Please use this identifier to cite or link to this item:
https://hdl.handle.net/10321/2346
Title: | Artificial intelligence for the evaluation of operational parameters influencing Nitrification and Nitrifiers in an activated sludge process |
Authors: | Awolusi, Oluyemi Olatunji Nasr, Mahmoud Kumari, Sheena K. Bux, Faizal |
Keywords: | Adaptive neuro-fuzzy inference system;Ammonia-oxidizing bacteria;Nitrite-oxidizing bacteria;Operational parameters;Statistical tools |
Issue Date: | 2016 |
Publisher: | Springer Science+Business Media |
Source: | Awolusi, O. O. 2016. Artificial intelligence for the evaluation of operational parameters influencing Nitrification and Nitrifiers in an activated sludge process. Environmental Microbiology. 1-15. |
Journal: | Environmental microbiology (Online) |
Abstract: | Abstract Nitrification at a full-scale activated sludge plant treating municipal wastewater was monitored over a period of 237 days. A combination of fluorescent in situ hybridiza-tion (FISH) and quantitative real-time polymerase chain reac-tion (qPCR) were used for identifying and quantifying the dominant nitrifiers in the plant. Adaptive neuro-fuzzy infer-ence system (ANFIS), Pearson’s correlati... |
URI: | http://hdl.handle.net/10321/2346 |
ISSN: | 1462-2912 (print) 1462-2920 (online) |
DOI: | 10.1007/s00248-016-0739-3 |
Appears in Collections: | Research Publications (Applied Sciences) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Awolusi_EM_Vol72No1_15Page_2016.pdf | 1.19 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.