI
Wachiranon Chuenchart, K.C. Surendra, Samir Kumar Khanal

et al.

ACS ES&T Engineering, Journal Year: 2024, Volume and Issue: 4(5), P. 1177 - 1192

Published: March 21, 2024

Anaerobic co-digestion (AcoD) is becoming increasingly popular in the biogas industry for its numerous advantages over mono-digestion, including balanced nutrient profiles, enhanced process stability, synergistic methane production, and reduced greenhouse gas emissions. However, varying results across AcoD studies highlight need an extensive meta-analysis of a large data set to better understand these synergies. Here, we compared yields from 432 sets, based on ratio means, mono-digestion. The relative index (RSI) revealed effects lignocellulosic biomass with animal manures, particularly pig (RSI = 1.91) chicken manures 1.71). Due rapid biodegradability, food waste also demonstrated both 1.28) 1.41). After regrouping by high-carbon co-substrates, interpretable machine learning models identified temperature, which accelerates hydrolysis emulsification fats, oils, grease, as key factor influencing yield. Furthermore, experimental validation using sewage sludge confirmed superiority multivariable linear regression predicting specific yield other terms simplicity efficiency.

Language: Английский

Citations

7
Applying machine learning to anaerobic fermentation of waste sludge using two targeted modeling strategies DOI

Shixin Zhai,

Kai Chen, Lisha Yang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 916, P. 170232 - 170232

Published: Jan. 24, 2024

Language: Английский

Citations

5
Selective enrichment of gram-staining bacteria by antimicrobial pressure enhances volatile fatty acids production from waste activated sludge DOI

Xue Xia,

Wenxuan Huang, Zhicheng Wei

et al.

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 163307 - 163307

Published: May 1, 2025

Language: Английский

Citations

0
The important role of high sludge concentration in anaerobic biological systems with low COD/SO42- sulfate-containing wastewater predicted by machine learning: Insights from microbial community and metabolic pathways DOI
Jianliang Xue, Yanan Li, Shujuan Liu

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 496, P. 154320 - 154320

Published: July 27, 2024

Language: Английский

Citations

3
Machine learning-aided inverse design for biogas upgrading through biological CO2 conversion DOI Creative Commons
Jiasi Sun, Yue Rao, Zhen He

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 399, P. 130549 - 130549

Published: March 9, 2024

Language: Английский

Citations

2
Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean DOI
Zhimin Liu, Weijun Wang, Yibo Geng

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 478, P. 135555 - 135555

Published: Aug. 23, 2024

Language: Английский

Citations

2
Machine learning for enhancing prediction of biogas production and building a VFA/ALK soft sensor in full-scale dry anaerobic digestion of kitchen food waste DOI

Jinlin Zou,

Fan Lü, Long Chen

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 371, P. 123190 - 123190

Published: Nov. 5, 2024

Language: Английский

Citations

2
Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification DOI
Shiqi Liu,

Zeqing Long,

Jinsong Liang

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 416, P. 131787 - 131787

Published: Nov. 8, 2024

Language: Английский

Citations

1