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

• Main Authors: Hussain, Mohd Azlan, Aroua, Mohamed Kheireddine, Yin, Chun Yang, Rahman, Ramzalina Abd, Ramli, Noor Asriah
• Format: Article
• Published: Springer Verlag 2010
• Subjects: TP Chemical technology
• Online Access: http://eprints.um.edu.my/7028/; https://doi.org/10.1007/s11814-010-0270-z
• Institution: Universiti Malaya

The solubility of CO 2 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 CO 2 partial pressures (0.01 to 6,895 kPa) and solution concentrations (0.5 to 5.0 M). The HNN model could be used to accurately predict CO 2 solubility in alkanolamine solutions since the predicted CO 2 loading values from the model were in good agreement with experimental data.