Publication:
Application of artificial neural network in horizontal subsurface flow constructed wetland for nutrient removal prediction

dc.contributor.buuauthorÖzengin, Nihan
dc.contributor.buuauthorElmacı, Ayşe
dc.contributor.buuauthorYonar, Taner
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentÇevre Mühendisliği Bölümü
dc.contributor.orcid0000-0002-0387-0656
dc.contributor.orcid0000-0002-1762-1140
dc.contributor.researcheridAAH-1475-2021
dc.contributor.researcheridAAD-9468-2019
dc.contributor.researcheridAAG-9866-2021
dc.contributor.scopusid16231232500
dc.contributor.scopusid16230326600
dc.contributor.scopusid6505923781
dc.date.accessioned2023-03-07T11:07:43Z
dc.date.available2023-03-07T11:07:43Z
dc.date.issued2016-07-19
dc.descriptionBu çalışma, Bursa Uludağ Üniversitesi Fen Bilimleri Enstitüsü Ayşe Elmacı'in danışmanlığında Nihan Özengin tarafından yazılan "Farmasötik ürünlerinin sulak alan sisteminde arıtılabilirliğinin araştırılması" adlı doktora tezine dayanılarak hazırlanmıştır.
dc.description.abstractThe 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.
dc.identifier.citationÖzengin, N. vd. (2016). "Application of artificial neural network in horizontal subsurface flow constructed wetland for nutrient removal prediction". Applied Ecology and Environmental Research, 14(4), 305-324.
dc.identifier.endpage324
dc.identifier.issn1589-1623
dc.identifier.issn1785-0037
dc.identifier.issue4
dc.identifier.scopus2-s2.0-84995695025
dc.identifier.startpage305
dc.identifier.urihttps://doi.org/10.15666/aeer/1404_305324
dc.identifier.urihttps://www.aloki.hu/pdf/1404_305324.pdf
dc.identifier.urihttp://hdl.handle.net/11452/31396
dc.identifier.volume14
dc.identifier.wos000387850600019
dc.indexed.wosSCIE
dc.language.isoen
dc.publisherCorvinus University Budapest
dc.relation.bap2010/52
dc.relation.journalApplied Ecology and Environmental Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectEnvironmental sciences & ecology
dc.subjectArtificial neural networks
dc.subjectConstructed wetlands
dc.subjectLECA
dc.subjectLevenberg-Marquardt algorithm
dc.subjectPhragmites australis
dc.subjectWastewater treatment
dc.subjectChemical oxygen-demand
dc.subjectMunicipal waste-water
dc.subjectLaboratory-scale
dc.subjectPhosphorus
dc.subjectNitrogen
dc.subjectDesign
dc.subjectPhosphate
dc.subjectSelection
dc.subjectCapacity
dc.subjectPlants
dc.subject.scopusConstructed Wetlands; Waste Water; Nitrogen Removal
dc.subject.wosEcology
dc.subject.wosEnvironmental sciences
dc.titleApplication of artificial neural network in horizontal subsurface flow constructed wetland for nutrient removal prediction
dc.typeArticle
dc.wos.quartileQ4
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Çevre Mühendisliği Bölümü
local.indexed.atScopus
local.indexed.atWOS

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Özengin_vd_2016.pdf
Size:
523.24 KB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
Placeholder
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: