(463f) Equation-Oriented Economic Optimization Model for an Amine-Based Post-Combustion Carbon Capture Flowsheet

Amine-based post-combustion capture is a promising technology to efficiently mitigate the increasing CO₂ emissions at point sources such as power and chemical plants. There have appeared lots of works in the literature trying to improve these systems. One prominent novelty in this field is the Piperazine/Advanced Flash Stripper (PZ/AFS) process that is claimed to be the new benchmark for second-generation amine scrubbing technologies [1]. This process uses PZ as solvent and employs the AFS modification, which entails using a flash tank integrated with the stripper column and a steam heater. Even though this novel process decreases the energy required for carbon capture, there are few commercial post-combustion installations of any kind and it is only recently reaching higher Technology Readiness Levels (TRL). Mitigating carbon dioxide emissions through adding carbon capture process capability is almost always an additional cost for existing facilities, so avenues to reduce cost of capture will play a crucial role towards accelerating the commercialization of carbon capture flowsheets. Keeping this in the forefront, the aim of this work is to improve the economic viability of this technology through process optimization.

In this work, we present a rigorous, equation-oriented mathematical model of this process built in Pyomo, a Python-based algebraic modeling language [2]. The main units in the model are the absorber and stripper columns, which were modeled in a rate-based fashion to improve fidelity, and then discretized via a finite difference methodology. This led to a non-linear programming (NLP) model with over 8400 variables and constraints, for which we also developed a custom initialization scheme to ensure its robust convergence.

The model was validated using pilot plant data [3, 4]. Optimization with an economic objective was performed to determine the optimum design and operating conditions with an economic model that considers both capital and operational costs [5]. Optimization at pilot scale showed that significant yearly savings can be achieved compared to the original design and conditions at the plant. Furthermore, optimization was performed for varying flowrates of flue gas to analyze the change of cost and optimal design, in support of scaled up units that shall be required with the more widespread use of this process.

The cost of capture was evaluated for different plant capacities and at varying capture targets between 90 to 98%. The model was able to converge with more than 600 times more flue gas than what is processed at the pilot scale, demonstrating numerical robustness. Moreover, the optimal design and conditions were compared for two different flue gas sources (coal-fired and natural gas combined cycle) with different CO₂ contents. Additional sensitivity analyses pertaining to epistemic and economic uncertainties were performed.

References:

[1] Gary T. Rochelle, Yuying Wu, Eric Chen, Korede Akinpelumi, Kent B. Fischer, Tianyu Gao, Ching-Ting Liu, and Joseph L. Selinger. Pilot Plant Demonstration of Piperazine with the Advanced Flash Stripper. International Journal of Greenhouse Gas Control, 84:72–81, 2019.

[2] William E. Hart, Jean-Paul Watson, and David L. Woodruff. Pyomo: Modeling and Solving Mathematical Programs in Python. Mathematical Programming Computation 3(3): 219-260, 2011.

[3] Yue Zhang, Darshan Sachde, Eric Chen, and Gary Rochelle. Modeling of Absorber Pilot Plant Performance for CO2 Capture with Aqueous Piperazine. International Journal of Greenhouse Gas Control, 64:300–313, 2017.

[4] Jorge Mario Plaza. Modeling of Carbon Dioxide Absorption Using Aqueous Monoethanolamine, Piperazine and Promoted Potassium Carbonate. PhD Dissertation, University of Texas at Austin, 2012.

[5] Patricia Mores, Nestor Rodriguez, Nicolas Scenna, and Sergio Mussati. CO2 Capture in Power Plants: Minimization of the Investment and Operating Cost of the Post-Combustion Process Using MEA Aqueous Solution. International Journal of Greenhouse Gas Control, 10:148-163, 2012.