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 >

Chemical engineering, like other engineering disciplines, has evolved from manual calculations to advanced process simulators and artificial intelligence tools. This perspective piece reflects on how such technological shifts have transformed both professional practice and engineering education. Drawing from historical perspectives and current teaching experiences, it examines the balance between leveraging modern tools and fostering deep conceptual understanding. The discussion invites educators to critically assess how and when to integrate digital resources into the chemical engineering curriculum. More >