A NEURAL NETWORK APPROACH TO PREDICT ACTIVITY COEFFICIENTS

作者：Ramirez Beltran Nazario D^*; Valles Harry Rodriguez; Estevez L Antonio; Duarte Horacio

来源：Canadian Journal of Chemical Engineering, 2009, 87(5): 748-760.

DOI：10.1002/cjce.20212

• VLE
• activity coefficient
• neural networks
• functional groups
• UNIFAC
• thermodynamic consistency

Artificial neural networks (ANNs) and a group-contribution approach were used to develop an algorithm to predict activity coefficients for binary solutions. The Levenberg-Marquardt algorithm was used to train the ANN and to predict the parameters of the Margules equation. The ANN was trained using phase-equilibrium database from DECHEMA. The selected systems include alcohols, phenols, aldehydes, ketones, and ethers. The trim mean based on 20% data elimination was selected as the best representation of the Margules-equation parameters. The algorithm was validated with 121 VLE systems and results show that the ANN provides a relative improvement over the UNIFAC method.

• 出版日期2009-10