Please use this identifier to cite or link to this item:
https://hdl.handle.net/10321/2346
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Awolusi, Oluyemi Olatunji | en_US |
dc.contributor.author | Nasr, Mahmoud | en_US |
dc.contributor.author | Kumari, Sheena K. | en_US |
dc.contributor.author | Bux, Faizal | en_US |
dc.date.accessioned | 2017-03-10T05:38:00Z | |
dc.date.available | 2017-03-10T05:38:00Z | |
dc.date.issued | 2016 | - |
dc.identifier.citation | 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. | en_US |
dc.identifier.issn | 1462-2912 (print) | - |
dc.identifier.issn | 1462-2920 (online) | - |
dc.identifier.uri | http://hdl.handle.net/10321/2346 | - |
dc.description.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 correlation coefficient, and quadratic models were employed in evaluating the plant oper-ational conditions that influence the nitrification performance. The ammonia-oxidizing bacteria (AOB) abundance was with-in the range of 1.55 × 108–1.65 × 1010 copies L−1, while Nitrobacter spp. and Nitrospira spp. were 9.32 × 109–1.40 × 1011 copies L− 1 and 2.39 × 109 –3.76 × 1010 copies L−1, respectively. Specific nitrification rate (qN)was significantly affected by temperature (r 0.726, p 0.002), hy-draulic retention time (HRT) (r −0.651, p 0.009), and ammo-nia loading rate (ALR) (r 0.571, p 0.026). Additionally, AOB was considerably influenced by HRT (r −0.741, p 0.002) and temperature (r 0.517, p 0.048), while HRT negatively impact-ed Nitrospira spp. (r −0.627, p 0.012). A quadratic combina-tion of HRT and food-to-microorganism (F/M) ratio also im-pacted qN (r2 0.50), AOB (r2 0.61), and Nitrospira spp. (r2 0.72), while Nitrobacter spp. was considerably influenced by a polynomial function of F/M ratio and temperature (r2 0.49). The study demonstrated that ANFIS could be used as a tool to describe the factors influencing nitrification process at full-scale wastewater treatment plants. | en_US |
dc.format.extent | 15 p | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Science+Business Media | en_US |
dc.relation.ispartof | Environmental microbiology (Online) | en_US |
dc.subject | Adaptive neuro-fuzzy inference system | en_US |
dc.subject | Ammonia-oxidizing bacteria | en_US |
dc.subject | Nitrite-oxidizing bacteria | en_US |
dc.subject | Operational parameters | en_US |
dc.subject | Statistical tools | en_US |
dc.title | Artificial intelligence for the evaluation of operational parameters influencing Nitrification and Nitrifiers in an activated sludge process | en_US |
dc.type | Article | en_US |
dc.dut-rims.pubnum | DUT-005443 | en_US |
dc.identifier.doi | 10.1007/s00248-016-0739-3 | - |
local.sdg | SDG06 | - |
local.sdg | SDG15 | - |
local.sdg | SDG11 | - |
item.languageiso639-1 | en | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | open | - |
item.openairetype | Article | - |
item.cerifentitytype | Publications | - |
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 |
Page view(s) 1
4,834
checked on Dec 22, 2024
Download(s) 20
1,183
checked on Dec 22, 2024
Google ScholarTM
Check
Altmetric
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.