Optimization of Wastewater Treatment Through Machine Learning-Enhanced Supervisory Control and Data Acquisition: A Case Study of Granular Sludge Process Stability and Predictive Control DOI Creative Commons
Igor Gulshin, Olga Kuzina

Automation, Journal Year: 2024, Volume and Issue: 6(1), P. 2 - 2

Published: Dec. 27, 2024

This study presents an automated control system for wastewater treatment, developed using machine learning (ML) models integrated into a Supervisory Control and Data Acquisition (SCADA) framework. The experimental setup focused on laboratory-scale Aerobic Granular Sludge (AGS) reactor, which utilized synthetic to model real-world conditions. models, specifically N-BEATS Temporal Fusion Transformers (TFTs), were trained predict Biological Oxygen Demand (BOD5) values historical data real-time influent contaminant concentrations obtained from online sensors. predictive approach proved essential due the absence of direct BOD5 measurements inconsistent relationship between Chemical (COD), with correlation approximately 0.4. Evaluation results showed that demonstrated highest accuracy, achieving Mean Absolute Error (MAE) 0.988 R2 0.901. integration SCADA enabled precise, adjustments reactor parameters, including sludge dose aeration intensity, leading significant improvements in granulation stability. effectively reduced standard deviation organic load fluctuations by 2.6 times, 0.024 0.006, thereby stabilizing process within AGS reactor. Residual analysis suggested minor bias, likely limited number features model, indicating potential through additional inputs. research demonstrates value learning-driven offering resilient solution dynamic environments. By facilitating proactive management, this supports scalability treatment technologies while enhancing efficiency operational sustainability.

Language: Английский

Integrating thermal phase-change material energy storage with solar collectors: A comprehensive review of techniques and applications DOI
Farooq H. Ali, Qusay Rasheed Al-Amir, Hameed K. Hamzah

et al.

International Communications in Heat and Mass Transfer, Journal Year: 2025, Volume and Issue: 162, P. 108606 - 108606

Published: Jan. 22, 2025

Language: Английский

Citations

5

Optimizing Humidification–Dehumidification Desalination Systems: Impact of Nozzle Position and Geometric Configuration on Performance and Efficiency DOI Creative Commons
Mohammad Alrbai, Ahmad Masri,

Dareen Makawii

et al.

International Journal of Thermofluids, Journal Year: 2025, Volume and Issue: 26, P. 101117 - 101117

Published: Jan. 31, 2025

Language: Английский

Citations

1

Unveiling the potential of solar cooling technologies for sustainable energy and environmental solutions DOI
Farooq H. Ali, Qusay Rasheed Al-Amir, Hameed K. Hamzah

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 321, P. 119034 - 119034

Published: Sept. 12, 2024

Language: Английский

Citations

8

A review of axial and radial ejectors: Geometric design, computational analysis, performance, and machine learning approaches DOI
Ghassan Al-Doori, Khalid Saleh,

Ahmed Al-Manaa

et al.

Applied Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 125694 - 125694

Published: Jan. 1, 2025

Language: Английский

Citations

0

Innovative Valorization of Waste Tire by Integrating Pyrolysis with Steam Rankine Cycle, Multi-generation, and Desalination: Novel Process Design, Simulation and Comprehensive Analysis DOI
Yusha Hu, Jianzhao Zhou,

Qiming Qian

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135812 - 135812

Published: March 1, 2025

Language: Английский

Citations

0

Optimization of Wastewater Treatment Through Machine Learning-Enhanced Supervisory Control and Data Acquisition: A Case Study of Granular Sludge Process Stability and Predictive Control DOI Creative Commons
Igor Gulshin, Olga Kuzina

Automation, Journal Year: 2024, Volume and Issue: 6(1), P. 2 - 2

Published: Dec. 27, 2024

This study presents an automated control system for wastewater treatment, developed using machine learning (ML) models integrated into a Supervisory Control and Data Acquisition (SCADA) framework. The experimental setup focused on laboratory-scale Aerobic Granular Sludge (AGS) reactor, which utilized synthetic to model real-world conditions. models, specifically N-BEATS Temporal Fusion Transformers (TFTs), were trained predict Biological Oxygen Demand (BOD5) values historical data real-time influent contaminant concentrations obtained from online sensors. predictive approach proved essential due the absence of direct BOD5 measurements inconsistent relationship between Chemical (COD), with correlation approximately 0.4. Evaluation results showed that demonstrated highest accuracy, achieving Mean Absolute Error (MAE) 0.988 R2 0.901. integration SCADA enabled precise, adjustments reactor parameters, including sludge dose aeration intensity, leading significant improvements in granulation stability. effectively reduced standard deviation organic load fluctuations by 2.6 times, 0.024 0.006, thereby stabilizing process within AGS reactor. Residual analysis suggested minor bias, likely limited number features model, indicating potential through additional inputs. research demonstrates value learning-driven offering resilient solution dynamic environments. By facilitating proactive management, this supports scalability treatment technologies while enhancing efficiency operational sustainability.

Language: Английский

Citations

1