Research on prediction algorithm of effluent quality and development of integrated control system for waste-water treatment DOI Creative Commons

Jin-Shing Lai

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: June 2, 2025

Research is implemented to protect the environment from an epidemic of chemical materials that could render living conditions hazardous. In order efficiently use productivity while maintaining a constant and reliable level waste quality, severe regulations regarding Waste-Water Treatment Control Systems (WWTCS) must be adopted mitigate serious nature water pollution impure performance. Suboptimal treatment efficiency resources are results methods used for WWTCS, which not highly susceptible changing impact features complex biological systems. The present study presented prediction algorithm Integrated System (ICS) address problems conventional methods. This research proposes Deep Learning (DL) quality wastewater employs Quantile Regression-Random Forest (QR-RF) meta-learner when combined with Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU). proposed method has been into practice tested at Asia's Jiangsu Province Metropolitan Plant (WWTP). With Root Mean Absolute Error (RMSE) 4.76 mg/L 24-h horizons (MAE) 0.85 1-h predictions, model outperforms in terms accuracy. ICS superior standard WWTCS by vital error boundary, minimizing energy consumption 17% boosting chemical-based optimization 24%. average removal rate 94.23% Chemical Oxygen Demand (COD) compared 88.76% systems, findings experiments exhibited significant performance improvements.

Language: Английский

Navigating the molecular landscape of environmental science and heavy metal removal: A simulation-based approach DOI Creative Commons

Iman Salahshoori,

Marcos A.L. Nobre, Amirhosein Yazdanbakhsh

et al.

Journal of Molecular Liquids, Journal Year: 2024, Volume and Issue: 410, P. 125592 - 125592

Published: July 20, 2024

Heavy metals pose a significant threat to ecosystems and human health because of their toxic properties ability bioaccumulate in living organisms. Traditional removal methods often fall short terms cost, energy efficiency, minimizing secondary pollutant generation, especially complex environmental settings. In contrast, molecular simulation offer promising solution by providing in-depth insights into atomic interactions between heavy potential adsorbents. This review highlights the for removing types pollutants science, specifically metals. These powerful tool predicting designing materials processes remediation. We focus on specific like lead, Cadmium, mercury, utilizing cutting-edge techniques such as Molecular Dynamics (MD), Monte Carlo (MC) simulations, Quantum Chemical Calculations (QCC), Artificial Intelligence (AI). By leveraging these methods, we aim develop highly efficient selective unravelling underlying mechanisms, pave way developing more technologies. comprehensive addresses critical gap scientific literature, valuable researchers protection health. modelling hold promise revolutionizing prediction metals, ultimately contributing sustainable solutions cleaner healthier future.

Language: Английский

Citations

20
Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects DOI Creative Commons
Mudita Nagpal,

Miran Ahmad Siddique,

Khushi Sharma

et al.

Water Science & Technology, Journal Year: 2024, Volume and Issue: 90(3), P. 731 - 757

Published: July 26, 2024

Artificial intelligence (AI) is increasingly being applied to wastewater treatment enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, major findings of various AI models in the three key aspects: prediction removal efficiency for both organic inorganic pollutants, real-time monitoring essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, conductivity), fault detection processes equipment integral treatment. The accuracy (

Language: Английский

Citations

12
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

1
Using Generative Adversarial Networks (GANs) for Predictive Water Management and Anomaly Detection in Smart Water Systems to Achieve SDG 6 DOI
Samuel Duraivel,

Venu Gopal,

Pavithra Kannan

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

1
Investigation of biomass and pollutant kinetics in batch bioreactors for effective industrial oily wastewater treatment DOI
Masoud Barani, Salar Helchi, Mohammad Mahdi A. Shirazi

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 70, P. 107115 - 107115

Published: Jan. 31, 2025

Language: Английский

Citations

1
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
A New Approach of Complexing Polymers Used for the Removal of Cu2+ Ions DOI Open Access
Nicoleta Mirela Marin

Polymers, Journal Year: 2024, Volume and Issue: 16(7), P. 920 - 920

Published: March 27, 2024

This study presents two modified polymers for Cu2+ ion removal from aqueous media. Shredded maize stalk (MC) and a strong-base anionic resin (SAX) were with indigo carmine (IC) in order to obtain different complexing polymers, i.e., IC-MC SAX-IC. Initially, the complex reaction between IC solution was studied. Additionally, formation Cu2+-IC liquid solutions evaluated at pH ranges of 1.5, 4.0, 6.0, 8.0, 10.0, respectively. For ions, adsorption onto IC-SAX batch experiments conducted. The contact time evaluating optimum ions on materials established 1 h. Efficient SAX-IC = 10 achieved. depends quantity retained MC SAX. At 2.63 mg IC/g MC(S4) 22 SAX(SR2), high amount reported. highest capacity (Qe) obtained 0.73 mg/g, IC-SAX, it attained 10.8 mg/g. Reusability performed using HCl (0.5 M) solution. High regeneration reusability studies confirmed, suggesting that they can be used many times remove matrices. Therefore, development could suitable wastewater.

Language: Английский

Citations

6
Preparation of phenol-formaldehyde composite modified with chitosan for the simultaneous removal of antibiotics and heavy metal ions in waters DOI

Yilin Yu,

Yanyun Li,

Yingmin Liao

et al.

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: 281, P. 136467 - 136467

Published: Oct. 18, 2024

Language: Английский

Citations

6
The Impact of Bacteria on Nitrous Oxide Emission from Wastewater Treatment Plants: Bibliometric Analysis DOI Open Access

Juvens Sugira Murekezi,

Wei Chen,

Biyi Zhao

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(4), P. 1592 - 1592

Published: Feb. 14, 2025

Nitrous oxide (N2O) is a potent greenhouse gas and contributor to ozone depletion, with wastewater treatment plants (WWTPs) serving as significant sources of emissions due biological processes involving bacteria. This study evaluates research on the role bacteria in N2O from WWTPs between 2000 2023 based an analysis Web Science Core Collection Database using keywords “bacteria”, “nitrous oxide”, “emission”, “wastewater plant”. The findings reveal substantial growth past decade, leading publications appearing Water Research, Bioresource Technology, Environmental & Technology. China, United States, Australia have been most active contributors this field. Key topics include denitrification, treatment, emissions. microbial community composition significantly influences WWTPs, bacterial consortia playing pivotal role. However, further needed explore strain-specific genes, enzyme expressions, differentiation contributing production emission. System design operation must also consider dissolved oxygen nitrite concentration factors. Advances genomics artificial intelligence are expected enhance strategies for reducing WWTPs.

Language: Английский

Citations

0
Artificial Intelligence and Applications in Drinking Water Management DOI
Ricardo A. Barrera-Cámara, Ana Canepa Sáenz

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 285 - 306

Published: Feb. 18, 2025

The increase in the urban population and climate change have driven development of Smart Water systems, which integrate artificial intelligence to improve drinking water management. AI optimizes distribution, monitors quality real-time, detects leaks, manages demand efficiently, thus addressing current challenges resource aim this research is analyse applications management within systems. method used includes a literature review, case studies an analysis data obtained. results show that improves through continuous monitoring its quality, accurate leak detection, optimization distribution efficient use resources, prediction management, predictive maintenance. In addition, it reduces energy consumption treatment distribution. However, there are technical, economic, regulatory need be addressed order achieve effective sustainable implementation.

Language: Английский

Citations

0