Abstract
Pyrolysis is an emerging green pathway to produce bulk chemicals and sustainable fuels. However, pyrolysis oil requires stabilisation via hydrotreatment, and this process generates an aqueous waste containing alcohols (mainly methanol and ethanol), carboxylic acids, and some ketones. To increase the economic sustainability of biofuels production, there is increasing interest in recovering these valuable chemicals from water. Reliable thermodynamics are necessary to address the separation and design of equipment to fractionate such complex mixtures, with multiple azeotropes and non-idealities. The Non-Random Two-Liquids (NRTL) models in both Aspen Plus V12.0 and COFE V3.7, a license-free software released by AmsterChem, do not accurately reproduce the equilibrium measurements of most of the binary and ternary mixtures involving water, a C1-C4 alcohol, and a light carboxylic acid. This work aims to retune the activity-based model to improve the NRTL model predictivity, using experimental data retrieved from NIST ThermoData Engine V10.1. The regression is performed in Aspen Plus V12 using the Maximum Likelihood algorithm. The resulting re-parametrised NRTL shows improved accuracy and reliability compared to the existing models. For all investigated binary and ternary mixtures, the Average Relative Deviation (ARD) is lower than 20%, and for 75% of them, ARD is lower than 10%. The ARD in the prediction of vapour phase composition drops from 16.0% and 12.1% of the original COFE and Aspen Plus models, respectively, down to 7.0%.