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Preparation of Hydrophobic Metal-Organic Frameworks via Plasma Enhanced Chemical Vapor Deposition of Perfluoroalkanes for the Removal of Ammonia

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A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic

Mohsen Shayanmehr¹, Sepehr Aarabi¹, Ahad Ghaemi²

¹School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran.

Scientific Reports

|January 24, 2025

Summary

Machine learning models accurately predict MOF adsorbent capacity for removing thiophenic compounds. Initial sulfur concentration and contact time are key factors for efficient desulfurization.

Keywords:

Adsorptive desulfurization MOFs adsorbents Machine learning Thiophenic compounds removal

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Published on: March 8, 2024

Area of Science:

Materials Science
Chemical Engineering
Computational Chemistry

Background:

Thiophenic compounds like benzothiophene (BT), dibenzothiophene (DBT), and 4,6-dimethyl dibenzothiophene (4,6-DMDBT) are common sulfur pollutants in fuels.
Metal-Organic Frameworks (MOFs) show promise as adsorbents for removing these compounds due to their tunable structures and high surface areas.
Predictive modeling can optimize the selection and application of MOFs for efficient desulfurization.

Purpose of the Study:

To develop and evaluate machine learning (ML) models for forecasting the adsorption capacity of MOFs towards specific thiophenic compounds.
To identify the most influential features affecting the desulfurization efficiency of MOF adsorbents.
To optimize the desulfurization process for maximum adsorption capacity using ML predictions.

Main Methods:

Utilized Python programming and five ML models (including MLP and Random Forest) trained on a dataset of 676 entries.
Correlated adsorbent features, adsorption conditions, and adsorbate characteristics with sulfur adsorption capability.
Employed the Shapley Additive plan (SHAP) method for feature importance analysis.

Main Results:

The MLP model demonstrated superior performance with low Mean Squared Error (MSE) of 0.0032 (test) and 0.0021 (training).
Initial sulfur concentration (SHAP value 0.51) and contact time (SHAP value 0.37) were identified as critical factors.
Process conditions emerged as the most significant feature category influencing desulfurization efficiency.

Conclusions:

ML techniques, particularly MLP, are effective tools for predicting MOF adsorption performance in desulfurization.
Optimized process conditions, including initial concentration and contact time, are crucial for maximizing sulfur removal.
The study provides a framework for optimizing MOF-based desulfurization processes through data-driven insights.