Supporting data–enhanced hybrid ordinary differential equation model for phosphate dynamics in municipal wastewater treatment DOI

Guang-yao Zhao,

Hiroaki Furumai, Masafumi Fujita

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 409, P. 131217 - 131217

Published: Aug. 6, 2024

Language: Английский

Bibliometric analysis of artificial intelligence in wastewater treatment: Current status, research progress, and future prospects DOI
Xingyang Li, Jiming Su, Hui Wang

et al.

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(4), P. 113152 - 113152

Published: May 23, 2024

Language: Английский

Citations

14
Enhancing wastewater treatment through artificial intelligence: A comprehensive study on nutrient removal and effluent quality prediction DOI
Offir Inbar, Dror Avisar

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 61, P. 105212 - 105212

Published: April 11, 2024

Language: Английский

Citations

9
Insights into the application of explainable artificial intelligence for biological wastewater treatment plants: Updates and perspectives DOI Creative Commons

Abdul Gaffar Sheik,

Arvind Kumar,

Chandra Sainadh Srungavarapu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 144, P. 110132 - 110132

Published: Jan. 31, 2025

Language: Английский

Citations

1
Dynamic multi-objective optimization control for wastewater treatment process based on modal decomposition and hybrid neural network DOI
Qing Liu, Xiangyuan Jiang

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 61, P. 105274 - 105274

Published: April 16, 2024

Language: Английский

Citations

7
Performance Evaluation and Triangle Diagram of Deep Learning Models for Embedment Depth Prediction in Cantilever Sheet Piles DOI Open Access
Thalappil Pradeep,

Divesh Ranjan Kumar,

Nitish Kumar

et al.

Engineered Science, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Sheet piles are essential for maintaining the stability and retention of soil in various applications, including railway highway embankments, offshore structures, post-excavation sites, slope stabilization projects.The required depth sheet is contingent upon factors such as characteristics, groundwater conditions, employed construction method.This study focused on predicting embedment cantilever pile walls cohesive with a cohesionless backfill.Artificial intelligence (AI) techniques, specifically deep neural networks (DNNs), recurrent (RNNs), long short-term memory (LSTM) networks, bidirectional (Bi-LSTM) applied this purpose.Performance evaluation conducted through rank analysis, performance parameter determination, comparison actual versus predicted curves, accompanied by an error plot.A triangle diagram introduced graphical representation to assess different datasets or models.External validation was evaluate generalizability

Language: Английский

Citations

6
A holistic global-local stochastic configuration network modeling framework with antinoise awareness for efficient semi-supervised regression DOI
Xiaogang Deng, Yue Zhao, Jing Zhang

et al.

Information Sciences, Journal Year: 2024, Volume and Issue: 661, P. 120132 - 120132

Published: Jan. 17, 2024

Language: Английский

Citations

5
Multi-step ahead dissolved oxygen concentration prediction based on knowledge guided ensemble learning and explainable artificial intelligence DOI
Tunhua Wu, Zhaocai Wang, Jinghan Dong

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131297 - 131297

Published: May 9, 2024

Language: Английский

Citations

5
A novel thermal management system for lithium-ion battery modules combining indirect liquid-cooling with forced air-cooling: Deep learning approach DOI

Chun Yang Guo,

Mohammed W. Muhieldeen, Kah Hou Teng

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 94, P. 112434 - 112434

Published: June 8, 2024

Language: Английский

Citations

5
Hybrid model composed of machine learning and ASM3 predicts performance of industrial wastewater treatment DOI
Boyan Xu, Ching Kwek Pooi, Tsuey Shan Yeap

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 65, P. 105888 - 105888

Published: Aug. 1, 2024

Language: Английский

Citations

5
Enhancing Decision Making and Decarbonation in Environmental Management: A Review on the Role of Digital Technologies DOI Open Access
Abdel‐Mohsen O. Mohamed, Dina Mohamed, Adham Fayad

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(16), P. 7156 - 7156

Published: Aug. 20, 2024

As global concerns about climate change intensify, the need for effective strategies to reduce carbon emissions, has never been more urgent. This review paper explores crucial role of digital technologies (i.e., data automation (DA) and decision support systems (DSSs)) in enhancing making achieving a ZERONET initiative (decarbonation efforts) within realms solid waste management (SWM), wastewater treatment (WWT), contaminated soil remediation (CSR). Specifically, provides (a) an overview footprint (CFP) relation environmental (EM) DA DSS decarbonization; (b) case studies areas SWM, WWT, CSR use (i) technology; ((ii) life cycle assessment (LCA)-based DSS; (iii) multi-criteria analysis (MCDA)-based (c) optimal contractual delivery method-based EM practices. concludes that adoption DSSs holds significant potential decarbonizing processes. By optimizing operations, resource efficiency, integrating renewable energy sources, smart can contribute reduction GHG emissions promotion sustainable demand eco-friendly solutions grows, will become increasingly pivotal decarbonization goals.

Language: Английский

Citations

5