화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Korean Journal of Chemical Engineering, Vol.27, No.6, 1864-1867, November, 2010
DOI10.1007/s11814-010-0270-z  Export Citation
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, and 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
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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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