(371a) Guide to COF Adsorbent for Ammonia-based Green Hydrogen with Multi-scale Evaluation Approach

Since green ammonia is holding the global spotlight as a transportation medium of carbon-free H₂, there has been active discussion on various separation technologies for H₂ purification after the NH₃ decomposition [1]. One of the H₂ purification technologies with the highest technology readiness level (TRL) is pressure swing adsorption (PSA) processes, widely used in various industries [2]. In the PSA processes, adsorbents are crucial parts where H₂ and other gases are adsorbed and desorbed selectively. Thus, the most significant improvement can be made by selecting the adsorbents with the best properties [3]. Despite the importance of adsorbent selection, the discovery of best-performing adsorbents has been barely attempted considering the circumstance where the H₂ is provided from NH₃ decomposition.

As the first attempt of the adsorbent discovery for such green H₂ separation, in this study, we explore 648 covalent organic frameworks (COFs) because COFs have high potential as adsorbents in terms of their mechanical and chemical durability [4]. In order for the practical and efficient evaluation of the adsorbents, we propose a multi-scale adsorbent screening approach, which includes both molecular and process scale evaluation. In the molecular scale evaluation, the adsorption isotherm curves of the COFs are calculated based on the grand canonical Monte Carlo (GCMC) simulations. The calculated isotherm curves, in the process scale evaluation, are employed to estimate the H₂ recovery and H₂ purity of the PSA process as performance indicators. Based on the performance indicators, we could provide a practical guide to adsorbent selection and development for green H₂ separation, where the 10 best COFs both for H₂ raffinate and N₂ raffinate cases were suggested.

Moreover, as a part of the multi-scale screening approach, we also propose a computation strategy that reduces the computational cost significantly in GCMC simulations. Based on the initial GCMC simulation results, the computation strategy determines the minimum number of GCMC simulations for adsorbent evaluation. By decreasing the number of simulations to the minimum numbers, the computation strategy could reduce at least 15 % of computational cost for our COF screening cases. For the finally selected COFs based on the performance indicators, the maximum value of normalized root mean square error (NRMSE) of all those adsorbents was smaller than 2.73 %, which barely deteriorates the entire adsorbent evaluation accuracy.

Literature cited:

[1] K.E. Lamb, M.D. Dolan, D.F. Kennedy, Ammonia for hydrogen storage; A review of catalytic ammonia decomposition and hydrogen separation and purification, International Journal of Hydrogen Energy. 44 (2019) 3580–3593. https://doi.org/10.1016/j.ijhydene.2018.12.024.

[2] A.M. Banu, D. Friedrich, S. Brandani, T. Düren, A multiscale study of MOFs as adsorbents in H2 PSA purification, Industrial and Engineering Chemistry Research. 52 (2013) 9946–9957. https://doi.org/10.1021/ie4011035.

[3] J.A. Delgado, V.I. Águeda, M.A. Uguina, P. Brea, C.A. Grande, Comparison and evaluation of agglomerated MOFs in biohydrogen purification by means of pressure swing adsorption (PSA), Chemical Engineering Journal. 326 (2017) 117–129. https://doi.org/10.1016/j.cej.2017.05.144.

[4] G.O. Aksu, I. Erucar, Z.P. Haslak, S. Keskin, Accelerating discovery of COFs for CO2 capture and H2 purification using structurally guided computational screening, Chemical Engineering Journal. 427 (2022) 131574. https://doi.org/10.1016/j.cej.2021.131574.