Modeling The Toxicity of Textile Industry Wastewater Using Artificial Neural Networks
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Dosyalar
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Toxicity tests are required to detect the possible effects of pollutants on organisms. This study investigates the effect of Chemical Oxygen Demand (COD), suspended solid (SS) and pH parameters on toxicity of textile industry wastewaters except for the color parameter, effect of which is well known. Fish bioassay taking place in legal regulation of Turkey was used as toxicity test. At the end of the toxicity test, various values of the parameters were predicted through Artificial Neural Networks (ANN). In addition, Artificial Neural Networks were used to calculate the effect of each parameter on toxicity (%). Accordingly, COD is the parameter which mostly affects toxicity following color parameter and SS is the parameter which has the minimum effect. It is found that results deviate at the rate of 15.41% when values of COD parameter are excluded from the model input data and the error rate becomes 5.07% when SS parameter is excluded. In this study, the effect of each input of each parameter, which is an open ecosystem, based on selected parameters is successfully predicted through Artificial Neural Networks which is a heuristic method.
Açıklama
Scientific Meeting on Electric Electronics, Computer Science, Biomedical Engineerings (EBBT) -- APR 20-21, 2017 -- Istanbul Arel Univ, Istanbul, TURKEY
Sivri, Nuket/0000-0002-4269-5950
WOS: 000413686600013
Sivri, Nuket/0000-0002-4269-5950
WOS: 000413686600013
Anahtar Kelimeler
Artificial Neural Networks (ANN), textile wastewater, acute toxicity, fish bioassay, Chemical Oxygen Demand (COD), Suspended Solids (SS), Color, pH
Kaynak
2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (Ebbt)
WoS Q Değeri
N/A
