Optimization Model and PID Temperature Control System Design for CO2 Capture Process by CaO Carbonation-CaCO3 Calcination Cycles

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 k_p, integral coefficient k₁, 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.