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Abstract

Summary

The rising concentration of atmospheric CO has intensified global warming and climate change, highlighting the need to develop effective mitigation strategies. Among various approaches, adsorption using metal-organic frameworks (MOFs) has gained attention as a promising method for CO capture. This study investigates the predictive capabilities of machine learning (ML) models, specifically the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Coupled Simulated Annealing–Least Squares Support Vector Machine (CSA-LSSVM), in estimating CO adsorption by MOFs. The ANFIS model is analyzed based on membership function type, epoch number, and optimization method, while CSA-LSSVM is examined using various kernel functions to enhance predictive accuracy and minimize error. The results demonstrate that CSA-LSSVM with the Gaussian kernel function achieves the highest accuracy, outperforming other models with a training RMSE of 0.026 and R2 of 0.974.

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/content/papers/10.3997/2214-4609.202522089
2025-09-01
2026-02-14

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MOF-Based CO2 Adsorption Predictions: Role of Membership and Kernel Functions in Machine Learning Models
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.202522089
/content/papers/10.3997/2214-4609.202522089
/content/papers/10.3997/2214-4609.202522089
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