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Vapor-liquid phase equilibria behavior prediction of water/organic-organic binary mixture using machine learning
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  • Guanlun Sun,
  • Zhenyu Zhao,
  • Shengjie Sun,
  • Yiming Ma,
  • Hong Li,
  • Xin Gao
Guanlun Sun
Tianjin University School of Chemical Engineering and Technology
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Zhenyu Zhao
Tianjin University School of Chemical Engineering and Technology
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Shengjie Sun
Qingdao University
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Yiming Ma
Tianjin University
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Hong Li
National Engineering Research Ceter of Distillation Technology
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Xin Gao
Tianjin university

Corresponding Author:gaoxin@tju.edu.cn

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Basic thermodynamic data plays an important role in chemical applications. However, the traditional acquisition of thermodynamic data through experiments is laborious. Thermodynamic data prediction is considered as an alternative to the experiments, especially when qualitative analysis is needed prior to experimental studies. In this work, we report a successful machine-learning based approach to predict the fundamental thermodynamics characteristics of vapor-liquid equilibrium (VLE) process. A new dataset of the VLE experimental data of 210 kinds of binary mixture with screened descriptors were constructed. The obtained results show that the VLE characteristics of the target system can be fully revealed for a pre-analysis by ML methods and the RF model has more excellent predictive ability on the VLE behavior than the ANN model. This work pioneers the development of the generalized model on the prediction of the VLE data and provide useful information for mechanistic study on the VLE phenomenon.
18 Feb 2023Submitted to AIChE Journal
24 Feb 2023Submission Checks Completed
24 Feb 2023Assigned to Editor
24 Feb 2023Review(s) Completed, Editorial Evaluation Pending
11 Mar 2023Reviewer(s) Assigned
08 Apr 2023Editorial Decision: Revise Major
25 Apr 20231st Revision Received
25 Apr 2023Submission Checks Completed
25 Apr 2023Assigned to Editor
25 Apr 2023Review(s) Completed, Editorial Evaluation Pending
16 May 2023Reviewer(s) Assigned