Prediction of binodal curve of H2O/DMAc/PSf system by ANN method

Xue Zhong He, Xiang Ping Zhang, Jin Dun Liu, Suo Jiang Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Based on the literature data, a Back-Propagation (BP) network was designed for prediction of the liquid-liquid equilibrium properties of H2O/DMAc (A'-A'-dimethylacetamide)/PSf (polysulfone) system in terms of the ANN (Artificial Neural Network) theory. Two input neurons (temperature and mass fraction of PSf) and two output neurons (mass fractions of water and DMAc) have been chosen as variables of the network. The results show that predicted ARD (average relative deviation) values of water and DMAc concentrations are 1.86% and 0.10%, respectively, therefore the proposed ANN method in this work could be used to predict the binodal curve of H2O/DMAc/PSf system in the temperature range of 20-60°C. From this work, it can be concluded that the properly selected and trained network provides an effective method for prediction of the liquid-liquid equilibrium properties of H2O/DMAc/PSf system, which could be used for guiding experimental researches in preparation of asymmetry polymer membrane.

Original languageEnglish
Pages (from-to)178-181
Number of pages4
JournalGuocheng Gongcheng Xuebao/The Chinese Journal of Process Engineering
Issue number2
StatePublished - Apr 2004
Externally publishedYes


  • Binodal curve
  • Liquid-liquid equilibrium
  • Neural network
  • Polymer system
  • Prediction


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