(582a) Crystal Engineering of a Zeolite Using Machine Learning | AIChE

(582a) Crystal Engineering of a Zeolite Using Machine Learning


Li, X. - Presenter, University of Minnesota
Evangelou, N. - Presenter, Johns Hopkins University
Wichrowski, N. J. - Presenter, Johns Hopkins University
Han, H., Dalian University of Technology
Lu, P., Johns Hopkins University
Xu, W., Argonne National Laboratory
Hwang, S. J., Caltech
Zhao, W., University of Minnesota
Song, C., The Chinese University of Hong Kong
Guo, X., Dalian University of Technology
Bhan, A., University of Minnesota
Kevrekidis, I. G., Princeton University
Tsapatsis, M., Johns Hopkins University
It is shown that machine learning (ML) algorithms can capture the effect of crystallization inputs on key microstructural characteristics (outputs) of faujasite (FAU), a widely used zeolite catalyst and adsorbent. This work focuses primarily on using the technique known as geometric harmonics to learn input-output relationships of interest, but we also provide a brief comparison with neural networks and Gaussian process regression, as alternative approaches. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of (FAU) zeolite prepared via direct template-free synthesis to the hitherto highest level (i.e., Si/Al = 3.5). Our analysis of the ML algorithms’ results offers the insight that reduced Na2O content is a key parameter to achieve the enhanced Si/Al ratio. An acid catalyst prepared by partial ion exchange of the new high-Si/Al-ratio FAU (Si/Al=3.5) exhibits improved proton reactivity in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al=2.8).