(445g) Rate-Based Dynamic Modeling and Analysis of an Amine-Based Carbon Capture Unit for Flexible Operation

Achieving net-zero carbon emissions is one of the greatest challenges for human society. Modeling and simulation of advanced multiscale process units have great potential in accelerating the commercialization of carbon capture technologies¹. Amine-based CO₂ capture is the baseline technology for retrofitting existing power stations. However, the integration of amine-based post-combustion CO₂ capture technology with power plants to reduce greenhouse gas emissions incurs a high energy penalty decreasing a power plant’s efficiency by about 23 percentage points². Understanding the capture plant dynamics plays an important role in its technical and economic performance. Capital and operating costs of capture plants strongly depend on the configuration³. For a given configuration, operating conditions play a vital role in reducing the operating costs⁴. Flowrate and composition of the flue gas change as the host power plant ramps up and down to meet required power output. Hence, various strategies can be considered for improving the economics of the capture unit. For example, loaded solvent can be stored when the price of electricity is high and regenerated when the price drops. Thus, the capture unit must be operated optimally under dynamic, off-design conditions. For dynamic optimization, an accurate dynamic model that captures off-design operation of the amine-scrubbing unit is required. Rate-based dynamic models of amine scrubbing units are limited in the open literature compared with steady-state models. Most existing dynamic rate-based models have been developed assuming linear driving force across the liquid and gas films^5-8. This assumption can lead to considerable inaccuracy under off-design conditions due to nonlinearity in the transport variables in the film region, especially under off-design dynamic operation. Furthermore, existing rate-based dynamic models consider simple thermodynamic models and constant heat of reaction that do not adequately represent the complex thermodynamics of electrolyte systems.

In this study, a dynamic model of a packed tower is developed for a monoethanolamine(MEA)-based capture unit considering simultaneous mass transfer and chemical reactions in the films. Multi-component transport of molecular and ionic species is modeled using an extended Maxwell-Stefan (MS) transport equation that includes the effect of electrostatic forces for reactive absorption process. A comprehensive description of the thermodynamic framework for electrolyte systems represented by the electrolyte-Non-Random Two Liquid is presented where analytical expressions for excess enthalpy is developed that can improve the accuracy of the enthalpy model for these highly nonlinear systems compared to numerical approaches for computing excess enthalpy. The dynamic model is validated using transient data from the National Carbon Capture Center in Alabama, USA. For dynamic optimization, the simultaneous approach is adopted over the sequential approach by fully discretizing in space and time⁹. While the fully discretized approach offers many advantages, including direct Jacobian and Hessian calculation within the optimizer and efficient decomposition strategies that can exploit structure and sparsity of the system of equations⁹, this approach leads to a large-scale optimization problem. The problem is solved using the flexible, open-source Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES)¹⁰ which provides access to efficient large-scale NLP solvers. Several approaches are developed for generating good initial guesses for future state and algebraic variables. In addition, capabilities in IDAES for activating and deactivating constraints are exploited to develop a sequential initialization strategy. Our results show that dynamic optimization not only reduced the energy usage, but also reduces mass transfer limitations thus improving the economics of capture processes under fast, part-load operations.

References

1. 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.
2. Supekar, S. D.; Skerlos, S. J., Reassessing the efficiency penalty from carbon capture in coal-fired power plants. Environmental science & technology 2015, 49 (20), 12576-12584.
3. Bhattacharyya, D.; Miller, D., Post-combustion CO 2 capture technologies — a review of processes for solvent-based and sorbent-based CO 2 capture. Current Opinion in Chemical Engineering 2017, 17, 78-92.
4. Akula, P.; Eslick, J.; Bhattacharyya, D.; Miller, D. C., Model Development, Validation, and Optimization of an MEA-Based Post-Combustion CO2 Capture Process under Part-Load and Variable Capture Operations. Industrial & Engineering Chemistry Research 2021.
5. Montañés, R. M.; Flø, N. E.; Nord, L. O., Dynamic process model validation and control of the amine plant at CO2 Technology Centre Mongstad. Energies 2017, 10 (10), 1527.
6. Ziaii, S.; Rochelle, G. T.; Edgar, T. F., Dynamic Modeling to Minimize Energy Use for CO2 Capture in Power Plants by Aqueous Monoethanolamine. Industrial & Engineering Chemistry Research 2009, 48 (13), 6105-6111.
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8. Nittaya, T.; Douglas, P. L.; Croiset, E.; Ricardez-Sandoval, L. A., Dynamic modeling and evaluation of an industrial-scale CO2 capture plant using monoethanolamine absorption processes. Industrial & Engineering Chemistry Research 2014, 53 (28), 11411-11426.
9. Biegler, L. T.; Zavala, V. M., Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization. Computers & Chemical Engineering 2009, 33 (3), 575-582.
10. Institute for the Design of Advanced Energy Systems (IDAES) https://idaes-pse.readthedocs.io/en/stable (accessed 4/1/2021).