Advancements in molecular modeling have made it possible to synthesize hypothetical materials and predict the adsorption behaviour of gases. Hence some large databases containing hundreds of thousands of such materials have been reported. Screening such large databases to find suitable candidates can be a challenge. Recent studies have shown that adsorption metrics, such as selectivity, working capacity are not good predictors of the ability of an adsorbent's performance when subjected to a process [1,2]. In this work, a simplified proxy-model called Batch adsorber analogue model (BAAM) is introduced . In this model, the adsorption column is represented as a well mixed batch adsorber with the adsorbent and the fluid phase in equilibrium. This assumption reduces the system of partial differential equations to a system of two ordinary differential equations, thereby reducing the computational complexity. A 4-step cycle with LPP, which is shown to provide a low parasitic energy is mimicked using this simplified model. Owing to the assumptions made in the model development, the BAAM does not provide an accurate representation of the key performance indicators, e.g. Purity, recovery and energy consumption. However, this restriction was overcome by calibrating the the model's output against results from detailed models. This procedure allows computing the Purity, recovery and energy consumption for the concentration of CO2 from a mixture of CO2 and N2. It is shown that the BAAM can predict the ability of an adsorbent to satisfy US-DOE capture requirements (95% CO2 purity with 90% recovery) for 83% of the adsorbents and the energy consumption can be estimated within 15% accuracy. Finally, the BAAM also results in a simple graphical approach
Using the BAAM, two databases were screened. The first one, the NIST/ARPA-E database containing experimental isotherms and the Carbon capture material database (CCMDB) containing over 100,000 hypothetical Zeolites and ZIFs. The top performing materials from each of these database were further studied using detailed process models combined with optimization algorithms which show an excellent reliability of the BAAM. The screening of such a large database also provides key insight about the ranges of properties for effective adsorbents.
1. Rajagopalan, A. K.; Avila, A. M.; Rajendran, A. Do adsorbent screening metrics predict process performance? A process optimization based study for post-combustion capture of CO2. Int. J. Greenhouse Gas Control 2016, 46, 76â85.
2. Farmahini, A.H., Krishnamurthy, S., Friedrich, D., Brandani, S., Sark- isov, L.: From crystal to adsorption column: challenges in multi- scale computational screening of materials for adsorption separation processes. Ind. Eng. Chem. Res. 57, 15491â15511 (2018)
3. Subramanian Balashankar, V., Rajagopalan, A. K., de Pauw, R., Avila, A. M., & Rajendran, A. (2019). Analysis of a Batch Adsorber Analogue for Rapid Screening of Adsorbents for Postcombustion CO2 Capture. Ind. Eng. Chem. Res. 58, 3314â3328 (2019)