模拟在工业条件下通过实验和人工智能选择性化缩乙流中的时间在流催化剂反应性
Jonathan M Mauß1, Klara S Kley1, Rohini Khobragade1
1Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, Mülheim an der Ruhr 45470, Germany.
人工智能模拟了基于的双金属催化剂的反应性,用于选择性乙化. 这种方法可以为具有挑战性的工业应用设计新型催化剂.
科学领域:
- 催化剂是一种催化剂.
- 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:
- 由于动态的催化剂结构和不断变化的化学环境,异质催化是复杂的.
- 了解催化剂的性能随着时间的推移 (时间流) 是至关重要的,但在恶劣的反应条件下具有挑战性.
研究的目的:
- 开发一种人工智能 (AI) 方法来建模基于的双金属催化剂的时流反应性.
- 在工业相关条件 (10 bar) 下,研究缩乙流 (>14.0体积%) 的选择性化.
主要方法:
- 系统的实验研究与专注的AI方法相结合.
- 发现子组,以确定有效的材料和反应条件.
- 确定独立选和分散运算符 (SISSO) 符号回归用于时间依赖的选择性建模.
主要成果:
- 人工智能成功模拟了基于的双金属催化剂的时流反应性.
- 确定了与催化剂活性相关的关键物理化学参数.
- 人工智能方法突出了催化剂结构和反应混合物的化学潜力之间的相互作用.
结论:
- 人工智能模型能够合理设计新的双金属和三金属催化剂.
- 实验验证证证实了人工智能驱动的催化剂设计.
- 这项工作为在具有挑战性的工业化过程中优化催化剂提供了一条途径.
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