Modeling of Ammonium and COD Adsorption in Aqueous Solutions Using an Artificial Neural Network

DOI

10.2991/isees-19.2019.42How to use a DOI?

Keywords

adsorption; artificial neural networks; modeling; ammonium.

Abstract

This paper illustrates the application of the artificial neural network for adsorption of ammonium NH4+ and COD from fish farm by rice straw as low cost carbonaceous. The effects of input parameters (contact time, pH, initial concentration of NH4+ and COD, adsorbent dosages, and temperature) are studied to optimize the conditions for maximum removal of NH4+ and COD. The artificial neural network with a single hidden layer with ten nodes trained with Levenberg-Marquardt algorithm predicted the removal efficiency of NH4+ and COD from aqueous solution accurately.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ah.S. Khalil
AU  - A.S. Smolyanichenko
AU  - E.V. Vilson
AU  - E.E. Shchutskaya
AU  - E.G. Tsurikova
PY  - 2019/08
DA  - 2019/08
TI  - Modeling of Ammonium and COD Adsorption in Aqueous Solutions Using an Artificial Neural Network
BT  - Proceedings of the International Symposium "Engineering and Earth Sciences: Applied and Fundamental Research" dedicated to the 85th anniversary of H.I. Ibragimov (ISEES 2019)
PB  - Atlantis Press
SP  - 610
EP  - 616
SN  - 2590-3217
UR  - https://doi.org/10.2991/isees-19.2019.42
DO  - 10.2991/isees-19.2019.42
ID  - Khalil2019/08
ER  -