Optimal operation of a large-scale packed bed chemical-looping combustion process using nonlinear model predictive control

In this work, a nonlinear model predictive control (NMPC) scheme is presented for chemical-looping combustion (CLC) in a large-scale packed bed reactor. For NMPC, the plant is represented by a multiscale model comprised of mass and energy balances for both the reactor and particles, with states dependent on their location within both the spatial and temporal domains. A kinetic scheme was chosen from existing literature such that it can accurately predict the process behaviour obtained from multiple sets of literature data, attained using reactors of different sizes operating under different inlet flowrates and temperatures. The multiscale model was used to simulate the plant behaviour, while a pseudo-homogeneous model was used as the internal NMPC model to reduce computational costs for implementation of feedback control. For the pseudo-homogeneous model, it was verified that the gradients within the particles could be assumed to be negligible, and the number of states is decreased by about 87% by focusing on the reactor behaviour. The resulting NMPC scheme is implemented for packed bed CLC and was performed for both the oxidation and reduction stages of CLC. For NMPC of the oxidation stage, manipulating both the inlet air and inert gas fluxes allowed the outlet gas temperature to track a given setpoint. In this case, the oxidation stage generated energy for three times as long as the case when a constant inlet air flux is used. When NMPC was implemented for the reduction stage, improved CO2 selectivity could be achieved by manipulating the inlet fuel flowrate. This improved the CO2 and H2O purity of the outlet stream by over 20% and thus improved the carbon capture effectiveness of the process.