title: Artificial Intelligence for the Evaluation of Operational Parameters Influencing Nitrification and Nitrifiers in an Activated Sludge Process.
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 × 10(8)-1.65 × 10(10) copies L(-1), while Nitrobacter spp. and Nitrospira spp. were 9.32 × 10(9)-1.40 × 10(11) copies L(-1) and 2.39 × 10(9)-3.76 × 10(10) 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 (r (2) 0.50), AOB (r (2) 0.61), and Nitrospira spp. (r (2) 0.72), while Nitrobacter spp. was considerably influenced by a polynomial function of F/M ratio and temperature (r (2) 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.
authors.affiliation: Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4000, South Africa.
authors.forename: Oluyemi Olatunji
authors.initials: OO
authors.lastname: Awolusi
authors.affiliation: Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4000, South Africa.
authors.forename: Mahmoud
authors.initials: M
authors.lastname: Nasr
authors.affiliation: Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4000, South Africa.
authors.forename: Sheena
authors.initials: S
authors.lastname: Kumari
authors.affiliation: Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4000, South Africa. faizalb@dut.ac.za.
authors.forename: Faizal
authors.initials: F
authors.lastname: Bux
mesh_terms: D000641:Ammonia / Q000378:metabolism; D001185:Artificial Intelligence; D001419:Bacteria / Q000145:classification* / Q000302:isolation & purification; D004269:DNA, Bacterial / Q000235:genetics; D017404:In Situ Hybridization, Fluorescence; D008962:Models, Theoretical; D058465:Nitrification; D009576:Nitrobacter / Q000145:classification; D060888:Real-Time Polymerase Chain Reaction; D012722:Sewage / Q000382:microbiology*; D013696:Temperature; D018505:Waste Management
chemical_list: D004269:DNA, Bacterial; D012722:Sewage; D000641:Ammonia
keywords: Adaptive neuro-fuzzy inference system; Ammonia-oxidizing bacteria; Nitrite-oxidizing bacteria; Operational parameters; Statistical tools