Abbas Azarpour
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
Also affiliated: Memorial University of Newfoundland (2018–2023); Shiraz University (2003–2005); Universiti Teknologi Petronas (2015–2017); University of Technology Malaysia (2011–2015)
Faculty Researcher
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Abbas Azarpour's research focuses on sustainable energy systems, biomass conversion, and predictive modeling for industrial processes. His work includes investigations into renewable and sustainable energy in North America, examining progress and challenges. Azarpour has published reviews on microalgal biomass and biodiesel production, as well as on the performance analysis and modeling of catalytic trickle-bed reactors. He has also explored thermoeconomic analyses of integrated systems for cogeneration and deterministic models for biomass torrefaction. More recently, his research has involved predictive modeling of CO2 adsorption in metal-organic frameworks using hybrid machine learning approaches and analyzing amine thermal degradation in CO2 capture processes. His scholarly contributions include 29 publications with over 1,100 citations and an h-index of 13.
Metrics
- h-index: 14
- Publications: 29
- Citations: 1,136
Selected Publications
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MOF-Based CO2 Adsorption Predictions: Role of Membership and Kernel Functions in Machine Learning Models (2025)
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Predictive Modeling of CO<sub>2</sub> Adsorption in Metal–Organic Frameworks Using Hybrid Machine Learning Approaches (2025)
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Deterministic Models for Performance Analysis of Lignocellulosic Biomass Torrefaction (2025)
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Thermoeconomic Analysis of an Innovative Integrated System for Cogeneration of Liquid Hydrogen and Biomethane by a Cryogenic-Based Biogas Upgrading Cycle and Polymer Electrolyte Membrane Electrolysis (2024)
Collaboration Network
Top Collaborators
- Thermoeconomic Analysis of an Innovative Integrated System for Cogeneration of Liquid Hydrogen and Biomethane by a Cryogenic-Based Biogas Upgrading Cycle and Polymer Electrolyte Membrane Electrolysis
- Deterministic Models for Performance Analysis of Lignocellulosic Biomass Torrefaction
- Predictive Modeling of CO<sub>2</sub> Adsorption in Metal–Organic Frameworks Using Hybrid Machine Learning Approaches
- Thermoeconomic Analysis of an Innovative Integrated System for Cogeneration of Liquid Hydrogen and Biomethane by a Cryogenic-Based Biogas Upgrading Cycle and Polymer Electrolyte Membrane Electrolysis
- Deterministic Models for Performance Analysis of Lignocellulosic Biomass Torrefaction
- Predictive Modeling of CO<sub>2</sub> Adsorption in Metal–Organic Frameworks Using Hybrid Machine Learning Approaches
- Thermoeconomic Analysis of an Innovative Integrated System for Cogeneration of Liquid Hydrogen and Biomethane by a Cryogenic-Based Biogas Upgrading Cycle and Polymer Electrolyte Membrane Electrolysis
- Thermoeconomic Analysis of an Innovative Integrated System for Cogeneration of Liquid Hydrogen and Biomethane by a Cryogenic-Based Biogas Upgrading Cycle and Polymer Electrolyte Membrane Electrolysis
- Predictive Modeling of CO<sub>2</sub> Adsorption in Metal–Organic Frameworks Using Hybrid Machine Learning Approaches
- MOF-Based CO2 Adsorption Predictions: Role of Membership and Kernel Functions in Machine Learning Models
- MOF-Based CO2 Adsorption Predictions: Role of Membership and Kernel Functions in Machine Learning Models
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