(274g) Optimal Design of Gas-Fired Moving-Bed Chemical Looping Combustion Systems

Considerable improvement in computational power and numerical algorithms over the past few decades has enabled the use of large-scale, first-principles equation-oriented process models for the design, scale-up and optimization of new and existing technologies. While several efficient commercial simulators offer a library of basic unit operations and support the development of equation-oriented mathematical models of complex unit operations and flowsheets, they often have limited capabilities for solving large-scale process system engineering optimization problems. In contrast, algebraic modeling languages (AMLs) enable the simultaneous solution and optimization of high-complexity large-scale process models, but do not provide a library of unit models, and building the required models and flowsheets often becomes a demanding, time consuming task.

The Institute for the Design of Advance Energy Systems (IDAES) is developing a comprehensive, next generation computational framework [1] to facilitate the design and optimization of innovative energy systems and related chemical engineering systems. The IDAES framework is developed in Pyomo [2], an open-source Python-based AML, and not only supports a wide range of optimization solvers, but also provides a library of open (editable) standard and complex unit operation models [3]. In this study, we demonstrate the capabilities of the IDAES framework by presenting the development of a complex unit model and its use in a multi-objective flowsheet optimization problem. The application area of interest is the optimization of methane-based chemical looping combustion (CLC) systems using different oxygen carrier materials.

CLC is a novel, promising advanced energy technology for the conversion of fossil fuels with inherent carbon capture. It consists of the cyclic reduction-oxidation of an oxygen carrier, generally a metal oxide, circulating between two reactors: a fuel (reducer) reactor and an air (oxidizer) reactor. The oxygen necessary for fuel conversion in the reducer reactor is provided by the oxygen carrier, thus ensuring combustion in pure oxygen rather than air. The exhaust gas leaving the fuel reactor is mainly a mixture of carbon dioxide (CO₂) and water (H₂O) from which H₂O can be condensed resulting a CO₂-rich stream ready for utilization or storage. Under carbon-constrained scenarios, CLC is an attractive alternative to conventional technologies due to its high efficiency and lower cost compared with post-combustion CO₂ capture.

The goal of this work is the optimal design of the chemical looping reactors for methane combustion carried out in two interconnected moving-bed (MB) reactors. Detailed models of MB reactors for CLC are currently lacking in the literature. In our previous work, a 1-D steady-state, nonisothermal, first-principles model of a MB reactor with hydrodynamics under reduction conditions was developed. The model is flexible and modular, and it can be readily adapted for the simulation of different gas-solid processes [4]. In this study, first an oxidizer reactor model is developed by modifying the reducer reactor model. Then, the development of a flowsheet for the CLC of methane using three promising oxygen carriers, Fe-, Cu-, and Ni-based carriers, is presented. Detailed property packages along with the kinetic models [5] governing the reduction and oxidation reactions of the different oxygen carriers are developed and used in the MB models. Finally, the equation-oriented process flowsheet is used to optimize the efficiency of the fuel reactor, while minimizing the capital and operational costs. The large-scale multi-objective optimization problem is solved using IPOPT. The feasibility of the interconnected MB reactor system for the combustion of methane with each oxygen carrier is investigated and the optimal design of the reactors is determined.

References

1. 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., and Gunter D., 2018, “Next Generation Multi-Scale Process Systems Engineering Framework”, Proceedings of the 13th International Symposium on Process Systems Engineering, San Diego, California, USA.
2. 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”, Second Edition. Vol. 67. Springer.
3. Lee A., Ghouse J.H., Chen Q., Eslick J.C., Siirola J.D., Grossmann I.E., Miller D.C., 2018, “A Flexible Framework and Model Library for Process Simulation, Optimization and Control”, Proceedings of the 13th International Symposium on Process Systems Engineering, San Diego, California, USA.
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, San Diego, California, USA.
5. Abad A., Adanez J., Garcia-Labiano F., de Diego L.F., Gayan P., and Celaya J., 2007, “Mapping of the range of operational conditions for Cu-, Fe-, and Ni-based oxygen carriers in chemical-looping combustion”, Chemical Engineering Science, 62, pp. 533-549