Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

Abstract

Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying H ∞ design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.

Machine Learning for Chemical Looping: Recent Advances and Prospects

“…ML has been applied to the chemical looping and related fields with positive results. Supervised learning is the primary method used in this field, with representative models including k -nearest neighbors (KNN), , support vector regression (SVR), , decision tree (DT), , and artificial neural network (ANN). − Table summarizes the predictive performance of these models in chemical looping. The prediction accuracy of the model will vary significantly with different sample sizes and feature dimensions.…”

Effects of orientation and structure on solar radiation interception in Chinese solar greenhouse

“…The transmissivity of the lighting roof is one of the factors that influence the amount of solar energy captured in CSG. The solar energy captured by the wall and ground can be calculated by Eqs 15 – 18 , respectively [ 18 ]. Where τ D is the direct light transmittance; τ S stands for the diffuse light transmittance.…”

“…The transmissivity of the lighting roof is one of the factors that influence the amount of solar energy captured in CSG. The solar energy captured by the wall and ground can be calculated by Eqs 15-18, respectively [18]. PLOS ONE r 1 , r 2 , and r 3 refer to the light transmission loss caused by the opaque materials, aging of plastic film, dust and water droplets, respectively.…”

“…Furthermore, it was found that the ridge height and the horizontal projection of the rear roof are the major structural parameters in the structure of CSG affecting the solar energy capture. But this model could not optimize the orientation [ 18 ]. After synthetically analyzing the influence of different orientations and structures on capturing solar energy and saving heating cost [ 19 ], it can be found that the E-W orientation of a greenhouse and elliptically curved surface aspect ratio equal to 4 was most suitable for a northern tropical region.…”

“…Moreover, there are great differences between Chinese solar greenhouses and foreign domed greenhouses. The structure of the CSG is more complex, which is composed of the lighting roof, wall, roof, and sidewalls [ 18 ]. All of these are factors that affect the achievement of maximum solar radiation interception and enhancement of the nighttime temperature in CSG.…”