Vapor-Liquid Phase Equilibria for Carbon Dioxide+Isopentanol Binary System at Elevated Pressure

2011 ◽  
Vol 29 (8) ◽  
pp. 1589-1594
Author(s):  
Lin Wang ◽  
Fengpu Cao ◽  
Shanshan Liu ◽  
Hao Yang
Keyword(s):  
Carbon Dioxide ◽  
Liquid Phase ◽  
Binary System ◽  
Phase Equilibria ◽  
Elevated Pressure ◽  
Vapor Liquid

scholarly journals Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 413
Author(s):  
Sandra Lopez-Zamora ◽  
Jeonghoon Kong ◽  
Salvador Escobedo ◽  
Hugo de Lasa
Keyword(s):  
Machine Learning ◽  
Liquid Phase ◽  
Phase Equilibria ◽  
Aspen Plus ◽  
Phase Region ◽  
Two Phase ◽  
Technical Literature ◽  
Phase Liquid ◽  
Liquid Vapor ◽  
Vapor Liquid

The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis using the Gibbs Free Energy of mixing and the NRTL model, provide a good understanding with calculation issues, when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. To clarify these matters, n-octane and water blends, are good surrogates of naphtha/water mixtures. Runs were developed in a CREC vapor–liquid (VL_ Cell operated with octane–water mixtures under dynamic conditions and used to establish the two-phase (liquid–vapor) and three phase (liquid–liquid–vapor) domains. Results obtained demonstrate that the two phase region (full solubility in the liquid phase) of n-octane in water at 100 °C is in the 10-4 mol fraction range, and it is larger than the 10-5 mol fraction predicted by Aspen Plus and the 10-7 mol fraction reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a machine learning (ML) technique was implemented and successfully demonstrated, in the present study.

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