Abstract
<jats:p>The prediction of chemical equilibrium requires both a physically sound thermodynamic model and reliable thermodynamic formation properties of the reacting species. In biomass valorization, however, many esterification reactions involve compounds for which formation properties are either unavailable or affected by significant experimental uncertainty. As a result, equilibrium constants are commonly obtained by correlating experimental equilibrium data, making the predicted chemical equilibrium dependent on the selected thermodynamic model and reducing its predictive capability. In this work, a predictive methodology based on homologous-series trends is proposed to estimate reaction formation properties and equilibrium constants without using chemical equilibrium data for parameter adjustment.The approach exploits the asymptotic behavior of thermodynamic formation properties within homologous families to identify inconsistent tabulated values and to estimate equilibrium constants for reactions involving compounds lacking thermodynamic data. The methodology is first validated for the acetylation of linear alcohols, where reliable formation properties are available. The proposed approach successfully identifies inconsistencies in published thermodynamic data and improves the prediction of chemical equilibrium and simultaneous chemical-phase equilibrium, particularly for systems affected by erroneous formation properties. It is subsequently applied to the esterification of long-chain fatty acids with ethanol, 1-butanol,and 2-ethyl-1-hexanol,forwhich formation properties are unavailableand equilibrium constants have traditionally been obtained by correlation. Predictions obtained with different thermodynamic models show good qualitative and, in many cases, semi-quantitative agreement with experimental data without any adjustment to chemical equilibrium measurements. The proposed methodology provides a practical workflow for estimating equilibrium constants whilepreservingthepredictivecharacterofthermodynamicmodels.Thisenablesfeasibility studies of new esterification processes prior to experimental characterization and offers a general framework for extending thermodynamic predictions to homologous reaction families lacking reliable formation-property data.</jats:p>