Machine learning has emerged as a flexible alternative for modeling adsorption equilibrium. However, its limitations in highly nonlinear systems remain unclear. In this work, a feedforward neural network was evaluated using experimental adsorption data for CO$_2$ and N$_2$ on zeolite 13X taken from the literature. While the model accurately reproduces N$_2$ adsorption behavior, it exhibits systematic deviations for CO$_2$, particularly at higher loadings. Training diagnostics reveal a persistent validation loss plateau, indicating a limitation in the model's ability to further reduce prediction error. These results suggest that the discrepancy is not due to data quality or overfitting, but r... More >