(341b) Design of Experiments and Uncertainty Quantification for Adsorptive CO2 Capture Systems

Carbon dioxide capture plays an important role in reducing global CO₂ emissions and mitigating global warming effects. Although aqueous solutions of organic amines have been widely employed as adsorbents for CO₂, more energy efficient capture materials are needed. Amine-appended metal-organic frameworks (MOFs) have been investigated as a promising alternative with high thermal stabilities and lower regeneration energies[1]. Modeling and optimization are key strategies to gain insights of the carbon capture process, helping understand and design the process performance for accelerating and de-risking the research and development of CO₂ capture technologies. Experimental model validation is crucial in providing the base for reliable experiments with the model by determining the degree to which a model can accurately represent the real world, which helps decide complex tradeoffs in processes with large uncertainties. The Carbon Capture Simulation for Industry Impact (CCSI²) project is focused on accelerating the development of CO₂ capture technology guided by computational modeling, technoeconomic optimization, and uncertainty quantification [2]. Design of experiments (DoE) methodologies have been applied to several technologies as part of the CCSI² program, ultimately providing essential data to validate computational models and reduce uncertainty in techno economic assessments[3].

In this work, we perform design of experiments for a fixed-bed adsorptive CO₂ capture process to characterize transport properties of these MOFs. A partial differential algebraic equation model couples mass and momentum transport phenomena with adsorption equilibria (isotherms) and kinetics[4]. Specially, we perform least squares parameter estimation to infer a lumped transport rate constant and discern between possible isotherm models from both breakthrough and thermogravimetric analysis data[5]. Optimization problems are implemented in the open-source Pyomo modeling environment and solved using Ipopt. Partial differential equations are discretized in both time and space resulting in over 20,000 sparse algebraic constraints for parameter estimation and design of experiments optimization problems. Directly exploiting the exact first a second derivatives, the so-called “glass box” approach, yields computationally efficient solutions in less than a minute. The ultimate goal of design of experiments is to reduce uncertainty in the fundamental adsorptive models which are inputs for detailed process design and techno economic evaluation of novel CO₂ capture technologies such as rotary bed contactor designs[6].

Reference

[1]Milner, P. J., Siegelman, R. L., Forse, A. C., Gonzalez, M. I., RuncÌŒevski, T., Martell, J. D., ... & Long, J. R. (2017). A diaminopropane-appended metal–organic framework enabling efficient CO₂ capture from coal flue gas via a mixed adsorption mechanism. Journal of the American Chemical Society, 139(38), 13541-13553.

[2]Morgan, J. C., Chinen, A. S., Anderson-Cook, C., Tong, C., Carroll, J., Saha, C., ... & Miller, D. C. (2020). Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO₂ capture process. Applied Energy, 262, 114533.

[3]Soepyan, F. B., Anderson-Cook, C. M., Morgan, J. C., Tong, C. H., Bhattacharyya, D., Omell, B. P., ... & Kress, J. D. (2018). Sequential Design of Experiments to Maximize Learning from Carbon Capture Pilot Plant Testing. In Computer Aided Chemical Engineering (Vol. 44, pp. 283-288). Elsevier.

[4]Dowling, A. W., Vetukuri, S. R., & Biegler, L. T. (2012). Large‐scale optimization strategies for pressure swing adsorption cycle synthesis. AIChE journal, 58(12), 3777-3791.

[5]Cavenati, S., Grande, C. A., & Rodrigues, A. E. (2006). Separation of CH4/CO2/N2 mixtures by layered pressure swing adsorption for upgrade of natural gas. Chemical engineering science, 61(12), 3893-3906.

[6]Franceschini, G., & Macchietto, S. (2008). Model-based design of experiments for parameter precision: State of the art. Chemical Engineering Science, 63(19), 4846-4872.