汽油脱硫催化剂的进步:结构-活动关系和机器学习方法的作用
Honglei Sun1, Chao Chen1, Ran Zhang1
1Petrochina Petrochemical Research Institute, Beijing 100083, China.
ACS omega
|August 4, 2025
概括
机器学习通过优化结构-活动关系来加速汽油水解硫 (HDS) 催化剂的设计. 这种方法可以快速发现用于深层去除硫的催化剂,同时保持燃料质量.
科学领域:
- 催化科学与技术 催化科学与技术
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 全球环境法规要求减少运输燃料中的硫含量.
- 汽油的水解硫 (HDS) 对于深度去除硫至关重要,但在保持燃料的八度质量方面面临挑战.
- 优化催化剂结构-活性关系 (SAR) 是高效HDS的关键.
研究的目的:
- 审查汽油HDS催化剂的最新进展,重点关注SAR和机器学习 (ML) 的整合.
- 探索ML方法如何加速催化剂设计和优化HDS过程.
- 确定 ML 驱动的 HDS 催化剂开发的挑战和未来方向.
主要方法:
- 审查基本的HDS原则和挑战.
- 分析ML应用在优化MoS2形态 (边缘/角的比) 中,以平衡直接脱硫 (DDS) 和化 (HYD).
- 探索ML算法 (随机森林,SVM,深度神经网络) 以捕获复杂的SAR并指导实验设计.
主要成果:
- ML有效地解读合成-结构-活性关系,优先考虑Co/Mo比率和硫化条件等参数.
- ML模型能够快速发现具有高硫去除效率和最小烯酸损失的HDS催化剂.
- 案例研究表明,成功地将实验数据与机器学习集成在一起,以提高预测能力和流程优化.
结论:
- ML正在通过解决反应参数之间的复杂非线性关系来彻底改变HDS催化剂设计.
- 成功的ML集成加速了清洁交通燃料的优质催化剂的识别.
- 未来的研究应该专注于克服数据限制和原料复杂性,以在HDS中实现工业规模的ML应用.
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