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

Microb Ecol. 2016 Jul;72(1):49-63. doi: 10.1007/s00248-016-0739-3. Epub 2016 Feb 23.


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.

Keywords: Adaptive neuro-fuzzy inference system; Ammonia-oxidizing bacteria; Nitrite-oxidizing bacteria; Operational parameters; Statistical tools.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Ammonia / metabolism
  • Artificial Intelligence*
  • Bacteria / classification*
  • Bacteria / isolation & purification
  • DNA, Bacterial / genetics
  • In Situ Hybridization, Fluorescence
  • Models, Theoretical
  • Nitrification*
  • Nitrobacter / classification
  • Real-Time Polymerase Chain Reaction
  • Sewage / microbiology*
  • Temperature
  • Waste Management


  • DNA, Bacterial
  • Sewage
  • Ammonia