Mathematical modeling of wastewater-derived biodegradable dissolved organic nitrogen

Wastewater-derived dissolved organic nitrogen (DON) typically constitutes the majority of total dissolved nitrogen (TDN) discharged to surface waters from advanced wastewater treatment plants (WWTPs). When considering the stringent regulations on nitrogen discharge limits in sensitive receiving waters, DON becomes problematic and needs to be reduced. Biodegradable DON (BDON) is a portion of DON that is biologically degradable by bacteria when the optimum environmental conditions are met. BDON in a two-stage trickling filter WWTP was estimated using artificial intelligence techniques, such as adaptive neuro-fuzzy inference systems, multilayer perceptron, radial basis neural networks (RBNN), and generalized regression neural networks. Nitrite, nitrate, ammonium, TDN, and DON data were used as input neurons. Wastewater samples were collected from four different locations in the plant. Model performances were evaluated using root mean square error, mean absolute error, mean bias error, and coefficient of determination statistics. Modeling results showed that the R(2) values were higher than 0.85 in all four models for all wastewater samples, except only R(2) in the final effluent sample for RBNN modeling was low (0.52). Overall, it was found that all four computing techniques could be employed successfully to predict BDON.