Research Article | OPEN ACCESS
Optimization of Anaerobic Treatment of Petroleum Refinery Wastewater Using Artificial Neural Networks
1H.A. Gasim, 1S.R.M. Kutty, 1M. Hasnain Isa and 2L.T. Alemu
1Department of Civil Engineering
2Department of Computer and Information Sciences, Universiti Teknologi
PETRONAS (UTP), Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia
Research Journal of Applied Sciences, Engineering and Technology 2013 11:2077-2082
Received: September 27, 2012 | Accepted: November 08, 2012 | Published: July 25, 2013
Abstract
Treatment of petroleum refinery wastewater using anaerobic treatment has many advantages over other biological method particularly when used to treat complex wastewater. In this study, accumulated data of Up-flow Anaerobic Sludge Blanket (UASB) reactor treating petroleum refinery wastewater under six different volumetric organic loads (0.58, 1.21, 0.89, 2.34, 1.47 and 4.14 kg COD/m3·d, respectively) were used for developing mathematical model that could simulate the process pattern. The data consist of 160 entries and were gathered over approximately 180 days from two UASB reactors that were continuously operating in parallel. Artificial neural network software was used to model the reactor behavior during different loads applied. Two transfer functions were compared and different number of neurons was tested to find the optimum model that predicts the reactor pattern. The tangent sigmoid transfer function (tansig) at hidden layer and a linear transfer function (purelin) at output layer with 12 neurons were selected as the optimum best model.
Keywords:
Anaerobic digestion, artificial neural networks, chemical oxygen demand, petroleum refinery wastewater, UASB,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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