在工业催化剂上对CO2甲化反应进行基于机器学习的动态建模
Hugo Pétremand1,2, Julia Witte3, Oliver Kröcher1,2
1PSI Center for Energy and Environmental Sciences, Paul Scherrer Institute, 5232 Villigen, Switzerland.
概括
机器学习 (ML) 模型可以在没有详细机制的情况下准确地描述二氧化碳甲化动力学. 这些基于ML的动力学在机械信息有限的情况下显示出对反应堆设计的希望.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 催化剂是一种催化剂.
- 机器学习 机器学习
背景情况:
- 工业催化剂通常具有机密成分,阻碍机械学理解和动力学模型开发.
- 准确的动力模型对于有效的反应堆设计至关重要,但对于商业催化剂来说很难获得.
- 机器学习 (ML) 为动态建模提供了一个潜在的替代方案,而不需要详细的反应机制.
研究的目的:
- 用商业催化剂研究基于ML的回归在描述二氧化碳甲化运动学的有效性.
- 将ML模型的性能与传统的动力建模方法 (功率定律和LHHW) 的性能进行比较.
- 评估ML衍生动力学在反应堆建模和设计中的适用性.
主要方法:
- 在固定床反应堆中,对基于Ni/ZrO2的商用二氧化碳甲化催化剂进行了动态实验.
- 在各种温度,部分压力和空间速度上生成了一个全面的数据集.
- 用高斯过程回归和1层神经网络作为ML模型,并使用RMSE与功率法和LHHW模型进行比较.
主要成果:
- 基于ML的模型,特别是高斯过程回归和1层神经网络,与功率定律模型相比,表现出更高的性能.
- 在描述催化剂的动态状态方面,ML模型的准确性与兰迈尔-欣舍尔伍德-霍根-沃森 (LHHW) 模型相提并论.
- 当集成到反应堆模型中时,ML衍生动力学准确地预测了甲产量,在非异热条件下甚至超过了LHHW.
结论:
- 基于ML的动力模型是二氧化碳甲化的一个可行和有前途的方法,特别是当没有详细的机械洞察力时.
- 这些ML模型可以有效地用于稳固的反应器设计,提供与传统方法相匹配或优越的性能.
- 该研究验证了ML用于从实验数据开发预测运动模型的使用,从而加速过程开发.
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