(183f) Mathematical Modeling and Economic Optimization of a Novel Amine-Based Post-Combustion Carbon Capture Process

Mitigating CO₂ emissions from fossil-fuel based power plants is a major requirement if we are to effectively alleviate the global warming problem. In this context, amine scrubbing is a mature and prominent post-combustion capture technology that offers flexibility in scale-up, on/off operation according to demand and retrofit possibilities for existing plants [1]. There is on-going research on exploring new solvents that are more efficient than the traditionally used Monoethanolamine (MEA) as well as on modifying conventional process flowsheets [2]. One novelty in this field that capitalizes on both these opportunities is the Piperazine/Advanced Flash Stripper (PZ/AFS) process. This process, which is viewed as the new benchmark for second-generation amine scrubbing technologies [3], uses PZ as solvent and employs the AFS modification that entails using a flash tank integrated with the stripper column and a steam heater instead of the conventional simple stripper with reboiler. This offers smaller footprint and lower capital costs [3]. Even though this novel process can in principle decrease the energy required for carbon capture significantly, it is not yet deployed at an industrial scale and can benefit from additional process optimization effort to make it more viable economically.

To optimize this system, a rigorous mathematical model of the flowsheet is needed. In this work, we present an equation-oriented, rate-based model of this process built in Pyomo, a Python-based algebraic modeling language [4]. The main units in the model are the absorber and stripper columns, which are modeled in a rate-based fashion to achieve satisfactory model fidelity. Using finite differences to evaluate the spatial differential components, mass and heat transfer are modeled using the two-film theory [5, 6]. The effect of liquid film reactions is accounted for with an enhancement factor that requires solving the mass transfer model simultaneously with a speciation model consisting of seven equilibrium reactions [5, 7]. The model also includes a flash tank and heat exchangers along with a complex recycle stream between the two columns with added make-up of water and PZ. Under sufficient resolution of column discretization, the size of the model resulting from this effort exceeds 8,000 constraints and 8,000 variables.

It is important to highlight that a non-linear programming problem (NLP) of this size does not converge easily, and to this end, we developed a custom initialization scheme to overcome this challenge. Simulations of the temperature profile in the absorber, which is highly intertwined with the mass transfer, were validated using pilot plant data for the cases with and without intercooler usage [8, 9], while we performed sensitivity analyses on the design and operating conditions that led to important insights about the system. Finally, we implemented an economic objective to account for both capital and operating expenditures [10, 11], which we used as the basis for performing model optimization to determine the minimum cost design.

References:

[1] Gary Rochelle, Eric Chen, Stephanie Freeman, David Van Wagener, Qing Xu, and Alexander Voice. Aqueous piperazine as the new standard for CO2 capture technology. Chemical Engineering Journal, 171(3):725–733, 2011. Special Section: Symposium on Post-Combustion Carbon Dioxide Capture.

[2] Evie Nessi, Athanasios I. Papadopoulos, Panos Seferlis. A review of research facilities, pilot and commercial plants for solvent-based post-combustion CO2 capture: Packed bed, phase-change and rotating processes. International Journal of Greenhouse Gas Control, 111: 103474, 2021.

[3] 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.

[4] 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.

[5] H.M. Kvamsdal, J.P. Jakobsen, and K.A. Hoff. Dynamic modeling and simulation of a CO2 absorber column for post-combustion co2 capture. Chemical Engineering and Processing: Process Intensification, 48(1):135–144, 2009.

[6] Calvin Tsay, Richard C. Pattison, Yue Zhang, Gary T. Rochelle, and Michael Baldea. Rate-based modeling and economic optimization of next-generation amine-based carbon capture plants. Applied Energy, 252:113379, 2019.

[7] Sanjay Bishnoi and Gary T. Rochelle. Absorption of carbon dioxide into aqueous piperazine: reaction kinetics, mass transfer and solubility. Chemical Engineering Science, 55(22):5531–5543, 2000.

[8] 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.

[9] Jorge Mario Plaza. Modeling of carbon dioxide absorption using aqueous monoethanolamine,

piperazine and promoted potassium carbonate. PhD Dissertation, University of Texas at Austin (2012).

[10] 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.

[11] Natalie M. Isenberg, Paul Akula, John C. Eslick, Debangsu Bhattacharyya, David C. Miller, and Chrysanthos E. Gounaris. A generalized cutting‐set approach for nonlinear robust optimization in process systems engineering. AIChE J. 2021.