Energy-Efficient Prediction of Carbon Deposition in DRM Processes Through Optimized Neural Network Modeling

Energy-Efficient Prediction of Carbon Deposition in DRM Processes Through Optimized Neural Network Modeling

Methane dry reforming (DRM) offers a promising route by converting two greenhouse gases into syngas, but catalyst deactivation through carbon deposition severely reduces energy efficiency. While neural networks offer potential for predicting carbon deposition and reducing experimental burdens, conve...

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Bibliographic Details
Main Authors: Rui Fang, Tuo Zhou, Zhuangzhuang Xu, Xiannan Hu, Man Zhang, Hairui Yang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Energies
Subjects:
methane dry reforming
carbon deposition
energy efficiency
machine learning prediction
neural networks
process optimization
Online Access:https://www.mdpi.com/1996-1073/18/12/3172
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