Artificial Neural Networks (ANNs) for Vapour-Liquid-Liquid Equilibrium (VLLE) Predictions in N-Octane/Water Blends

نویسندگان

چکیده

Blends of bitumen, clay, and quartz in water are obtained from the surface mining Athabasca Oil Sands. To facilitate its transportation through pipelines, this mixture is usually diluted with locally produced naphtha. As a result this, naphtha has to be recovered later, recovery unit (NRU). The NRU process complex one requires knowledge Vapour-Liquid-Liquid Equilibrium (VLLE) thermodynamics. present study uses experimental data, CREC-VL-Cell, Artificial Intelligence (AI) for vapour-liquid-liquid equilibrium calculations. proposed Neural Networks (ANNs) do not require prior number vapour-liquid phases. These ANNs involve hyperparameters that used obtain best ANN model architecture. accomplish considers (a) R2 Coefficients Determination (b) training requirements avoid data underfitting overfitting. Results demonstrate temperature major influence on vapour pressure predictions, while concentration octane, surrogate having, contrast, lesser effect. Furthermore, allows calculation octane-in-water water-in-octane maximum solubilities.

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ژورنال

عنوان ژورنال: Processes

سال: 2023

ISSN: ['2227-9717']

DOI: https://doi.org/10.3390/pr11072026