The increasing demand for sustainable energy has accelerated the development of green hydrogen production technologies. Among these, the catalytic decomposition of ammonia stands out because of its efficient storage and transportation as well as its compatibility with existing infrastructure. Nevertheless, challenges in enhancing the reaction performance still hinder its large-scale implementation. To address these limitations and optimize the process, this work presents the development of a deep-learning-based artificial neural network to model ammonia conversion as a function of operating conditions and catalyst composition encoded directly into the network. The final model designed significantly outperformed traditional machine learning techniques and the smaller architectures tested. Deep learning was fundamental for achieving the lowest predictive errors (RMSE = 10.06 ppm and MAE = 7.98 ppm) and minimizing the difference between training and validation errors, indicating a high degree of stability and generalization. A comprehensive sensitivity analysis was also conducted and aligned with literature findings, revealing the model’s capacity to capture complex physicochemical patterns. Finally, validation on external data further confirmed its generalization capabilities. To the best of our knowledge, this is the first study to implement deep neural networks for modeling the catalytic decomposition of ammonia, including catalyst compositional features, while also contributing to the broader, still-emerging application of deep learning in catalytic systems.
Dalcamim et al. (Fri,) studied this question.