Development of an empirical model for microwave assisted CO2 stripping/solvent recovery
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Shree Vidhya Ramamoorthy
Abstract
With global CO2 emissions continuing to rise, finding efficient and scalable carbon capture solutions is more important than ever. Among available technologies, post-combustion carbon capture (PCCC) using solvents like monoethanolamine (MEA), piperazine (PZ), and potassium carbonate (K2CO3) remains one of the most practical and widely adopted methods. Traditional regeneration methods, such as steam stripping, are often energy-intensive and limit process efficiency. This study explores the use of microwave (MW) heating as a promising alternative for solvent regeneration, aiming to reduce energy use and improve system performance. Experiments were designed using Taguchi’s L27 orthogonal array, testing the effects of input power, regeneration time, solvent load, and solvent type. Results showed that PZ had the highest stripping rate, while K2CO3 offered the best diffusivity. The stripping rate increased linearly with input power due to enhanced electromagnetic energy transfer. An optimum regeneration time of 15 min and solvent load of 40 g provided maximum stripping performance. An empirical model was developed to predict CO2 stripping efficiency, incorporating stripping rate, diffusivity, nominal input power, rich and lean carbon loading, density, viscosity, surface tension, initial, final and average temperatures, molecular weight of solvent and CO2, change in mass after experiment, regeneration time, initial and final pH considered to develop the model. The correlation matrix and dimensional analysis were employed to formulate dimensionless groups influencing stripping efficiency and least square method was utilized to develop the model. t-test as a statistical validation technique confirmed model’s reliability at 98.9 % accuracy and 95 % confidence level signifying excellent predictive capability.
Acknowledgments
The authors acknowledge the financial support of the Sri Sivasubramaniya Nadar (SSN) Trust, Sri Sivasubramaniya Nadar College of Engineering (SSNCE), Chennai, Tamil Nadu, India.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: SR- Methodology, Formal Analysis, Investigation, Validation, Data curation, Writing – Original Draft, Writing - Review & Editing, Visualization. AB- Conceptualization, Methodology, Resources, Writing – Original Draft, Writing – Review & Editing, Project Administration, Supervision.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.
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