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使用机器学习和微动力学建模,对用于蒸汽甲改造的耐硫催化剂进行高通量选

Siqi Wang¹, Satya Saravan Kumar Kasarapu¹, Peter T Clough¹

¹Energy and Sustainability Theme, Cranfield University, Cranfield, Bedfordshire MK43 0AL, U.K.

|March 18, 2024

概括

机器学习和微动力学建模快速选了500多个双金属催化剂,以检测蒸汽甲改革 (SMR) 中的硫阻力. 基于Ge和Ni3Cu1的合金显示出对具有成本效益,耐硫的SMR应用的希望.

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
计算化学的计算化学

背景情况:

双金属催化剂对于蒸汽甲改造 (SMR) 是至关重要的.
它们在富含硫的环境中的表现在很大程度上仍未被探索.
硫中毒是SMR催化剂的一个主要挑战.

研究的目的:

使用机器学习 (ML) 和微动力学建模,开发一种对抗硫双金属催化剂的快速选过程.
确定新型双金属材料,在SMR中提高抗硫中毒的稳定性.
利用计算方法加速催化剂的发现.

主要方法:

开发ML模型 (Ensemble,ANN,SVR) 以预测双金属表面上的原子吸附能.
利用金属和吸附物的物理和化学特性作为ML输入特征.
构建SMR反应的微动力学模型,并选500多种双金属材料.

主要成果:

ML模型实现了对吸附能量的高预测性能 (R^2高达0.74).
集体学习模型与微动力学建模一起用于催化剂选.
四种基于Ge的合金 (Ge3Cu1,Ge3Ni1,Ge3Co1,Ge3Fe1) 和Ni3Cu1被确定为有前途的候选物.

结论:

结合ML和微动力学建模方法,可以有效地发现耐硫催化剂.
已识别的基于Ge和Ni3Cu1的合金为具有成本效益和耐硫的SMR提供了潜力.
这种方法加快了为具有挑战性的工业过程开发下一代催化剂的速度.

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Heterogeneous Catalysis

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