Cracking of Heavy-Inferior Oils with Different Alkane-Aromatic Ratios to Aromatics over MFI Zeolites:structure-Activity Relationship Derived by Machine Learning
Energy(2024)
摘要
This paper investigated the performance of catalysts with different morphology in cracking of heavy-inferior oil (HIO) to aromatics with different alkane-aromatic ratios (AAR), which include high and low-temperature coal tar (HMCT, SMCT), liquid products of coal-oil co-refining (LCOCR and HCOCR) and petroleum (YLP). The experimental results indicated that Na+ and OH- have a competitive effect on the catalyst morphology, and that low alkalinity in the synthesis system favors the synthesis of 2D zeolites. The highest selectivity of aromatics in the products of HMCT, SMCT, HCOCR and YLP after catalysis by fast pyrolysis-gas chromatography/mass spectrometry can reach 92.8 %, 44.5 %, 51.7 % and 42.0 %, which are 8.9 %, 36.3 %, 38.2 % and 39.3 % higher than those in non-catalytic pyrolysis under the same conditions, respectively. The catalyst with a high amount of strong acid facilitates the conversion of HIO, and it is noteworthy that the presence of aromatics in HIO will contribute to the aromatization in the reaction, which is of great significance in promoting the deep processing of HIO. The structure-activity relationship between catalysts and products was investigated by machine learning, and the importance of features on the selectivity of BTEX decreases in the order of AS(S) > AS(T) > HF '' > S-micro > D-pore size.
更多查看译文
关键词
Heavy-inferior oil,Alkane-aromatic ratio,MFI zeolites,Modulation,Machine learning
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要