COMPARISON OF DIFFERENT CONTROL STRATEGIES FOR ENERGY SAVING IN A BIOLOGICAL NITROGEN REMOVAL ACTIVATED SLUDGE PROCESS

2004 ◽  
Vol 2004 (9) ◽  
pp. 388-396
Author(s):  
D Abad ◽  
J Manga ◽  
J Ribes ◽  
M Palma ◽  
C Venegas
Keyword(s):  
Activated Sludge ◽  
Energy Saving ◽  
Nitrogen Removal ◽  
Control Strategies ◽  
Biological Nitrogen Removal ◽  
Activated Sludge Process ◽  
Biological Nitrogen

Biological Nitrogen Removal Activated Sludge Process in Warm Climates

2015 ◽  
Vol 7 (0) ◽  
pp. 9781780401904-9781780401904
Author(s):  
C. Y. Shi ◽  
W. Y. Long ◽  
A. C. Meng ◽  
K. S. Raajeevan
Keyword(s):  
Activated Sludge ◽  
Nitrogen Removal ◽  
Biological Nitrogen Removal ◽  
Activated Sludge Process ◽  
Biological Nitrogen ◽  
Warm Climates

scholarly journals Support vector machines for environmental informatics : application to biological nitrogen removal in wastewater treatment plants

2021 ◽  
Author(s):  
Yinghui Yang
Keyword(s):  
Wastewater Treatment ◽  
Nitrogen Removal ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
Weather Conditions ◽  
Support Vector ◽  
Biological Nitrogen Removal ◽  
Complex Nature ◽  
Generalized Regression Neural Network ◽  
Biological Nitrogen

In order to meet the more stringent environmental regulations, the adaptive and optimal control strategies should be investigated for the biological nitrogen removal (BNR) processes in wastewater treatment plants. Because of the complex nature of the microbial metabolism involved, the conventional mechanistic models for nitrogen removal are difficult to formulate and the existing ones are still uncertain to some extent. Alternatively, the machine learning methods have been investigated as black-box modelling techniques. A new approach, Support Vector Machine (SVM) was proposed to be used to model the biological nitrogen removal processes in this thesis. Specifically, LS-SVM, a simplified formulation of SVM, was applied to predict the concentration of nitrate & nitrite (NO). The simulation results indicate that the proposed method has better generalization performance in comparison with generalized regression neural network, especially under weather conditions that are quite different from the training weather conditions.

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