Operating Condition Recognition of Pre-denitrification Bioprocess Using Robust EMPCA and FCM

Author

Zhao, Lijie ; Chai, Tianyou ; Cong, Qiumei

Author_Institution

Shenyang Inst. of Chem. Eng., Northeastern Univ., Shenyang

Volume

2

fYear

0

fDate

0-0 0

Firstpage

9386

Lastpage

9390

Abstract

Poor-quality data has become a serous problem to the model, control and optimization. An improved robust EMPCA integrated with fuzzy c-means (FCM) clustering is used to classify the operational state in the activated sludge process. The method is demonstrated by IWA simulation benchmark. The experimental results show the proposed the method can accurately classify the operational state of the activated sludge process in the PC-space after outliers and missing data are effectively detect, rectified

Keywords

expectation-maximisation algorithm; fuzzy set theory; optimisation; pattern clustering; principal component analysis; sludge treatment; wastewater treatment; activated sludge process; fuzzy c-means clustering; operating condition recognition; optimization; predenitrification bioprocess; robust EMPCA; Automatic control; Automation; Chemicals; Effluents; Fluctuations; Noise robustness; Plants (biology); Principal component analysis; Sludge treatment; Wastewater treatment; EM; FCM; PCA; activated sludge; optimisation;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713818

Filename

1713818