(181f) Dynamic Modeling, Validation and Optimization of a Solvent-Based CO2 Capture Process Under Flexible Operation | AIChE

(181f) Dynamic Modeling, Validation and Optimization of a Solvent-Based CO2 Capture Process Under Flexible Operation


Bhattacharyya, D. - Presenter, West Virginia University
Akula, P. - Presenter, West Virginia University
Eslick, J. C., National Energy Technology Laboratory
Miller, D., National Energy Technology Laboratory
Amines-based CO2 capture technology is the most matured technology for post combustion CO2 (PCC) capture. However, the parasitic loss in the capture plant and its dynamics can adversely affect the efficiency and load-following capability of the host plant. One way of mitigating these problems is flexible capture in the PCC unit where the extent of CO2 capture can be considered as a degree of freedom. However, during load-following operation of the host power plant, the CO2 capture unit is subjected to significant variability due to changing flue gas flowrate and composition. Flexible capture in the PCC unit under load-following operation requires excellent tracking control performance along with good disturbance rejection while maximizing the efficiency. Optimal design and operation of the capture plant using an accurate dynamic model is the key to satisfying such challenging operational requirements.

Development of dynamic models of the amine-based capture units including model validation with limited industrial data has been reported by various authors.1-5 However, a rate-based dynamic model with an accurate two-film model and electrolyte NRTL-based thermodynamic model is still missing in the literature. In this talk, development of such a rigorous dynamic model and its validation with large amount of industrial data will be presented.

The dynamic model is developed using the Institute for Design of Advanced Energy Systems (IDAES) Process System Engineering (PSE) computation platform.6 The tower model includes mass and heat transfer between the liquid and gas phases using a two-film model. Extended Fick’s law and Maxwell-Stefan equations are applied to describe the transport in the films. An electrolyte-NRTL model is developed for modeling the vapor-liquid equilibrium at the interface of the two films. The models are developed considering the ion-ion, ion-molecule and molecule-molecule interactions.7-9 The reaction kinetics are modeled so that the complex equilibrium behavior of these electrolyte systems can be captured in a thermodynamically consistent manner. The enthalpy model is developed so that it remains consistent with the thermodynamic model. A rigorous dynamic reboiler model is also developed. The tower models are validated with the dynamic test run data collected from the National Carbon Capture Center (NCCC) located in Wilsonville, Alabama, USA. These data sets are generated under considerable variation of solvent flowrate, gas flow rate, CO2 concentration in the gas, and steam flowrate to the reboiler as would be expected under the part-load operation of the host power plant. As the plant data are noisy and do not satisfy the mass and energy balances, a dynamic data reconciliation problem is solved. The validated dynamic model is used for optimal design and operation considering various flexible operation scenarios.


  1. Bui, M.; Flø, N. E.; de Cazenove, T.; Mac Dowell, N., Demonstrating flexible operation of the Technology Centre Mongstad (TCM) CO2 capture plant. International Journal of Greenhouse Gas Control 2020, 93, 102879.
  2. Bui, M.; Tait, P.; Lucquiaud, M.; Mac Dowell, N., Dynamic operation and modelling of amine-based CO2 capture at pilot scale. International Journal of Greenhouse Gas Control 2018, 79, 134-153.
  3. Biliyok, C.; Lawal, A.; Wang, M.; Seibert, F., Dynamic modelling, validation and analysis of post-combustion chemical absorption CO2 capture plant. International Journal of Greenhouse Gas Control 2012, 9, 428-445.
  4. Montañés, R. M.; Flø, N. E.; Nord, L. O., Experimental results of transient testing at the amine plant at Technology Centre Mongstad: Open-loop responses and performance of decentralized control structures for load changes. International Journal of Greenhouse Gas Control 2018, 73, 42-59.
  5. Flø, N. E.; Knuutila, H.; Kvamsdal, H. M.; Hillestad, M., Dynamic model validation of the post-combustion CO 2 absorption process. International Journal of Greenhouse Gas Control 2015, 41, 127-141.
  6. Miller, D. C.; Siirola, J. D.; Agarwal, D.; Burgard, A. P.; Lee, A.; Eslick, J. C.; Nicholson, B.; Laird, C.; Biegler, L. T.; Bhattacharyya, D., Next Generation Multi-Scale Process Systems Engineering Framework. In Computer Aided Chemical Engineering, Elsevier: 2018; Vol. 44, pp 2209-2214.
  7. Chen, C. C.; Evans, L. B., A local composition model for the excess Gibbs energy of aqueous electrolyte systems. AIChE Journal 1986, 32 (3), 444-454.
  8. Morgan, J. C.; Chinen, A. S.; Omell, B.; Bhattacharyya, D.; Tong, C.; Miller, D. C., Thermodynamic modeling and uncertainty quantification of CO2-loaded aqueous MEA solutions. Chemical Engineering Science 2017, 168, 309-324.
  9. Zhang, Y.; Que, H.; Chen, C.-C., Thermodynamic modeling for CO2 absorption in aqueous MEA solution with electrolyte NRTL model. Fluid Phase Equilibria 2011, 311, 67-75.