Research Article

Estimation Of Organic Pollution (COD) Of Copper (Cu2 +) Biosorption To Tobacco Leaf With Artificial Neural Network

Volume: 20 Number: 4 December 27, 2017
Kahramanmaras Sutcu Imam University Journal of Engineering Sciences Cover
Kahramanmaras Sutcu Imam University Journal of Engineering Sciences
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Estimation Of Organic Pollution (COD) Of Copper (Cu2 +) Biosorption To Tobacco Leaf With Artificial Neural Network

Abstract

In this study, prediction of Cu2+ biosorption to tobacco leaf, and the organic pollution (COD) created by this process on the biosorption system using Artificial Neural Network (ANN) models was investigated. In this scope, 26 data obtained from the biosorption process from the model were normalized and used in the comparison. For the development of the model, data regarding pH, temperature, and biosorbent dosage were used in the input layer, and COD was predicted in the output layer. In this model, five different models were created, and the most suitable model was determined with 3-4-1 structure and 0,991 R2 value. High R2 values were also obtained in other model structures. The statistical comparison of the predictions obtained from ANN models with the measurement results shows that the models have good predicting capability and can be used for prediction purpose. 

Keywords

References

  1. Referans1
  2. Yard.Doç.Dr. Sayiter YILDIZ
  3. Referans2
  4. Yard.Doç.Dr. Turgay Dere

Details

Primary Language

Turkish

Subjects

Environmental Engineering

Journal Section

Research Article

Authors

Publication Date

December 27, 2017

Submission Date

September 29, 2017

Acceptance Date

December 21, 2017

Published in Issue

Year 2017 Volume: 20 Number: 4

DOI

https://doi.org/10.17780/ksujes.340546

IZ

https://izlik.org/JA63TJ65CX

Cite

RIS / Bibtex
APA
Cekim, M. (2017). Tütün Yaprağına Bakır (Cu2+) Biyosorpsiyonu Sonucu Oluşan Organik Kirliliğin (KOİ) Yapay Sinir Ağları İle Tahmin Edilmesi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 20(4), 181-189. https://doi.org/10.17780/ksujes.340546