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 Engineering    2017, Vol. 3 Issue (2) : 232 -243     https://doi.org/10.1016/J.ENG.2017.02.001
 Research |
Improving Prediction Accuracy of a Rate-Based Model of an MEA-Based Carbon Capture Process for Large-Scale Commercial Deployment
Xiaobo Luo,Meihong Wang()
Department of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, UK
Abstract
 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. Fund: Corresponding Authors: Meihong Wang Just Accepted Date: 17 March 2017   Online First Date: 27 April 2017    Issue Date: 27 April 2017