Dynamic Optimization Applied for Modelling and Optimal Control of a Packed Bed Reactor for Chemical-Looping Combustion
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Chemical-looping combustion (CLC) has recently emerged as a promising technology capable of curbing CO2 emissions while also reducing the energy penalty entailed in carbon capture and sequestration (CCS). The novelty of CLC resides in its use of a metal oxide as an intermediate that serves the purpose of avoiding direct contact between fuel and air. The CLC process can be carried out in a packed bed reactor, in which the metal oxide supported in an inert material is intermittently exposed to both air and fuel streams. The oxidation stage produces a high temperature air stream that is used to feed a gas turbine and the reduction stage produces a highly concentrated CO2 stream suitable for sequestration. The transient operation of the system is complex and temperature fluctuations and unconverted fuel at the reactor’s exit is expected during the oxidation and reduction stages. To the author’s knowledge, a study that specifies optimal control strategies focused on increasing the efficiency of every stage in the CLC PBR cycle is in critical need to advance this emerging technology. The aim of this study is to adapt an existing 1-D mechanistic heterogeneous dynamic model, which considers mass and heat transport resistances in the particle (metal oxide and support) and the bulk fluid phases. The non-linear model is subject to validation against published data and a sensitivity analysis on key parameters during both reaction stages. Later, each reaction-stage simulation is formulated as an optimal control dynamic optimization problem that is solved using the direct transcription approach. The optimization results show improvements in the heat recovery process during the oxidation stage and a considerable reduction in fuel slip during the reduction stage, effectively producing more CO2. Moreover, based on the outcome of the sensitivity analysis, an optimistic and a worst-case scenario are considered. The dynamic optimization of the optimistic case shows even greater improvements in energy production during the oxidation stage and the results from the worst-case shows that a 97% rate of fuel conversion can be achieved within the reactor.
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Marco Antonio Lucio Hernandez (2019). Dynamic Optimization Applied for Modelling and Optimal Control of a Packed Bed Reactor for Chemical-Looping Combustion. UWSpace. http://hdl.handle.net/10012/14951