(767b) Modeling and Optimization of a Moving-Bed Chemical Looping Process for Coal Combustion

Chemical looping combustion (CLC) is a potentially transformative technology for efficient combined power generation and carbon capture from carbonaceous gaseous and solid fuels. The CLC process relies on the high-temperature oxidation-reduction cycling of a solid oxygen carrier (OC) which transports oxygen between two reactors, a fuel (reducer) reactor and an air (oxidizer) reactor. In the fuel reactor, the OC reacts with the fuel, generating near sequestration-ready CO₂ and water, while in the air reactor, the reduced OC is regenerated with air, producing a hot O₂-depleted air stream that can be used for power generation. Thus, CLC has the potential advantage over traditional fossil-fuel power generation technology of effectively capturing CO₂ without the need for an expensive CO₂ separation process.

Nearly all pilot-scale coal-CLC processes to date rely on fluidized bed reactors as fuel reactors [1], while some research groups have reported a moving-bed (MB) reactor approach for coal-CLC [1,2]. MB reactors have the potential advantages of low particle attrition, efficient control of the fuel residence time and high conversions of both fuel and OC, leading to a lower solids circulation rate compared to that required in fluidized beds [3]. Despite the advantages, rigorous solid-fueled MB reducer models do not exist in the literature. To assist with further development and scale-up of the MB coal-CLC technology, we have developed a rigorous MB model to help understand the complex solid-solid-gas interactions between the coal-fuel, OC and other gaseous species.

The developed MB model is a steady-state, one-dimensional, counter-current MB fuel reactor model suitable for the simulation and optimization of a coal-fueled CLC process using an iron-based OC. The equation-oriented model comprises of first principles mass and energy balances, and tightly coupled sub-models that represent the physical and thermodynamic properties, reactions, mass and heat transfer, and hydrodynamics of the MB fuel reactor. The model also provides axial profiles of the concentrations, temperatures, pressure, and velocities in the reactor.

The modeled counter-current MB fuel reactor consists of a devolatilization section, a volatiles section, and a char gasification section. In the devolatilization section, tar and gaseous volatiles get released from the incoming coal producing carbon-rich char. In the volatiles section, the OC particles that move downward are reduced by the gaseous volatiles flowing upward. In the char gasification section, the char particles are gasified by recycled enhancer gas (CO₂, H₂O) and the gasification products. This particular design of the MB reducer is capable of fully converting the coal feed, thus providing the opportunity for high-efficiency CO₂ capture [1].

The capabilities of the developed coal-fueled MB model are demonstrated by an application to the simulation and optimization of a complete coal-CLC unit, in which the developed coal-fueled MB reducer model is connected to gas-fueled MB [4] model as the oxidizer reactor, and other auxiliary equipment such as compressors and heat recovery equipment. The optimization objective is to minimize the total annualized cost (TAC), subject to design and operating constraints. For this analysis, the TAC includes the costs of the reactors, auxiliary equipment, the solids inventory, and operating costs. The large-scale optimization problem is solved using IPOPT [5].

The entire study is carried out using the Institute for the Design of Advanced Energy Systems (IDAES) open-source equation-oriented process systems engineering framework [6]. The IDAES framework was developed to aid the rapid development and optimization of next generation advanced energy systems, and is built using Pyomo [7], a Python-based algebraic modeling language.

1. Tong A., Kathe M.V., Wang D., and Fan L.-S. (2018) “The Moving Bed Fuel Reactor Process”, in Handbook of Chemical Looping Technology 1–40, Wiley-VCH Verlag GmbH & Co. KGaA., doi:10.1002/9783527809332, Ch 1.
2. Kim H.R., Wang D., Zeng L., Bayham S., Tong A., Chung E., Kathe M.V., Luo S., McGiveron O., Wang A., Sun Z., Chen D., Fan L.-S. (2013) “Coal direct chemical looping combustion process: Design and operation of a 25-kWth sub-pilot unit”. Fuel, 108, 370-384.
3. Adánez J., Abad A., Mendiara T., Gayan P., de Diego L. F., and Garcia-Labiano F. (2018) “Chemical looping combustion of solid fuels”. Prog Energy Combust Sci., 65, 6–66.
4. Ostace A., Lee A., Okoli C.O., Burgard A.P., Miller D.C., and Bhattacharyya D. (2018) “Mathematical Modeling of a Moving-Bed Reactor for Chemical Looping Combustion of Methane”, Proceedings of the 13th International Symposium on Process Systems Engineering, Comput-Aided Chem En, 44, pp. 325-330, Elsevier, Amsterdam, M. R. Eden, M. Ierapetritou and G. P. Towler (eds.)
5. Wächter A., and Biegler, L.T. (2006) “On the implementation of an interior-point filter line search algorithm for large-scale nonlinear programming”, Math Program, 106, 25 – 57.
6. Miller D.C., Siirola J.D., Agarwal D.A., Burgard A.P., Lee A., Eslick J.C., Nicholson B.L., Laird C.D., Biegler L.T., Bhattacharyya D., Sahinidis N.V., Grossmann I.E., Gounaris C.E., Gunter D. (2018) “Next generation multi-scale process systems engineering framework”, Proceedings of the 13th International Symposium on Process Systems Engineering, Comput-Aided Chem En, 44, pp. 2209-2214, Elsevier, Amsterdam, M. R. Eden, M. Ierapetritou and G. P. Towler (eds.)
7. Hart W.E., Laird C.D., Watson J.P., Woodruff D.L., Hackebeil G.A., Nicholson B.L., and Siirola J.D. (2017) “Pyomo - Optimization Modeling in Python”, 2nd ed. Springer.