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

Abstract

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.