A fast predicting neural fuzzy model for on-line estimation of nutrient dynamics in an anoxic/oxic process.
In this paper a software sensor based on a fuzzy neural network approach was proposed for real-time estimation of nutrient concentrations. In order to improve the network performance, fuzzy subtractive clustering was used to identify model architecture, extract and optimize fuzzy rule of the model. A split network structure was applied separately for anaerobic and aerobic conditions was employed with dynamic modeling methods such as autoregressive with exogenous inputs and multi-way principal component analysis (MPCA). The proposed methodology was applied to a bench-scale anoxic/oxic process for biological nitrogen removal. The simulative results indicate that the learning ability and generalization of the model performed well and also worked well for normal batch operations corresponding to three data points inside the confidence limit determined by MPCA. Real-time estimation of NO(3)(-), NH(4)(+) and PO(4)(3-) concentration based on fuzzy neural network analysis were successfully carried out with the simple on-line information regarding the anoxic/oxic system.
Huang
Ming-zhi
MZ
College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510640, China. mz.huang@mail.scut.edu.cn
Wan
Jin-quan
JQ
Ma
Yong-wen
YW
Li
Wei-jiang
WJ
Sun
Xiao-fei
XF
Wan
Yan
Y
D000465:Algorithms; D000641:Ammonia / Q000737:chemistry; D019149:Bioreactors; D003470:Culture Media / Q000378:metabolism; D004867:Equipment Design; D017143:Fuzzy Logic; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000737:chemistry; D010100:Oxygen / Q000737:chemistry*; D010710:Phosphates / Q000737:chemistry; D025341:Principal Component Analysis; D012044:Regression Analysis; D012984:Software; D013997:Time Factors
D003470:Culture Media; D010710:Phosphates; D000641:Ammonia; D009584:Nitrogen; D010100:Oxygen