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, Год журнала: 2024, Номер 6(1), С. 2 - 2

Опубликована: Дек. 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.

Язык: Английский

Intelligent Robust Control of Roadheader Based on Disturbance Observer DOI Creative Commons

Shuo Wang,

Dongjie Wang,

Aixiang Ma

и другие.

Actuators, Год журнала: 2025, Номер 14(1), С. 36 - 36

Опубликована: Янв. 17, 2025

The formation of a coal mine roadway cross-section is primary task the boom-type roadheader. This paper proposes an intelligent robust control scheme for cutting head trajectory tunneling robot, which susceptible to unknown external disturbances, system nonlinearity, and parameter uncertainties. First, working conditions section were analyzed, mathematical model was established. Then, high-gain disturbance observer designed based on analyze loads compensate uncertainties disturbances. A sliding mode controller proposed using backstepping design method, incorporating saturation function term avoid chattering. eel foraging optimization algorithm also improved used tune parameters. simulation developed performance comparison tests. Finally, experimental verification conducted under actual in tunnel face, results demonstrated effectiveness method.

Язык: Английский

Процитировано

0
Enhancing resource recovery from acid whey through chitosan-based pretreatment and machine learning optimization DOI
Fei Long,

Hong Liu

Bioresource Technology, Год журнала: 2024, Номер unknown, С. 131932 - 131932

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

1
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, Год журнала: 2024, Номер 6(1), С. 2 - 2

Опубликована: Дек. 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.

Язык: Английский

Процитировано

1