CO2 capture processes by carbonation-calcination cycles of CaO/CaCO3 were limited by the carbonation conversion and sorbents reutilization with the number of carbonation/calcinations cycles. In order to optimizing the CaO/CaCO3 cycles, BP neural network model and PID temperature control system were established based on the simulation of the process parameters and dynamic characteristics. The carbonization/calcination temperature, the mass fraction of additives for sorbents and calcination time were selected for the input conditions, while the output conditions were capture capacity and the reutilization of sorbents. Genetic algorithm(GA) model is established to optimize the PID controller's proportional coefficient kP, integral coefficient kI, and differential coefficient kD. The results indicated that BPNN coupled with PID model could form a complete optimization strategy for CO2 capture process by CaO/CaCO3 cycles. Keywords-CO2 capture; CaO/CaCO3 cycles; BPNN; GA; PID controller;