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

Abstract

Flash calculations, including phase split and phase classification for both n-octane/water blends and paraffinic aromatic synthetic naphtha (PASN)/water blends present significant computational challenges. Calculations to establish the two-phase and three-phase regions, as well as the transitions between regions, were addressed by a phase classification method proposed in a recent contribution involving machine learning (ML). This work focusses on the phase-split calculations, considering (a) the lack of numerical convergence of the traditional calculations and their related numerical issues for water/n-octane and PASN/water systems based on the Rachford–Rice derived surfaces and (b) the successful implementation of an ML approach based on a K-nearest-neighbor (KNN) algorithm, which uses the abundant experimental data obtained in a CREC-VL cell.