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16 August 2023 Prediction of CF4/N2 adsorption and separation performance in organic frameworks based on machine learning algorithms
Hao Wang, Xuan Peng
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Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127871I (2023) https://doi.org/10.1117/12.3004624
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China
Abstract
The separation of different components in a binary mixture of CF4 and N2 is a challenging task. The adsorption of 603 experimental (CoRE) organic frameworks on CF4 and N2 binary mixture gases at room temperature and 10 different pressures was simulated using GCMC in this study. The performance of adsorption separation is described by the product of the adsorption amount of CF4 and the logarithm of the CF4 adsorption selectivity. Among the nine features extracted are pressure, structural features of adsorbent, and two easily accessible user-defined descriptors (AVG_SIG and AVG_SQRT_EPS). The importance of AVG_SIG and AVG_SQRT_EPS was assessed using random forest and found to play an important role in predicting adsorption separation performance. By using Harris Hawks Optimization (HHO) to explore the potential of different models, it was found that XGBoost and GangNeuron performed excellently in predicting the adsorption and separation performance of CF4 and N2, with prediction accuracy of 99.2% and 98.8%, respectively.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Hao Wang and Xuan Peng "Prediction of CF4/N2 adsorption and separation performance in organic frameworks based on machine learning algorithms", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127871I (16 August 2023); https://doi.org/10.1117/12.3004624
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KEYWORDS
Adsorption
Machine learning
Mathematical optimization
Decision trees
Monte Carlo methods
Performance modeling
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