Decoding the CO2 adsorption of nitrogen-doped carbon under variable temperature and pressure conditions: A machine learning guideline

Minghong Wang, Shuai Gao, Liang Wang, Xiong Yang and Wei Chen

Energy, 2025, vol. 328, issue C

Abstract: Efficient capture and utilization of CO2 is the key to achieving carbon peak and neutrality. This study used the radial basis function neural network and extreme gradient boosting regression to build the regression models about CO2 adsorption uptake based on variable temperature and pressure swing adsorption dataset. Besides, the structure of the models was optimized by the particle swarm optimization algorithm. Results revealed that the extreme gradient boosting regression models showed better performance (Test R2 = 0.80–0.97). The relative importance diagram canulated by Shapley additive explanations showed that the pore structure was highly correlated with CO2 uptake. Furthermore, the partial dependence plot found that the carbon material adsorbent, whose micropore volume was greater than 0.6 cm3/g and N-5 content was between 3.5 and 4.5 wt%, had better adsorption performance, and the CO2 uptake of more than 3 mmol/g could be achieved. What's more, these models were integrated into an interactive web page by the Gradio library. This study offered a new idea about the preparation of high-performance nitrogen-doped carbon adsorbents under various adsorption conditions.

Keywords: CO2 adsorption; Machine learning; Nitrogen-doped carbon; Carbon capture; Shapley additive explanations (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225020390
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:328:y:2025:i:c:s0360544225020390

DOI: 10.1016/j.energy.2025.136397

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().