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. (28th April 2022)

• Record Type: Journal Article
• Authors: Ostace, Anca; Chen, Yu-Yen; Parker, Robert; Mebane, David S.; Okoli, Chinedu O.; Lee, Andrew; Tong, Andrew; Fan, Liang-Shih; Biegler, Lorenz T.; Burgard, Anthony P.; Miller, David C.; Bhattacharyya, Debangsu
• Is Part Of: Chemical engineering science. Volume 252(2022)
• Publication Date: 2022-04-28
• Subjects: Uncertainty quantification -- Model structure uncertainty -- Parameter uncertainty -- Bayesian calibration -- Bayesian model building -- Chemical looping -- Oxygen carrier; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2022.117512
• Languages: English
• ISSNs: 0009-2509

Abstract

Highlights: Kinetic models for reduction of Fe-based oxygen carrier with CH4, CO, and H2 developed. Uncertainties quantified using fully Bayesian approach. Bayesian smoothing splines used for representing model uncertainties. Full posterior probability distribution of the parameters obtained using MCMC. Models calibrated and validated using thermogravimetric data. Abstract: 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 H2, 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.