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Determining Surface Areas and Pore Volumes of Metal-Organic Frameworks

Determining Surface Areas and Pore Volumes of Metal-Organic Frameworks

Published on: March 8, 2024

Interpretable white-box modeling for nitrogen storage in metal-organic frameworks.

Arefeh Naghizadeh¹, Fahimeh Hadavimoghaddam^2,3, Meftah Ali Abuswer⁴

¹Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

Scientific Reports

|June 1, 2026

Summary

Developing accurate models for nitrogen storage in MOFs is crucial for industrial gas purification. Gene expression programming (GEP) offers a reliable method for predicting nitrogen storage capacity, outperforming other techniques.

Keywords:

GEP GMDH GP Metal–organic framework Nitrogen storage White-box

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Area of Science:

Materials Science
Chemical Engineering
Computational Chemistry

Background:

Efficient nitrogen (N₂) removal is vital for producing high-purity oxygen (O₂) and methane (CH₄) in industrial processes.
Accurate prediction of nitrogen storage capacity in metal-organic frameworks (MOFs) is challenging but essential for optimizing separation processes.

Purpose of the Study:

To develop user-friendly mathematical correlations for predicting the nitrogen storage capacity of MOFs.
To compare the performance of three advanced modeling techniques: Group Method of Data Handling (GMDH), Gene Expression Programming (GEP), and Genetic Programming (GP).

Main Methods:

Applied GMDH, GEP, and GP to a dataset of 3073 laboratory measurements for nitrogen storage in MOFs.
Evaluated model accuracy and reliability using statistical metrics (MAE, R²) and graphical methods.
Utilized correlation analyses (Pearson, Spearman, Kendall) to determine the influence of temperature and pressure on storage capacity.

Main Results:

The GEP model demonstrated superior performance with high R² values (0.9703-0.9750) and low MAE (0.9924-1.0101) across training, testing, and overall datasets.
All developed models accurately reflected the expected trend of N₂ storage under varying pressure conditions.
Temperature was identified as the most significant factor influencing storage capacity, followed by nonlinear pressure interactions.

Conclusions:

Gene Expression Programming (GEP) provides a highly reliable and accurate method for predicting nitrogen storage capacity in MOFs.
The developed correlations are applicable and credible, validated by the leverage method showing >95% data within acceptable ranges.
This work offers valuable predictive tools for optimizing MOF-based gas separation technologies.