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Harvesting Electrical Power during Carbon Capture using Various Amine Solvents.

ABSTRACT: There exists an urgent demand for the advancement of technologies that reduce and capture carbon dioxide (CO₂) emissions to mitigate anthropogenic contributions to climate change. This paper compares the maximum power densities achieved from the combination of reverse electrodialysis (RED) with carbon capture (CC) using various CC solvents. Carbon capture reverse electrodialysis (CCRED) harvests energy from the salinity gradients generated from the reaction of CO₂ with specific solvents, generally amines. To eliminate the requirement of freshwater as an external resource, we took advantage of a semiclosed system that would allow the inputs to be industrial emissions and heat and the outputs to be electrical power, clean emissions, and captured CO₂. We assessed the power density that can be attained using CCRED with five commonly studied CC solvents: monoethanolamine (MEA), diethanolamine (DEA), N-methyldiethanolamine (MDEA), 2-amino-2-methyl-2-propanol (AMP), and ammonia. We achieved the highest power density, 0.94 W m^-2 cell^-1, using ammonia. This work provides a foundation for future iterations of CCRED that may help to incentivize adoption of CC technology.

SUBMITTER: Kalkus TJ

PROVIDER: S-EPMC9483915 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Harvesting Electrical Power during Carbon Capture using Various Amine Solvents.

Kalkus Trevor J TJ Shanahan Caitlin J CJ Smart Jansie J Coskun Ali A Mayer Michael M

Energy & fuels : an American Chemical Society journal 20220831 18

There exists an urgent demand for the advancement of technologies that reduce and capture carbon dioxide (CO<sub>2</sub>) emissions to mitigate anthropogenic contributions to climate change. This paper compares the maximum power densities achieved from the combination of reverse electrodialysis (RED) with carbon capture (CC) using various CC solvents. Carbon capture reverse electrodialysis (CCRED) harvests energy from the salinity gradients generated from the reaction of CO<sub>2</sub> with spec ...[more]

PMID: 36148000

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Project description:In recent years, deep eutectic solvents (DESs) have garnered considerable attention for their potential in carbon capture and utilization processes. Predicting the carbon dioxide (CO2) solubility in DES is crucial for optimizing these solvent systems and advancing their application in sustainable technologies. In this study, we presented an evolving hybrid Quantitative Structure-Property Relationship and Gaussian Process Regression (QSPR-GPR) model that enables accurate predictions of CO2 solubility in various DESs. The QSPR-GPR model combined the strengths of both approaches, leveraging molecular descriptors and structural features of DES components to establish a robust and adaptable predictive framework. Through a systematic evolution process, we iteratively refined the model, enhancing its performance and generalization capacity. By incorporating experimental CO2 solubility data in varied DES compositions and temperatures, we trained the model to capture the intricate solubility behaviour precisely. The analytical capability of the evolving hybrid model was validated against an extensive dataset of experimental CO2 solubility values, demonstrating its superiority over individual QSPR and GPR models. The model achieves high accuracy, capturing the complex interactions between CO2 and DES components under varying thermodynamic conditions. The versatility of the evolving hybrid model was highlighted by its ability to accommodate new experimental data and adapt to different DES compositions and temperatures. The proposed QSPR-GPR model presented a powerful tool for predicting CO2 solubility in DES, providing valuable insights for designing and optimizing solvent systems in carbon capture technologies. The model's remarkable performance enhances our understanding of CO2 solubility mechanisms and contributes to sustainable solutions for mitigating greenhouse gas emissions. As research in DESs progresses, the evolving hybrid QSPR-GPR model offers a versatile and accurate means for predicting CO2 solubility, supporting advancements in carbon capture and utilization processes towards a greener and more sustainable future.

Dataset Information

Harvesting Electrical Power during Carbon Capture using Various Amine Solvents.

Publications

Harvesting Electrical Power during Carbon Capture using Various Amine Solvents.

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