Data-driven framework based on machine learning and optimization algorithms to predict oxide-zeolite-based composite and reaction conditions for syngas-to-olefin conversion

Abstract

Bifunctional oxide-zeolite-based composites (OXZEO) have emerged as promising materials for the direct conversion of syngas to olefins. However, experimental screening and optimization of reaction parameters remain resource-intensive. To address this challenge, we implemented a three-stage framework integrating machine learning, Bayesian optimization, and experimental validation, utilizing a carefully curated dataset from the literature. Our ensemble-tree model (R-2 > 0.87) identified Zn-Zr and Cu-Mg binary mixed oxides as the most effective OXZEO systems, with their light olefin space-time yields confirmed by physically mixing with HSAPO-34 through experimental validation. Density functional theory calculations further elucidated the activity trends between Zn-Zr and Cu-Mg mixed oxides. Among 16 catalyst and reaction condition descriptors, the oxide/zeolite ratio, reaction temperature, and pressure emerged as the most significant factors. This interpretable, data-driven framework offers a versatile approach that can be applied to other catalytic processes, providing a powerful tool for experiment design and optimization in catalysis. (c) 2025, Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.