Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA.
State Key Laboratory of Fine Chemicals, PSU-DUT Joint Center for Energy Research, School of Chemical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning Province, China.
Nat Commun. 2023 May 31;14(1):3152. doi: 10.1038/s41467-023-38738-5.
It is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms' results offers the insight that reduced NaO content is key to formulating FAU materials with high Si/Al ratio. An acid catalyst prepared by partial ion exchange of the high-Si/Al-ratio FAU (Si/Al = 3.5) exhibits improved proton reactivity (as well as specific activity, per unit mass of catalyst) in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al = 2.8).
研究表明，机器学习（ML）算法可以有效地捕捉结晶成分和条件（输入）对丝光沸石型沸石（FAU、EMT 结构类型及其混合体，广泛用作沸石催化剂和吸附剂）关键微观结构特征（输出）的影响。展示了 ML（特别是几何谐波）在学习感兴趣的输入-输出关系方面的实用性，并与神经网络和高斯过程回归进行了比较，作为替代方法。通过 ML，确定了合成条件，以将高纯度 FAU 沸石的 Si/Al 比提高到迄今为止通过直接（非种子）合成以及从硅酸钠铝溶胶中无有机结构导向剂合成所达到的最高水平（即 Si/Al=3.5）。对 ML 算法结果的分析提供了这样的见解，即降低 NaO 含量是形成高 Si/Al 比 FAU 材料的关键。与从之前报道的 Si/Al 比最高（Si/Al=2.8）的 FAU 制备的催化剂相比，部分离子交换制备的高-Si/Al 比 FAU（Si/Al=3.5）制备的酸催化剂在丙烷裂化和脱氢反应中表现出更高的质子反应性（以及单位催化剂质量的比活性）。
