Wavelet multiresolution model based generalized predictive control for Hybrid Combustion-Gasification Chemical Looping process

Chemical looping (CL) process is a novel technology that separates oxygen from nitrogen to facilitate carbon dioxide capture in the design of clean coal power plants. The process, based on the multi-phase gas-solid flow, has an extremely challenging nonlinear multi-scale dynamics with jumps, rendering traditional robust control techniques, such as switching H-infinity design, difficult to apply and marginally successful. In an effort to model and control such a complex system, we present a generalized predictive control (GPC) scheme based on multiresolution wavelet model structure that characterizes well the nonlinear dynamics of single loop gas/solid flow. The NARX model, nonlinear in the wavelet basis, but linear in parameters, is used for the online chemical looping process identification. The control inputs and wavelet model parameters are calculated by optimizing the cost function using a gradient descent method. The convergence of the proposed GPC scheme is derived using Lyapunov function. Experimental results are provided to demonstrate the effectiveness of the proposed control strategy.