Quantifying the mitigation potential of energy and chemical consumption for a full-scale wastewater treatment plant with deep learning methods DOI

Chenyang Yu,

Runyao Huang,

Jie Yu

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 394, P. 126123 - 126123

Published: May 26, 2025

Language: Английский

Advanced Temporal Deep Learning Framework for Enhanced Predictive Modeling in Industrial Treatment Systems DOI Creative Commons

S Ramya,

S Srinath,

Pushpa Tuppad

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104158 - 104158

Published: Jan. 1, 2025

Language: Английский

Citations

2
Assessing Agricultural Reuse Potential of Treated Wastewater: A Hybrid Machine Learning Approach DOI Creative Commons
Daniyal Durmuş Köksal, Yeşim Ahi, Mladen Todorović

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(3), P. 703 - 703

Published: March 14, 2025

Estimating the quality of treated wastewater is a complex, nonlinear challenge that traditional statistical methods struggle to address. This study introduces hybrid machine learning approach predict key effluent parameters from an advanced biological treatment plant and assesses reuse potential for irrigation. Three artificial intelligence (AI) models, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Fuzzy Logic-Mamdani (FLM), were applied three years daily inlet outlet water data. Logic was employed usability wastewater, with ANFIS categorizing ANN-based high-performance models (low MSE, 74–99% R2) in fuzzy inference system. The qualitative agricultural irrigation ranged 69% 72% based on best-performing model. It estimated could irrigate approximately 35% 20,000-hectare area. By integrating this research enhances accuracy interpretability predictions, providing reliable framework sustainable resource management. findings support optimization processes highlight AI’s role advancing strategies agriculture, ultimately contributing improved efficiency environmental conservation.

Language: Английский

Citations

1
Machine Learning-Driven Benchmarking of China's Wastewater Treatment Plant Electricity Consumption DOI Creative Commons

Manru Li,

C. J. Tang,

Jing Gu

et al.

Water Research X, Journal Year: 2025, Volume and Issue: 26, P. 100309 - 100309

Published: Jan. 1, 2025

Language: Английский

Citations

0
Reliable Water Quality Prediction Using Bayesian Multi-Scale Convolutional Attention Network DOI Open Access
Xiaolin Guo

Journal of Geoscience and Environment Protection, Journal Year: 2025, Volume and Issue: 13(03), P. 347 - 363

Published: Jan. 1, 2025

Language: Английский

Citations

0
A Water Quality Prediction Model Based on Neural Network at Data-Scarce Sites DOI Creative Commons

C. L. Philip Chen,

Jinghua Hao

Water-Energy Nexus, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

Citations

0
Quantifying the mitigation potential of energy and chemical consumption for a full-scale wastewater treatment plant with deep learning methods DOI

Chenyang Yu,

Runyao Huang,

Jie Yu

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 394, P. 126123 - 126123

Published: May 26, 2025

Language: Английский

Citations

0