Dynamic modelling and simulation of a post-combustion CO2 capture process for coal-fired power plants

Jianlin Li, Ti Wang, Pei Liu, Zheng Li

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Front. Chem. Sci. Eng. ›› 2022, Vol. 16 ›› Issue (2) : 198-209. DOI: 10.1007/s11705-021-2057-7
RESEARCH ARTICLE
RESEARCH ARTICLE

Dynamic modelling and simulation of a post-combustion CO2 capture process for coal-fired power plants

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Abstract

Solvent-based post-combustion capture technologies have great potential for CO2 mitigation in traditional coal-fired power plants. Modelling and simulation provide a low-cost opportunity to evaluate performances and guide flexible operation. Composed by a series of partial differential equations, first-principle post-combustion capture models are computationally expensive, which limits their use in real time process simulation and control. In this study, we propose a first-principle approach to develop the basic structure of a reduced-order model and then the dominant factor is used to fit properties and simplify the chemical and physical process, based on which a universal and hybrid post-combustion capture model is established. Model output at steady state and trend at dynamic state are validated using experimental data obtained from the literature. Then, impacts of liquid-to-gas ratio, reboiler power, desorber pressure, tower height and their combination on the absorption and desorption effects are analyzed. Results indicate that tower height should be designed in conjunction with the flue gas flow, and the gas-liquid ratio can be optimized to reduce the reboiler power under a certain capture target.

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Keywords

CO2 capture / post-combustion capture / simulation / dominant factor

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Jianlin Li, Ti Wang, Pei Liu, Zheng Li. Dynamic modelling and simulation of a post-combustion CO2 capture process for coal-fired power plants. Front. Chem. Sci. Eng., 2022, 16(2): 198‒209 https://doi.org/10.1007/s11705-021-2057-7

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Acknowledgements

This study was support by The National Key Research and Development of China (Grant No. 2019YFE0100100), Shanxi Key Research and Development Program (Grant No. 201603D312001), the National Natural Science Foundation of China (Grant No. 71690245), and the Phase III Collaboration between BP and Tsinghua University.

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