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

M.A. Hussain; M.K. Aroua; C-Y Yin; R.A. Rahman; N.A. Ramli

doi:10.1007/s11814-010-0270-z

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

Journal article

Peer reviewed

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

M.A. Hussain, M.K. Aroua, C-Y Yin, R.A. Rahman and N.A. Ramli

Korean Journal of Chemical Engineering, Vol.27(6), pp.1864-1867

2010

DOI: https://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.

Details

Title: Hybrid neural network for prediction of CO2 solubility in monoethanolamine and diethanolamine solutions
Authors/Creators: M.A. Hussain (Author/Creator) - University of Malaya
M.K. Aroua (Author/Creator) - University of Malaya
C-Y Yin (Author/Creator) - Universiti Teknologi MARA
R.A. Rahman (Author/Creator) - University of Malaya
N.A. Ramli (Author/Creator) - University of Malaya
Publication Details: Korean Journal of Chemical Engineering, Vol.27(6), pp.1864-1867
Publisher: Springer Verlag
Identifiers: 991005542174707891
Copyright: 2010 M.A. Hussain et al
Murdoch Affiliation: Murdoch University
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration
Citation topics: 7 Engineering & Materials Science; 7.139 Energy & Fuels; 7.139.835 CO2 Capture
Web Of Science research areas: Chemistry, Multidisciplinary; Engineering, Chemical
ESI research areas: Chemistry