Prediction of Advanced Batch Distillation Curve for Bio-Oil and Biodiesel Mixtures Using Pθ Flash Algorithm and UNIFAC-DMD as a Predictive Thermodynamic Group Contribution Model

Distillation under vacuum is usually used to separate high boiling and sensitive components. Experimental and industrial equipment used to record distillation curves is expensive and time-consuming. Also, very accurate data may not be obtained. Hence, a computational solution is needed to predict the distillation curve. Thermodynamic models can be a suitable option. First, an algorithm integrated with the UNIFAC-DMD activity coefficient model was developed to obtain the batch distillation curve. The procedure was initialized using bubble-point and dew-point calculations to generate initial guesses, followed by successive Pθ flash calculations along the distillation path. The flash Pθ calculation solves the 2C + 1 equations simultaneously to determine the still pot temperature (T), mole fractions of components in the liquid (x), and the vapor phase (y). The validity of the provided method evaluated using the batch distillation curves of the n-decane + n-tetradecane, benzene + ethanol, benzene + isopropanol, acetone + chloroform; residue curve map (RCM) of 2-ethoxyethanol + ethylbenzene + DMSO; fast pyrolysis bio-oil (FPBO); tail-gas reactive pyrolysis (TGRP); a fatty acid + levulinate biodiesel fuel blend; and a surrogate mixture for Jet-A/JP-8 at different pressures. The results obtained from the presented method are consistent with experimental data, and data accuracy can be assessed. Finally, an FPBO sample and plastic waste pyrolysis oil were selected, their distillation curves were predicted, and the separation of the constituent components was discussed.