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Abstract
Each stage of the sewage treatment process emits odor causing compounds and these compounds may vary from one location in a sewage treatment works to another. In order to determine the boundaries of legal standards, reliable and efficient odor measurement methods need to be defined. An electronic NOSE equipped with 12 different polypyrrole sensors is used for the purpose of characterizing sewage odors. Samples collected at different locations of a WWTP were classified using a fuzzy clustering technique and a neural network trained with a back-propagation algorithm.
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Authors and Affiliations
Department of Environmental Engineering, Gebze Institute of Technology, 41400, Gebze, Kocaeli, Turkey
Güleda Önkal-Engin
Environmental Informatics and Control Program, Warnell School of Forest Resources, University of Georgia, Athens, GA, 30602, USA
Ibrahim Demir
Department of Electrical Engineering, Yildiz Technical University, 34800, Besiktas, Istanbul, Turkey
Seref N. Engin
- Güleda Önkal-Engin
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- Ibrahim Demir
You can also search for this author inPubMed Google Scholar
- Seref N. Engin
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Editors and Affiliations
School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore
Lipo Wang
School of Software, Sun Yat-Sen University, 510275, Guangzhou, China
Ke Chen
School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore
Yew Soon Ong
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© 2005 Springer-Verlag Berlin Heidelberg
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Önkal-Engin, G., Demir, I., Engin, S.N. (2005). e-NOSE Response Classification of Sewage Odors by Neural Networks and Fuzzy Clustering. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_92
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