A neural network approach to predict activity coefficients |
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| Authors: | Nazario D Ramírez‐Beltrán Harry Rodríguez Vallés L Antonio Estévez Horacio Duarte |
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| Affiliation: | 1. Department of Industrial Engineering, University of Puerto Rico, Mayaguez, Puerto Rico 00680;2. Cordis LLC a Johnson & Johnson Company, San German, Puerto Rico 00683;3. Department of Chemical Engineering, University of Puerto Rico, Mayaguez, Puerto Rico 00680;4. Department of Chemical Engineering, Texas A&M University, Kingsville, Texas |
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| Abstract: | 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. |
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| Keywords: | VLE activity coefficient neural networks functional groups UNIFAC thermodynamic consistency |
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