A systematic approach to data-driven modeling and soft sensing in a full-scale plant

The well-known mathematical modeling and neural networks (NNs) methods have limitations to incorporate the key process characteristics at the wastewater treatment plants (WWTPs) which are complex, non-stationary, temporal correlation, and nonlinear systems. In this study, a systematic methodology of...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 60(2009), 2 vom: 18., Seite 363-70
1. Verfasser: Kim, M H (VerfasserIn)
Weitere Verfasser: Kim, Y S, Prabu, A A, Yoo, C K
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2009
Zugriff auf das übergeordnete Werk:Water science and technology : a journal of the International Association on Water Pollution Research
Schlagworte:Journal Article Research Support, Non-U.S. Gov't Sewage
LEADER 01000caa a22002652 4500
001 NLM190247274
003 DE-627
005 20250210152349.0
007 cr uuu---uuuuu
008 231223s2009 xx |||||o 00| ||eng c
024 7 |a 10.2166/wst.2009.346  |2 doi 
028 5 2 |a pubmed25n0634.xml 
035 |a (DE-627)NLM190247274 
035 |a (NLM)19633378 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Kim, M H  |e verfasserin  |4 aut 
245 1 2 |a A systematic approach to data-driven modeling and soft sensing in a full-scale plant 
264 1 |c 2009 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 08.10.2009 
500 |a Date Revised 10.12.2019 
500 |a published: Print 
500 |a Citation Status MEDLINE 
520 |a The well-known mathematical modeling and neural networks (NNs) methods have limitations to incorporate the key process characteristics at the wastewater treatment plants (WWTPs) which are complex, non-stationary, temporal correlation, and nonlinear systems. In this study, a systematic methodology of NNs modeling which can be efficiently included in the key modeling information of the WWTPs is performed by selecting the temporal effect of the hydraulics based on multi-way principal components analysis (MPCA). The proposed method is applied for modeling wastewater quality of a full-scale plant, which is a Daewoo nutrient removal (DNR) process. Through the experimental results in a full-scale plant, the efficiency of the proposed method is evaluated and the prediction capability is highly improved by the inclusion of the hydraulics term due to the optimized structure of neural networks 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
650 7 |a Sewage  |2 NLM 
700 1 |a Kim, Y S  |e verfasserin  |4 aut 
700 1 |a Prabu, A A  |e verfasserin  |4 aut 
700 1 |a Yoo, C K  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t Water science and technology : a journal of the International Association on Water Pollution Research  |d 1986  |g 60(2009), 2 vom: 18., Seite 363-70  |w (DE-627)NLM098149431  |x 0273-1223  |7 nnns 
773 1 8 |g volume:60  |g year:2009  |g number:2  |g day:18  |g pages:363-70 
856 4 0 |u http://dx.doi.org/10.2166/wst.2009.346  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 60  |j 2009  |e 2  |b 18  |h 363-70