Abstract

Chemical looping technologies have been demonstrated to have favorable economy for CO2 separation and high exergy efficiency. In order to realize the energy cascade utilization, a hybrid system combing autothermal coal-fueled chemical looping combustion (CLC), chemical looping air separation (CLAS), supercritical CO2 Brayton cycle and transcritical CO2 cycle is constructed for high-efficiency power production and CO2 separation. The proposed hybrid system is first modeled and simulated under fundamental condition to investigate the basic performance and exergy efficiency of each component. Then the thermodynamic evaluations of the system performance are conducted under different conditions with five variables. Finally, using these simulation results as training samples, a neural network model (NNM) of the system is constructed to predict the power output under variable working parameters. As the energy is used in different subsystems according to its grade, the net power efficiency of this hybrid system can reach to a high value of 64.59%. The established NNM is accurate for power prediction under random conditions with correlation coefficient of 0.9931.

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