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可解释的机器学习预测用于CO2辅助氧化脱的催化剂

Hongyu Liu^1,2, Kangyu Liu², Hairuo Zhu¹

¹State Key Laboratory of Heavy Oil Processing, China University of Petroleum Beijing 102249 PR China liyuming@cup.edu.cn.

|March 4, 2024

概括

机器学习模型,包括随机森林,通过二氧化碳辅助的氧化脱有效预测烯的生产. 这种方法有助于通过分析文献数据来设计高效的催化剂.

科学领域:

催化剂是一种催化剂.
化学工程是化学工程的重要组成部分.
材料科学材料科学材料科学

背景情况:

烯是重要的化学原料,需要可持续的生产方法.
以CO2为辅助的氧化脱 (CO2-ODHP) 为提供了一条绿色通路,利用废弃的CO2.
有效的催化剂设计对于推进CO2-ODHP技术至关重要.

研究的目的:

应用机器学习算法来预测烯在CO2-ODHP中的时空产量.
确定最有效的机器学习模型来预测催化性能.
解释模型预测并了解反应参数对催化物的影响.

主要方法:

编制和分析了关于CO2-ODHP的文献数据.
使用了机器学习算法,包括人工神经网络 (ANN),k-最近邻居 (KNN),支持向量回归 (SVR) 和随机森林回归 (RF).
哈普利添加式扩展 (SHAP) 用于模型解释.

主要成果:

随机森林 (RF) 成为预测转换和选择性的优越算法.
机器学习模型成功地预测了烯的时空产量.
SHAP分析揭示了反应条件和化学成分对催化性能的影响.

结论:

机器学习,特别是射频,为指导CO2-ODHP中的催化剂开发提供了一个强大的工具.
从文献中获得数据驱动的见解可以加速发现用于轻基转换的高性能催化剂.
这项研究为在异质催化研究中利用机器学习提供了有价值的视角.

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