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

Hussain, M.A., Aroua, M.K., Yin, C-Y, Rahman, R.A. and Ramli, N.A. (2010) Hybrid neural network for prediction of CO2 solubility in monoethanolamine and diethanolamine solutions. Korean Journal of Chemical Engineering, 27 (6). pp. 1864-1867.

Link to Published Version: http://dx.doi.org/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 °C), 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.

Publication Type: Journal Article
Publisher: Springer Verlag
Copyright: 2010 M.A. Hussain et al
URI: http://researchrepository.murdoch.edu.au/id/eprint/5303
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