Optimising Adsorption-Based Distillery Wastewater Treatment by Predicting Effluent Characteristics Using Machine Learning DOI

Dipak Bhoye,

Gayatri S. Vyas, Chaitali K. Nikhar

et al.

Lecture notes in civil engineering, Journal Year: 2024, Volume and Issue: unknown, P. 1105 - 1119

Published: Jan. 1, 2024

Language: Английский

Use, Potential, Needs, and Limits of AI in Wastewater Treatment Applications DOI Open Access
Andrea G. Capodaglio, Arianna Callegari

Water, Journal Year: 2025, Volume and Issue: 17(2), P. 170 - 170

Published: Jan. 10, 2025

Artificial intelligence (AI) uses highly powerful computers to mimic human intelligent behavior; it is a major research hotspot in science and technology, with an increasing number of applications wider range fields, including complex process supervision control. Wastewater treatment example involving many uncertainties external factors achieve final product specific requisites (effluents prescribed quality). Reducing energy consumption, greenhouse gas emissions, resources recovery are additional requirements these facilities’ operation. AI could extend the purpose expected results previously adopted tools present operational approaches by leveraging superior simulation, prediction, control, adaptation capabilities. This paper reviews current wastewater field discusses achievements potentials. So far, almost all sector involve predictive studies, often at small scale or limited data use. Frontline aimed creation AI-supported digital twins real systems being conducted, few encouraging but still applications. aims identifying discussing key barriers adoption field, which include laborious instrumentation maintenance, lack expertise design software, instability control loops, insufficient incentives for resource efficiency achievement.

Language: Английский

Citations

2
Simulation and prediction of PM2.5 concentrations and analysis of driving factors using interpretable tree-based models in Shanghai, China DOI
Wei Qing, Yongqi Chen, Huijin Zhang

et al.

Environmental Research, Journal Year: 2025, Volume and Issue: unknown, P. 121003 - 121003

Published: Feb. 1, 2025

Language: Английский

Citations

2
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
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

13
Machine learning framework for wastewater circular economy — Towards smarter nutrient recoveries DOI Creative Commons
Allan Soo, Li Gao, Ho Kyong Shon

et al.

Desalination, Journal Year: 2024, Volume and Issue: 592, P. 118092 - 118092

Published: Sept. 7, 2024

Language: Английский

Citations

5
An auto-configurable and interpretable ensemble learning framework for optimal catalyst design of green methanol production via Bayesian optimization DOI

Dongwen Rong,

Zhao Wang,

Qiwen Guo

et al.

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: 488, P. 144666 - 144666

Published: Jan. 1, 2025

Language: Английский

Citations

0
Sustainable water purification: evaluating Phumdi biomass adsorbent performance through machine learning-based feature analysis DOI

Lairenlakpam Helena,

Sudhakar Ningthoujam,

Potsangbam Albino Kumar

et al.

Clean Technologies and Environmental Policy, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 13, 2025

Language: Английский

Citations

0
Comparative Analysis of Advanced Machine Learning Regression Models with Advanced Artificial Intelligence Techniques to Predict Rooftop PV Solar Power Plant Efficiency Using Indoor Solar Panel Parameters DOI Creative Commons

İhsan Levent,

Gökhan Şahin, Gültekin Işık

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3320 - 3320

Published: March 18, 2025

As a result of the increase in number smart buildings and advances technology, energy consumption has become increasingly important. The estimation is critical for efficiency. Accurate photovoltaic (PV) solar power plant efficiency crucial optimizing performance renewable applications. In this study, advanced machine learning regression models such as XGBoost, CatBoost, LightGBM, AdaBoost Histogram-Based Gradient Boosting are used to predict PV based on ten internal features (Open Circuit Voltage (Voc), Short Current (Isc), Maximum Power (Pmpp), Solar Irradiation Spread (SIS), (Vmpp), (Impp), Fill Factor (FF), Parallel Resistance (Rp), Series (Rs), Module Temperature (Tm)) module measurements from Utrecht University Photovoltaic Outdoor Test Facility. result, CatBoost outperformed others, achieving lowest prediction error MSE 0.002 highest R2 value 0.90. To interpret model’s predictions, we applied Explainable Artificial Intelligence techniques, particular SHAP LIME, which identify key affecting model transparency. integration these methods provides valuable insights design optimization.

Language: Английский

Citations

0
Prediction of Waste Sludge Production in Municipal Wastewater Treatment Plants by Deep-Learning Algorithms with Antioverfitting Strategies DOI
Juanjuan Chen, Weixiang Chao, Yixuan Wang

et al.

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

Published: April 3, 2025

Language: Английский

Citations

0
Meta-heuristic optimization algorithms based feature selection for joint moment prediction of sit-to-stand movement using machine learning algorithms DOI
Ekin Ekıncı, Zeynep Garip, Kasım Serbest

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 178, P. 108812 - 108812

Published: June 28, 2024

Language: Английский

Citations

3