To compare the thermodynamic approach to the purely data driven neural networks, both were applied to predict FP values of 1-butanol + FAEEs, as shown in Figure . The software FLAMMA was used in the thermodynamic approach to calculate the FP of miscible mixtures. It uses Liaw's mathematical approach, along with activity coefficient models such as UNIFAC, UNIQUAC and NRTL. The plots showed that the neural network model's performance slightly decreases as the molar fraction of butanol increases, ranging in terms of average absolute deviations from 2.09 K to 3.64 K. This is due to the limited number of pure alcohols in the database, including only ethanol and butanol, leading to a higher deviation in predicting the FP of pure butanol compared to mixtures. However, the overall performance was satisfactory and close to the values predicted by the UNIFAC model, as RMSEs and MAEs from Tables and show, respectively. This highlights the proposed neural network approach's potential, as it can provide decent predictions without any non-ideality modeling, in contrast to the traditional approach, which requires more thermodynamics knowledge.