LAPSE:2023.36568
Published Article
LAPSE:2023.36568
Artificial Neural Networks (ANNs) for Vapour-Liquid-Liquid Equilibrium (VLLE) Predictions in N-Octane/Water Blends
August 3, 2023
Blends of bitumen, clay, and quartz in water are obtained from the surface mining of the Athabasca Oil Sands. To facilitate its transportation through pipelines, this mixture is usually diluted with locally produced naphtha. As a result of this, naphtha has to be recovered later, in a naphtha recovery unit (NRU). The NRU process is a complex one and requires the knowledge of Vapour-Liquid-Liquid Equilibrium (VLLE) thermodynamics. The present study uses experimental data, obtained in a CREC-VL-Cell, and Artificial Intelligence (AI) for vapour-liquid-liquid equilibrium (VLLE) calculations. The proposed Artificial Neural Networks (ANNs) do not require prior knowledge of the number of vapour-liquid phases. These ANNs involve hyperparameters that are used to obtain the best ANN model architecture. To accomplish this, this study considers (a) R2 Coefficients of Determination and (b) ANN training requirements to avoid data underfitting and overfitting. Results demonstrate that temperature has a major influence on ANN vapour pressure predictions, while the concentration of octane, the naphtha surrogate having, in contrast, a lesser effect. Furthermore, the ANN data obtained allows the calculation of octane-in-water and water-in-octane maximum solubilities.
Record ID
Keywords
Artificial Neural Networks, hydrocarbon/water blends, Machine Learning, vapour-liquid-liquid equilibrium
Suggested Citation
Lopez-Ramirez E, Lopez-Zamora S, Escobedo S, de Lasa H. Artificial Neural Networks (ANNs) for Vapour-Liquid-Liquid Equilibrium (VLLE) Predictions in N-Octane/Water Blends. (2023). LAPSE:2023.36568
Author Affiliations
Lopez-Ramirez E: Department of Chemical and Biochemical Engineering, Chemical Reactor Engineering Centre, The University of Western Ontario, London, ON N6A 3K7, Canada; Faculty of Engineering and Architecture, Department of Civil Engineering, Universidad Nacional de Colom
Lopez-Zamora S: Department of Chemical and Biochemical Engineering, Chemical Reactor Engineering Centre, The University of Western Ontario, London, ON N6A 3K7, Canada [ORCID]
Escobedo S: Department of Chemical and Biochemical Engineering, Chemical Reactor Engineering Centre, The University of Western Ontario, London, ON N6A 3K7, Canada [ORCID]
de Lasa H: Department of Chemical and Biochemical Engineering, Chemical Reactor Engineering Centre, The University of Western Ontario, London, ON N6A 3K7, Canada
Lopez-Zamora S: Department of Chemical and Biochemical Engineering, Chemical Reactor Engineering Centre, The University of Western Ontario, London, ON N6A 3K7, Canada [ORCID]
Escobedo S: Department of Chemical and Biochemical Engineering, Chemical Reactor Engineering Centre, The University of Western Ontario, London, ON N6A 3K7, Canada [ORCID]
de Lasa H: Department of Chemical and Biochemical Engineering, Chemical Reactor Engineering Centre, The University of Western Ontario, London, ON N6A 3K7, Canada
Journal Name
Processes
Volume
11
Issue
7
First Page
2026
Year
2023
Publication Date
2023-07-06
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr11072026, Publication Type: Journal Article
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Published Article
LAPSE:2023.36568
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External Link
doi:10.3390/pr11072026
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Version History
[v1] (Original Submission)
Aug 3, 2023
Verified by curator on
Aug 3, 2023
This Version Number
v1
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https://psecommunity.org/LAPSE:2023.36568
Original Submitter
Calvin Tsay
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