Improving Prediction Accuracy of a Rate-Based Model of an MEA-Based Carbon Capture Process for Large-Scale Commercial Deployment

Xiaobo Luo; Meihong Wang

doi:10.1016/J.ENG.2017.02.001

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Engineering ›› 2017, Vol. 3 ›› Issue (2) : 232-243. DOI: 10.1016/J.ENG.2017.02.001

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Improving Prediction Accuracy of a Rate-Based Model of an MEA-Based Carbon Capture Process for Large-Scale Commercial Deployment

Xiaobo Luo ,
Meihong Wang

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Abstract

Carbon capture and storage (CCS) technology will play a critical role in reducing anthropogenic carbon dioxide (CO2) emission from fossil-fired power plants and other energy-intensive processes. However, the increment of energy cost caused by equipping a carbon capture process is the main barrier to its commercial deployment. To reduce the capital and operating costs of carbon capture, great efforts have been made to achieve optimal design and operation through process modeling, simulation, and optimization. Accurate models form an essential foundation for this purpose. This paper presents a study on developing a more accurate rate-based model in Aspen Plus^® for the monoethanolamine (MEA)-based carbon capture process by multistage model validations. The modeling framework for this process was established first. The steady-state process model was then developed and validated at three stages, which included a thermodynamic model, physical properties calculations, and a process model at the pilot plant scale, covering a wide range of pressures, temperatures, and CO2 loadings. The calculation correlations of liquid density and interfacial area were updated by coding Fortran subroutines in Aspen Plus^®. The validation results show that the correlation combination for the thermodynamic model used in this study has higher accuracy than those of three other key publications and the model prediction of the process model has a good agreement with the pilot plant experimental data. A case study was carried out for carbon capture from a 250 MWe combined cycle gas turbine (CCGT) power plant. Shorter packing height and lower specific duty were achieved using this accurate model.

Keywords

Process modeling / Model validation / Monoethanolamine / Carbon capture / Combined cycle gas turbine power plant / Carbon capture and storage

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Xiaobo Luo, Meihong Wang. Improving Prediction Accuracy of a Rate-Based Model of an MEA-Based Carbon Capture Process for Large-Scale Commercial Deployment. Engineering, 2017, 3(2): 232‒243 https://doi.org/10.1016/J.ENG.2017.02.001

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Acknowledgement

This work was supported by the EU FP7 Marie Curie International Research Staff Exchange Scheme (PIRSES-GA-2013-612230).

Compliance with ethics guidelines

Xiaobo Luo and Meihong Wang declare that they have no conflict of interest or financial conflicts to disclose.

Funding

RIGHTS & PERMISSIONS

2017 2017 THE AUTHORS. Published by Elsevier LTD on behalf of the Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).