466883 Performance Analysis of Chemical-Looping Fixed Bed Reactors Integrated in Combined Cycle Power Plants
This work presents a dynamic model for an integrated CLC-CC power plant and transient analyses of the integrated plant performance. A tool chain is presented for modeling the integrated CLC-based combined cycle power plant, coupling different equation-oriented platforms. Optimization of high-pressure fixed bed CLC reactor is performed to improve the efficiency of the downstream CC by maximizing the utilization of the gas turbine of the power plant. Optimal operating strategies are explored for the control of the temperature variations inside the reactor bed. The resulting time-varying gas stream exiting the CLC reactor is used by the gas turbine and steam cycle of a dynamic combined cycle power plant model. The power plant model is first validated against steady-state data from an existing natural gas-fueled, combined cycle power plant.10Then the combustion chamber of this plant is substituted by a CLC island operating optimally for improving the gas turbine performance. The transient variations of the integrated plant in terms of power, temperature, and pressure profiles are presented.
The CLC-CC power plant was estimated to operate at an average efficiency of 48.2%. This efficiency was accomplished by optimizing operational strategy for fixed bed reactors and the utilization of the exhaust streams of oxidation and heat removal. Dynamic simulation of the integrated CLC-CC plant showed that the combined cycle is only slightly affected by the batch nature of operation of the fixed bed CLC reactors. The CLC-CC plant was estimated to generate a relatively flat power output, especially from its gas turbine. Future work will focus on plant simulation under part-load operation and simulation of the integrated CLC-CC plant subject to time-varying loads. The developed simulation environment paves the way for conducting real-time optimization of power cycles with high-efficiency CO2capture technologies.
This material is based upon work supported by the National Science Foundation under Grant No. 1054718.
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