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19 July 2024 Adsorption performance of refrigerants in metal-organic frameworks using machine learning prediction
Shaoli Dong
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Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131818D (2024) https://doi.org/10.1117/12.3031229
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China
Abstract
Metal-organic frameworks (MOFs) have received significant attention in the domains of adsorption refrigeration and energy storage, owing to their exceptional characteristics. In this study, machine-learning techniques were employed to anticipate the adsorption potential of refrigerants within MOFs. And an approach to characterize the MOF-refrigerant working pairs was proposed. By applying Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP) algorithms, the adsorption capacity of refrigerants in MOFs at different temperatures and pressures were predicted with correlation coefficients of 0.9489, 0.9134, and 0.9017 respectively, along with absolute average deviations of 18.69%, 29.39%, and 21.86%. Furthermore, the predicted adsorption curves by the neural network model showed good fitting to experimental curves with deviations within 15% at saturation points and inflection points.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Shaoli Dong "Adsorption performance of refrigerants in metal-organic frameworks using machine learning prediction", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131818D (19 July 2024); https://doi.org/10.1117/12.3031229
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KEYWORDS
Adsorption
Micro optical fluidics
Machine learning
Performance modeling
Engineering
Materials properties
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