Artificial intelligence for the evaluation of operational parameters influencing Nitrification and Nitrifiers in an activated sludge process

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.