Neural Network Modeling of The Sulfur Dioxide Removal by Activated Carbon Sorbent

Safa A. Al-Naimi; Neran K. Ibrahim; Afraa H. Kamel

doi:10.24237/djes.2016.09211

Authors

Safa A. Al-Naimi Chemical Engineering Department, University of Technology, Baghdad, Iraq
Neran K. Ibrahim Chemical Engineering Department, University of Technology
Afraa H. Kamel Chemical Engineering Department, University of Technology, Baghdad, Iraq

Keywords:

Artificial Neural Networks, Sorption, Removal Efficiency

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.

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