scholarly journals Effects of High Circulation Ratio on Biological Nitrogen Removal in Anaerobic-aerobic Wastewater Treatment System

2006 ◽  
Vol 42 (3) ◽  
pp. 151-157 ◽  
Author(s):  
HIROSHI YAMAZAKI ◽  
RIE SUZUKI ◽  
YASUTOSHI SHIMIZU ◽  
YOSHITAKA EBIE ◽  
YUHEI INAMORI ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Nitrogen Removal ◽  
Treatment System ◽  
Biological Nitrogen Removal ◽  
Wastewater Treatment System ◽  
Aerobic Wastewater Treatment ◽  
Circulation Ratio ◽  
Biological Nitrogen

Removal of sulfide in integrated anaerobic?aerobic wastewater treatment system

2003 ◽  
Vol 6 (1) ◽  
pp. 66-72 ◽  
Author(s):  
A. Gangagni Rao ◽  
K. Krishna Prasad ◽  
G. Venkata Naidu ◽  
N. Chandrashekar Rao ◽  
P. N. Sarma
Keyword(s):  
Wastewater Treatment ◽  
Treatment System ◽  
Wastewater Treatment System ◽  
Aerobic Wastewater Treatment

scholarly journals Thermophilic biological nitrogen removal in industrial wastewater treatment

2013 ◽  
Vol 98 (2) ◽  
pp. 945-956 ◽  
Author(s):  
C. M. Lopez-Vazquez ◽  
M. Kubare ◽  
D. P. Saroj ◽  
C. Chikamba ◽  
J. Schwarz ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Nitrogen Removal ◽  
Industrial Wastewater ◽  
Biological Nitrogen Removal ◽  
Industrial Wastewater Treatment ◽  
Biological Nitrogen

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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