Artificial Intelligence for the Evaluation of Operational Parameters Influencing Nitrification and Nitrifiers in an Activated Sludge Process

Oluyemi Olatunji Awolusi, Mahmoud Nasr, Sheena Kumari, Faizal Bux

Research output: Contribution to journalArticle

10 Citations (Scopus)

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 hybridization (FISH) and quantitative real-time polymerase chain reaction (qPCR) were used for identifying and quantifying the dominant nitrifiers in the plant. Adaptive neuro-fuzzy inference system (ANFIS), Pearson’s correlation coefficient, and quadratic models were employed in evaluating the plant operational conditions that influence the nitrification performance. The ammonia-oxidizing bacteria (AOB) abundance was within 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), hydraulic retention time (HRT) (r −0.651, p 0.009), and ammonia 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 impacted Nitrospira spp. (r −0.627, p 0.012). A quadratic combination of HRT and food-to-microorganism (F/M) ratio also impacted 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.

Original languageEnglish
Pages (from-to)49-63
Number of pages15
JournalMicrobial Ecology
Volume72
Issue number1
DOIs
Publication statusPublished - 1 Jul 2016
Externally publishedYes

Fingerprint

artificial intelligence
activated sludge
nitrification
fluid mechanics
ammonia
Nitrobacter
hydraulics
bacterium
bacteria
microorganism
microorganisms
temperature
food
fluorescence in situ hybridization
wastewater treatment
polymerase chain reaction
quantitative polymerase chain reaction
wastewater
evaluation
parameter

Keywords

  • Adaptive neuro-fuzzy inference system
  • Ammonia-oxidizing bacteria
  • Nitrite-oxidizing bacteria
  • Operational parameters
  • Statistical tools

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Soil Science

Cite this

Artificial Intelligence for the Evaluation of Operational Parameters Influencing Nitrification and Nitrifiers in an Activated Sludge Process. / Awolusi, Oluyemi Olatunji; Nasr, Mahmoud; Kumari, Sheena; Bux, Faizal.

In: Microbial Ecology, Vol. 72, No. 1, 01.07.2016, p. 49-63.

Research output: Contribution to journalArticle

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