Machine learning for predicting catalytic ammonia decomposition: An approach for catalyst design and performance prediction

文献类型： 外文期刊

第一作者： Guo, Wenjuan

作者： Guo, Wenjuan;Shafizadeh, Alireza;Rafiee, Shahin;Motamedi, Shahrzad;Nia, Seyyed Alireza Ghafarian;Aghbashlo, Mortaza;Shahbeik, Hossein;Tabatabaei, Meisam;Nadian, Mohammad Hossein;Li, Fanghua;Pan, Junting

作者机构：

关键词： Ammonia decomposition; Hydrogen formation; Machine learning; Gradient boost regression; Catalyst properties; Reaction conditions

期刊名称：JOURNAL OF ENERGY STORAGE （ 影响因子：9.4； 五年影响因子：9.1 ）

ISSN： 2352-152X

年卷期： 2024 年 89 卷

页码：

收录情况： SCI

摘要： Ammonia, a cost-effective hydrogen carrier, holds the potential for hydrogen production through decomposition, where catalysts play a pivotal role in lowering the decomposition temperature. However, identifying suitable catalysts involves expensive and time-consuming experiments. Machine learning (ML) emerges as a powerful solution to address challenges in catalytic ammonia decomposition. This study focuses on creating an ML model to predict ammonia decomposition. A comprehensive database is compiled and statistically analyzed to discern correlations between descriptors and responses. Employing random forest regression, support vector machine, and gradient boost regression models, the study models the ammonia decomposition process as a function of catalyst properties and reaction conditions. Feature importance analysis evaluates the influence of descriptors on responses. The results unveil a robust positive correlation between ammonia decomposition and reaction temperature. Improved ammonia decomposition and hydrogen formation rates are achievable with a total metal loading below 20 %. The gradient boost regression tree model exhibits satisfactory performance during testing (R-2 > 0.85, RMSE <13.24, and MAE < 10.31). Notably, reaction temperature and gas hourly space velocity emerge as the two most influential descriptors impacting ammonia conversion and hydrogen formation rate. This research underscores the efficacy of ML in addressing challenges in catalytic ammonia decomposition, providing valuable insights for the advancement of hydrogen production.

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