Mathematical modeling of wastewater-derived biodegradable dissolved organic nitrogen
| Autor: | Halis Simsek |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Mean squared error
Nitrogen Trickling filter 0208 environmental biotechnology chemistry.chemical_element 02 engineering and technology 010501 environmental sciences Wastewater 01 natural sciences Waste Disposal Fluid chemistry.chemical_compound Nitrate Ammonium Compounds Environmental Chemistry Ammonium Nitrite Waste Management and Disposal Nitrites 0105 earth and related environmental sciences Water Science and Technology Nitrates Bacteria Environmental engineering General Medicine Models Theoretical 020801 environmental engineering Biodegradation Environmental chemistry Sewage treatment Neural Networks Computer Water Pollutants Chemical |
| DOI: | 10.6084/m9.figshare.3125968 |
| Popis: | 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 R2 values were higher than 0.85 in all four models for all wastewater samples, except only R2 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. |
| Databáze: | OpenAIRE |
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