Machine learning-based prediction of non-aeration linear alkylbenzene sulfonate mineralization in an oxygenic microalgal-bacteria biofilm DOI

L. Xia,

Beibei Wu, Xiaocai Cui

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 419, P. 132028 - 132028

Published: Dec. 28, 2024

Language: Английский

Artificial intelligence in wastewater treatment: Research trends and future perspectives through bibliometric analysis DOI Creative Commons
Abdullah O. Baarimah, Mahmood A. Bazel, Wesam Salah Alaloul

et al.

Case Studies in Chemical and Environmental Engineering, Journal Year: 2024, Volume and Issue: 10, P. 100926 - 100926

Published: Aug. 31, 2024

Language: Английский

Citations

14
Machine Learning in Wastewater Treatment: A Comprehensive Bibliometric Review DOI
Wenjing Yang, Haiyan Li

ACS ES&T Water, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 6, 2025

Accurate identification and control of wastewater treatment processes are critical for the efficient use water resources. Advances in online monitoring computational capabilities have facilitated integration artificial intelligence (AI), particularly machine learning (ML), into systems. This review analyzes 433 studies on ML applications from 2000 to 2022 using bibliometric methods, examining research trends, hotspots, future directions. Since 2015, field has experienced a significant surge publications. The United States Spain notable their long-standing contributions, while China, despite entering late 2012, emerged as leading contributor publication volume. Keyword analysis reveals "neural networks" "artificial neural most frequently applied techniques, alongside terms like "prediction", "optimization", "fault detection", "design". Our comprehensive further shows that primarily focus feature identification, parameter prediction, anomaly detection, optimized with key application scenarios including systems, wastewater, waste gas, sludge. As demand AI continues grow, multimodel in-depth development may become address multiobjective challenges more effectively.

Language: Английский

Citations

0
Study on prediction models of oxygenated components content in biomass pyrolysis oil based on neural networks and random forests DOI

Yuqian Zou,

Hong Tian,

Zhangjun Huang

et al.

Biomass and Bioenergy, Journal Year: 2025, Volume and Issue: 193, P. 107601 - 107601

Published: Jan. 7, 2025

Language: Английский

Citations

0
Leveraging big data to elucidate the impact of heavy metal nanoparticles on anammox processes in wastewater treatment DOI
Yanbin Hong, Zhenguo Chen,

Zehua Huang

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 382, P. 125243 - 125243

Published: April 16, 2025

Language: Английский

Citations

0
Advances on hybrid modelling for bioprocesses engineering: insights into research trends and future directions from a bibliometric approach DOI Creative Commons

Juan Federico Herrera-Ruiz,

Javier Fontalvo, Oscar Andrés Prado-Rúbio

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103548 - 103548

Published: Nov. 1, 2024

Language: Английский

Citations

3
Short-term memory artificial neural network modelling to predict concrete corrosion in wastewater treatment plant inlet chambers using sulphide sensors DOI Creative Commons

J. Mendizabal,

David H. Vernon,

Ben Martin

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 69, P. 106821 - 106821

Published: Dec. 26, 2024

Language: Английский

Citations

0
Machine learning-based prediction of non-aeration linear alkylbenzene sulfonate mineralization in an oxygenic microalgal-bacteria biofilm DOI

L. Xia,

Beibei Wu, Xiaocai Cui

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 419, P. 132028 - 132028

Published: Dec. 28, 2024

Language: Английский

Citations

0