Kinetic model development and Bayesian uncertainty quantification for the complete reduction of Fe-based oxygen carriers with CH4, CO, and H2 for chemical looping combustion

Three kinetic models are developed and calibrated for the complete multi-step reduction of an Fe-based oxygen carrier (OC) particle with CH4, CO, and H-2, using data from thermogravimetric analysis. The complete reduction rate profiles exhibit complex dynamics whose trajectory is significantly different depending on the reducing gas. A Bayesian model building and parameter estimation framework is applied for simultaneous parameter and model structure uncertainty quantification. The final models show excellent agreement between model predictions and calibration data, as well as new data not used for calibration (for the reduction of the OC with HC4). Parameter uncertainty is quantified by determining their joint posterior distribution, and model structure uncertainty is addressed by incorporating Gaussian process stochastic functions (represented by Bayesian smoothing splines) into the kinetic models. The final kinetic models with discrepancy functions are readily employable in equation-oriented simulation and optimization platforms. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Three kinetic models were developed and calibrated for the complete multi-step reduction of an Fe-based oxygen carrier particle with CH4, CO, and H-2. Bayesian model building and parameter estimation framework were applied to quantify parameter and model structure uncertainty simultaneously. The final kinetic models showed excellent agreement between model predictions and calibration data, as well as new data not used for calibration.