Maheri, Mahmoud (2024) New perspectives on gas adsorption and diffusion in solid porous systems: integrating numerical modeling and machine learning. Masters thesis, Memorial University of Newfoundland.
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Abstract
The escalating levels of atmospheric CO₂ necessitate efficient and sustainable solutions for its capture and sequestration. This thesis investigates the theoretical and practical aspects of gas diffusion and adsorption in KOH-treated activated carbon and Biomass Waste Derived Porous Carbon, employing advanced mathematical modeling and machine learning techniques. The objective is to contribute to the development of sustainable technologies to mitigate climate change impacts. In this work, we correlate diffusivity with the uptake rate and physical properties of both gas and solid phases, integrating these factors into the governing diffusion equations. Through an extended Fick’s Law model, we develop a new mathematical framework that predicts gas adsorption behaviors under diverse operational and thermodynamic conditions. This model is validated against experimental data across various adsorbents, demonstrating excellent alignment with real-world adsorption rates, particularly for KOH-treated activated carbons. The high accuracy of this model underscores its robustness and reliability in predicting adsorption dynamics. To further enhance predictive accuracy and computational efficiency, advanced machine learning models including GBR, DNN, CNN, and DWNN are applied. Trained on extensive datasets of CO₂ adsorption characteristics, these models outperform traditional approaches and identify the critical features influencing adsorption, such as surface area and carbon-to-pressure ratios, which are essential for optimizing gas adsorption systems. Our findings illustrate the potential of combining theoretical modeling with machine learning to improve the design, operation, and optimization of gas adsorption and separation processes, such as CO2 capture and natural gas processing. This work contributes significantly to the advancement of scalable, efficient gas separation technologies, paving the way for sustainable solutions in climate change mitigation and resource management.
| Item Type: | Thesis (Masters) |
|---|---|
| URI: | http://research.library.mun.ca/id/eprint/16846 |
| Item ID: | 16846 |
| Additional Information: | Includes bibliographical references (pages 108-121) --Restricted until December 17, 2025 |
| Keywords: | CO₂ adsorption, KACa KACi, Fick's Law, machine learning, BWDPC |
| Department(s): | Science, Faculty of > Computer Science |
| Date: | December 2024 |
| Date Type: | Submission |
| Digital Object Identifier (DOI): | https://doi.org/10.48336/75fp-st56 |
| Library of Congress Subject Heading: | Carbon dioxide mitigation; Machine learning |
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