Total nitrogen and ammonia removal prediction in horizontal subsurface flow constructed wetlands: use of artificial neural networks and development of a design equation.
The aim of this paper is to examine if artificial neural networks (ANNs) can predict nitrogen removal in horizontal subsurface flow (HSF) constructed wetlands (CWs). ANN development was based on experimental data from five pilot-scale CW units. The proper selection of the components entering the ANN was achieved using principal component analysis (PCA), which identified the main factors affecting TN removal, i.e., porous media porosity, wastewater temperature and hydraulic residence time. Two neural networks were examined: the first included only the three factors selected from the PCA, and the second included in addition meteorological parameters (i.e., barometric pressure, rainfall, wind speed, solar radiation and humidity). The first model could predict TN removal rather satisfactorily (R(2)=0.53), and the second resulted in even better predictions (R(2)=0.69). From the application of the ANNs, a design equation was derived for TN removal prediction, resulting in predictions comparable to those of the ANNs (R(2)=0.47). For the validation of the results of the ANNs and of the design equation, available data from the literature were used and showed a rather satisfactory performance.
Akratos
Christos S
CS
Laboratory of Ecological Engineering and Technology, Department of Environmental Engineering, School of Engineering, Democritus University of Thrace, 67100 Xanthi, Greece.
Papaspyros
John N E
JN
Tsihrintzis
Vassilios A
VA
D000465:Algorithms; D000641:Ammonia / Q000302:isolation & purification*; D003198:Computer Simulation; D017753:Ecosystem; D008954:Models, Biological; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000302:isolation & purification*; D012212:Rheology / Q000379:methods*; D018508:Water Purification / Q000379:methods*; D053833:Wetlands
D000641:Ammonia; D009584:Nitrogen