(328c) Data-Driven Model Building of Zeolite Adsorption Processes with Uncertainty Quantification and Propagation to Dynamic Simulations of CO2 Adsorption
Post-combustion CO2 capture is a technology for which the interest in up-scaling for commercial applications has gained significant momentum of late . In this study, a rigorous complete dynamic mathematical model of a fixed bed adsorber for CO2 adsorption onto NaX zeolite (also known as zeolite 13X) is developed and implemented in gPROMS®. The small-scale adsorption model that governs the dynamic phase equilibrium between the gas and the solid phase is constructed from adsorption isotherm and calorimetry data using a model form based on the Langmuir isotherm equipped with Gaussian process stochastic functions. The Bayesian approach to model construction and calibration quantifies uncertainty in the adsorption model. The physical adsorption model is then implemented into the process-scale fixed bed adsorber model, thus propagating uncertainty to the dynamic, non-isothermal fixed bed adsorber model.
To save computational time, two simplified mathematical models of the fixed bed adsorber are formulated. From a deterministic perspective, the simplified models are shown to be in close agreement with the complete model. In order to evaluate whether the simplified models can be used as substitutes of the complete model for uncertainty propagation, an ANOVA analysis is conducted at a significance level of 0.05, followed by linear contrast analysis to test specific hypothesis related to the output distributions of the three models.
A number of case studies are performed and for each case, the uncertainty is quantified in output variables of interest â namely, CO2 breakthrough times and concentration and temperature profiles. The case studies show that the uncertainty inherent to the physical adsorption model scales up differently depending on the operating conditions. Some operating conditions give rise to increased process-scale uncertainty, while others to decreased uncertainty. An additional case study demonstrates that accounting for model form discrepancy at the small scale (in contrast with accounting for parameter uncertainty only) significantly reduces the uncertainty in the process-scale variables.
- Bhat K. S., Mebane D. S., Storlie C. B., and P. Mahapatra, âUpscaling Uncertainty with Dynamic Discrepancy for a Multi-scale Carbon Capture Systemâ. Journal of the American Statistical Association, 2017.
- Kalyanaraman J., Kawajiri Y., Lively R. P., and M. J. Realff. âUncertainty Quantification via Bayesian Inference Using Sequential Monte Carlo Methods for CO2 Adsorption Processâ. AIChE Journal, 2016, 62 (9) pp. 3352-3368.
- Kennedy M. C., and A. OâHagan, âBayesian calibration of computer modelsâ, Journal of the Royal Statistical Society Series B, 2001, 63 (3) pp. 425-464.Â
- Miller D. C., Syamlal M., Mebane D. S., Storlie C., Bhattacharyya D., Sahinidis N. V., Agarwal D., Tong C., Zitney S. E., Sarkar A., Sun X., Sundaresan S., Ryan E., Engel D., and C. Dale (), âCarbon Capture Simulation Initiative: A Case Study in Multiscale Modeling and New Challengesâ, Annual Review of Chemical and Biomolecular Engineering, 2014, 5 pp. 301-23.
- Miller D. C., Agarwal D. A., and C. Tong, âCCSI and the role of advanced computing in accelerating the commercial deployment of carbon capture systemsâ, SciDAC Conference, June 2011.