연구
Research Outcome
미래를 창조하는 포스텍 화학공학과
Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization
- Title of paper
- Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization
- Author
- [윤용주 교수 연구실] 머신러닝 및 메타 휴리스틱 최적화 알고리즘을 통한 데이터 기반 프로판 탈수소화 촉매 개발
- Publication in journal
- ACS Materials Letters
- Publication date
- 20241016
[Abstract]
Recent advances in data-driven approaches using the machine learning (ML) method have enabled the discovery of high-performance materials. This paper presents a hybrid framework that combines ML models with a metaheuristic optimization algorithm, to explore improved heterogeneous catalysts for propane dehydrogenation (PDH). The framework proposes multiple PDH catalysts, utilizing our laboratory-scale database. A unique five-component catalyst, 2.4Ga 2.2Pt 1.7B 1.3Zr/Al2O3, exhibits superior performance, achieving a propylene yield of 58% at 600 °C. This work highlights the excellent predictive capability of the framework and offers a new data-driven approach for developing high-performance materials for heterogeneous catalysis.
DOI: 10.1021/acsmaterialslett.4c01367
Link: https://pubs.acs.org/doi/10.1021/acsmaterialslett.4c01367
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