Vapour–liquid–liquid and vapour–liquid equilibrium of paraffinic aromatic synthetic naphtha/water blends: Prediction of the number of phases

Abstract

Bitumen is extracted from oil sands using warm water and additives. The resulting bitumen froth is diluted with naphtha in a froth treatment process. Residual naphtha in the aqueous tailings of the froth treatment unit is recovered in a naphtha recovery unit (NRU). It is imperative to maximize the naphtha recovery process to minimize the plant's environmental and economic impact. It is, in this respect, that NRU vapour–liquid–liquid equilibrium data is of significant value. In this work, a paraffinic‐aromatic synthetic naphtha (PASN) with a true boiling point (TBP) similar to that of froth treatment naphtha is used. Water/PASN mixtures are studied using the Soave‐Redlich‐Kwong equation of state with a Kabadi‐Danner modification. The tangent plane distance (TPD) is evaluated as a possible criterion to calculate the number of phases, with its significant shortcomings being established. As well, experimental data obtained in a CREC‐VL‐Cell is observed to display higher solubilities of PASN in water than the ones obtained by HYSYS‐Aspen Plus V9 simulation. To address these issues, a machine learning (ML)‐based phase classification methodology was considered, predicting the number of phases with a 99% recall. This anticipates that ML will be of significant value for faster convergence of the flash split calculations for the naphtha hydrocarbon‐water systems under consideration.

A Machine Learning Approach for Phase-Split Calculations in n-Octane/Water and PASN/Water Systems

“…While the number of phases at thermodynamic equilibrium is an unknown condition, as considered in our previous work [4][5][6], experimental data from the CREC-VL cell can be used to determine whether two-phase or three-phase regions are present. This allows for, in combination with ML, an effective "a priori" classification of the number of phases.…”

Issue Highlights

“…With these data, a machine learning (ML) approach is developed and validated, allowing the prediction of the number of phases with rapid convergence. [2] Liquid and solids phase backmixing in a bubble and slurry bubble column using a virtual tracer response methodology based on the trajectory data of the radioactive particle tracking (RPT) technique…”

“…To address this issue, the present study considers vapour‐liquid‐liquid equilibrium (VVLE) data acquired in a CREC‐VL‐Cell unit, while employing surrogate paraffinic‐aromatic synthetic naphtha (PASN)‐water blends. With these data, a machine learning (ML) approach is developed and validated, allowing the prediction of the number of phases with rapid convergence [2] …”