Decision tree analysis on the performance of zeolite-based SCR catalysts
2021 · IFAC-PapersOnLine
저자
Shin Young Bae, Jun‐Seop Shin, Yeon Soo Kim, 이종민 (Jong Min Lee)
연구주제
Catalytic Processes in Materials Science · Machine Learning in Materials Science · Industrial Gas Emission Control
NTIS 과학기술표준분류
가장 가까운 분류(환경 > 대기질 관리 > 대기오염 방지기술)가 화학·화공 범위 밖이다. 배정 근거: AI 키워드가 잡은 NTIS 과제의 분류.
AI 연구 키워드·방법
catalyst performance analysis 선택적 촉매 환원(SCR) 제올라이트 촉매
decision tree analysis
키워드 (OpenAlex)
zeolite catalysts selective catalytic reduction urea-SCR NOx reduction beta zeolite ZSM-5 zeolite catalyst performance prediction decision tree classification
초록
Urea-based selective catalytic reduction (SCR) is a promising method for removing NOx emissions. In the urea-based SCR method, zeolite-based catalysts are popularly used owing to their applicability over a wide range of temperatures compared to other catalysts. However, they still have several drawbacks such as inferior performance at low temperatures and thermal instability. This has subsequently led to numerous studies and experiments on the development of superior catalysts. While substantial experimental data exist on zeolite-based catalysts, extracting useful information from the data with a simple literature search is difficult owing to not only the amount of data but also complex correlations between feature, including preparation variables such as doped-metal loading and operational variables such as reaction temperature. Recently, extracting insights from a large database has be…
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교수 1
연구주제 1
Catalytic Processes in Materials Science