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Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received July 9, 2009
Accepted February 15, 2010

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Hybrid neural network for prediction of CO2 solubility in monoethanolamine and diethanolamine solutions

Mohd Azlan Hussain Mohamed Kheireddine Aroua^† Chun-Yang Yin¹ Ramzalina Abd Rahman Noor Asriah Ramli

Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia ¹Faculty of Chemical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

Korean Journal of Chemical Engineering, November 2010, 27(6), 1864-1867(4), 10.1007/s11814-010-0270-z

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Abstract

The solubility of CO2 in single monoethanolamine (MEA) and diethanolamine (DEA) solutions was predicted by a model developed based on the Kent-Eisenberg model in combination with a neural network. The combination forms a hybrid neural network (HNN) model. Activation functions used in this work were purelin, logsig and tansig. After training, testing and validation utilizing different numbers of hidden nodes, it was found that a neural network with a 3-15-1 configuration provided the best model to predict the deviation value of the loading input. The accuracy of data predicted by the HNN model was determined over a wide range of temperatures (0 to 120 ℃), equilibrium CO2 partial pressures (0.01 to 6,895 kPa) and solution concentrations (0.5 to 5.0M). The HNN model could be used to accurately predict CO2 solubility in alkanolamine solutions since the predicted CO2 loading values from the model were in good agreement with experimental data.

Keywords

Diethanolamine Monoethanolamine CO2 Solubility Kent-Eisenberg Model Hybrid Neural Network

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