New Perspectives on Gas Adsorption and Diffusion in Solid Porous Systems - Integrating Numerical Modeling and Machine Learning

Aug 5, 2026

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Elevated atmospheric CO2 levels drive demand for efficient, sustainable capture and sequestration strategies. This thesis analyzes gas diffusion and adsorption in KOH-treated activated carbon and biomass waste derived porous carbon using mathematical modeling and machine learning. A Fick’s Law–based framework correlates gas and solid phase diffusivity with uptake rate and physical properties, enabling prediction of adsorption behaviors across operational and thermodynamic conditions. Model predictions are validated with experimental data, and advanced ML models (GBR, DNN, CNN, DWNN) improve accuracy while highlighting key factors such as surface area and carbon-to-pressure ratios.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages136

Reading Time5 h 43 min

AccessFree to download

Introduction

Objectives

Scope of Study

Structure of the Thesis

Background and Related Work

CO2 Capture Technologies

Post-combustion Capture

Direct Air Capture

BWDPC for CO2 Capture

New Perspectives on Gas Adsorption and Diﬀusion in Solid Porous Systems: Integrating Numerical Modeling and Machine Learning by © Mahmoud Maheri A thesis submitted to the Department of Computer Science in partial fulﬁllment of the requirements for the degree of Master of Science Supervisors: Dr. Hamid Useﬁ, Dr. Sohrab Zendehboudi, Dr. Carlos Bazan Department of Computer Science Memorial University of Newfoundland December 2024 St. John’s Newfoundland 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. Acknowledgments I would like to express my deepest gratitude to Dr. Hamid Useﬁ, Dr. Sohrab Zendehboudi, and Dr. Carlos Bazan for their invaluable mentorship, guidance, and unwavering support throughout the course of this thesis. Their insightful feedback, constructive criticism, and encouragement have been instrumental in shaping both my personal and professional growth. I am truly grateful for the time and eﬀort they invested in helping me succeed. I also wish to extend my sincere thanks to the Department of Computer Science at Memorial University and the Process Engineering Department for providing the resources and environment that made this research possible. Their support has been critical to my ability to pursue and complete this work. I dedicate this work to the loving memory of my dear grandmother, Roghayyeh, and my beloved mother, Ashraf, whose absence I deeply feel every day. Their love and wisdom continue to guide me, and though they are no longer with me, I carry their memories in my heart always. Finally, my heartfelt appreciation goes to my son, Arash, whose constant companionship and unwavering support have been a source of strength and motivation during the most challenging moments of this journey. His presence has been a true blessing, and I dedicate this work to him as well. Statement of Originality I hereby declare that this thesis, entitled "New Perspectives on Gas Adsorption and Diﬀusion in Solid Porous Systems: Integrating Numerical Modeling and Machin

FAQs

What materials and process conditions does the thesis focus on for gas adsorption research?

The research investigates gas diffusion and adsorption in KOH-treated activated carbon and biomass waste derived porous carbon, considering diverse operational and thermodynamic conditions.

How is the extended Fick’s Law model used in the study?

The work develops a new framework based on an extended Fick’s Law approach, integrating diffusivity and uptake behavior across gas and solid phases to predict adsorption performance.

Which machine learning methods are applied, and what do they help identify?

GBR, DNN, CNN, and DWNN are trained on CO2 adsorption datasets to outperform traditional methods and to identify key influencing features such as surface area and carbon-to-pressure ratios.

Aug 5, 2026

Upload by Ophelia