Side-by-side comparison of horizontal subsurface flow and free water surface flow constructed wetlands and artificial neural network (ANN) modelling approach |
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| Authors: | Muhsin Naz Sinan Uyanik M. Irfan Yesilnacar Erkan Sahinkaya |
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| Affiliation: | 1. Department of Bioscience, Plant Biology, Aarhus University, Ole Worms Allé 1, Building 1135, 8000 Aarhus C, Denmark;2. Helmholtz Center for Environmental Research (UFZ), Environmental and Biotechnology Center (UBZ), Permoserstrasse 15, 04318 Leipzig, Germany;3. Bauer Nimr LLC, P.O. Box 1186, Al Mina, Muscat, Oman;4. Naturally Wallace Consulting LLC, P.O. Box 2236, 109 E. Myrtle Street, Stillwater, MN 55082, USA;1. College of Resources and Environment, Northwest A & F University, Yangling, Shaanxi 712100, China;2. Shandong Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science & Engineering, Shandong University, Jinan 250100, PR China;3. School of Civil and Environmental Engineering, University of Technology, Sydney, Broadway, NSW 2007, Australia;4. National Engineering Laboratory of Coal-Fired Pollutants Emission Reduction, Shandong University, Jinan 250061, PR China;1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China;2. Jiangxi Center Station of Irrigation Experiment, Nanchang 330201, China;1. College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi, 712100, China;2. Shandong Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science & Engineering, Shandong University, Jinan, 250100, China;3. School of Civil and Environmental Engineering, University of Technology Sydney, Broadway, NSW, 2007, Australia |
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| Abstract: | A horizontal subsurface flow (HSSF) and a free water surface flow (FWSF) constructed wetlands (4 m2 of each) were set up on the campus of Harran University, Sanliurfa, Turkey. The main objective of the research was to compare the performance of two systems to decide the better one for future planning of wastewater treatment system on the campus. Both of the wetland systems were planted with Phragmites australis and Canna indica. During the observation period (10 months), environmental conditions such as pH, temperature and total chemical oxygen demand (COD), soluble COD, total biochemical oxygen demand (BOD), soluble BOD, total suspended solids (TSS), total phosphate (TP), total nitrogen (TN) removal efficiencies of the systems were determined. According to the results, average yearly removal efficiencies for the HSSF and the FWSF, respectively, were as follows: total COD (75.7% and 69.9%), soluble COD (85.4% and 84.3%), total BOD (79.6% and 87.6%), soluble BOD (87.7% and 95.3%), TN (33.2% and 39.4%), and TP (31.5% and 6.5%). Soluble COD and BOD removal efficiencies of both systems increased gradually since the start-up. After nine months of operation, above 90% removal of organic matters were observed. The treatment performances of the HSSF were better than that of the FWSF with regard to the removal of suspended solids and total COD at especially high temperatures. In FWSF systems, COD concentrations extremely exceeded the discharge limit values due to high concentrations of algae in spring months.The performance of the two systems was modelled using an artificial neural network-back-propagation algorithm. The ANN model was competent at providing reasonable match between the measured and the predicted concentrations of total COD (R = 0.90 for HSSF and R = 0.96 for FWSF), soluble COD (R = 0.90 for HSSF and R = 0.74 for FWSF) and total BOD (R = 0.94 for HSSF and R = 0.84 for FWSF) in the effluents of constructed wetlands. |
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