APPLICATION OF ARTIFICIAL NEURAL NETWORK IN HORIZONTAL SUBSURFACE FLOW CONSTRUCTED WETLAND FOR NUTRIENT REMOVAL PREDICTION


Ozengin N. , Elmaci A., YONAR T.

APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH, vol.14, no.4, pp.305-324, 2016 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 4
  • Publication Date: 2016
  • Doi Number: 10.15666/aeer/1404_305324
  • Title of Journal : APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH
  • Page Numbers: pp.305-324
  • Keywords: artificial neural networks, constructed wetlands, LECA, Levenberg-Marquardt algorithm, Phragmites australis, wastewater treatment, CHEMICAL OXYGEN-DEMAND, MUNICIPAL WASTE-WATER, LABORATORY-SCALE, PHOSPHORUS, NITROGEN, DESIGN, PHOSPHATE, SELECTION, CAPACITY, PLANTS

Abstract

The aim of this study is to determine the appropriateness of the field measurements for the effectiveness of nutrients removal of Phragmites australis (Cav.) Trin. Ex. Steudel by applying artificial neural network (ANN) and also evaluate the removal capacity of LECA (light expanded clay aggregate) in a horizontal subsurface flow constructed wetland (SSFW). Two laboratory scale reactors were operated with weak and strong synthetic domestic wastewater continuously. One unit was planted with P. australis and the other unit remained unplanted (control reactor). The best performance was achieved with strong domestic wastewater treatment, the average removal efficiencies obtained from the evaluation of the system were 70.15% and 65.29% for TN, 66% and 57.4% for NH4-N, 61.64% and 67.37% for TP and, 66.52% and 51.7% for OP in planted and unplanted reactors, respectively. The average NO3- concentration was 0.90 mg l(-1) in the influent and 0.47 mg l(-1) and 0.60 mg l(-1) from planted and unplanted reactors, respectively. The average NO2- concentration was 0.80 mg l(-1) in the influent and 0.56 mg l-1 and 0.88 mg l(-1) from planted and unplanted reactors, respectively. Based on the obtained results, this system has potential to be an applicable system to treat strong domestic wastewater. The data obtained in this study was assessed using NeuroSolutions 5.06 model. Each sample was characterized using eight independent variables (hydraulic retention time (HRT), dissolved oxygen (DO), pH, temperature (T), ammonium-nitrogen (NH4-N), nitrate (NO3-), nitrite (NO2-), ortho-phosphate (OP), and two dependent variable (total nitrogen (TN) and total phosphorus (TP)). The correlation coefficients between the neural network estimates and field measurements were as high as 0.9463 and 0.9161 for TN and TP, respectively. The results indicated that the adopted Levenberg-Marquardt back-propagation algorithm yields satisfactory estimates with acceptably low MSE values. Besides, the support matrix may play an important role in the system. The constructed wetland planted with P. australis and with LECA as a support matrix may be a good option to encourage and promote the prevention of environmental pollution.