Diyala Journal of Engineering Sciences DJES

Volume 9, No. 1, Pages: 85-95,  June 2016

(Received: 4/5/2015; Accepted: 26/10/2015)

NEURAL NETWORK MODELING OF THE SULFUR DIOXIDE REMOVAL BY ACTIVATED CARBON SORBENT

Safa A. Al-Naimi

Chemical Engineering Department, University of Technology

Neran K. Ibrahim

Chemical Engineering Department, University of Technology

Afraa H. Kamel

Chemical Engineering Department, University of Technology

DOI: https://dx.doi.org/10.24237/djes.2016.09211

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ABSTRACT

An artificial neural network (ANN) model of three-layers was advanced to predict the efficiency of the sulfur dioxide (SO₂) removal from the flue gas stream (SO₂+air) in a fixed bed reactor using granulated activated carbon sorbent. The experimental data were collected from varying six process variables, namely, initial SO₂ concentration, reaction temperature, flue gas flow rate, sorbent particle size, bed height and reaction time. The data were used to create input-base information to train and test the NN strategy.  Back propagation algorithm with two hidden layers was used for training and tests the NN. The neural network predictions of SO₂ removal efficiency agree with experimental data with the minimum mean squared error (MSE) for training and testing with values of 0.112*10^-4 and 0.817*10^-3, respectively.

Keywords: Artificial Neural Networks, Sorption, Removal Efficiency

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