(377f) The Pore Space of Packed Bed Reactors: From Characterization to Improvements

Packed bed reactors have been commonly used in the continuous synthesis of chemicals for many decades now [1, 2]. While established, these systems suffer from severe limitations obstructing their overall efficiency. The most notable issues are mass and heat transfer inefficiency, both leading to reduced yields of the desired product, high-pressure drops, and intense heat transfer with the cooling media required for the reactor to achieve optimal operation. Improvements in yield and energy uptake in these systems would have a substantial impact on both economy and sustainability as packed bed reactors are the systems behind some of the most massive chemical processes in the World, such as ammonia synthesis [3]. There are, unsurprisingly, many ongoing efforts to intensify and modernize these reactors [4].

Computational simulations represent powerful means to study packed bed reactors, devise, and test strategies for their improvements [5, 6]. A particularly important class of such reactors consists of randomly distributed porous pellets loaded with catalysts [1-4]. The spatial distribution of these pellets and their own pore structure are detrimental to the heat and mass transfer across the reactor. Thus, knowledge of the exact structure of the bed would benefit both computational and experimental researchers. While many models successfully simulate these systems without this information, pore-scale models have their unique advantages and capability to contribute to these ongoing improvement efforts [6]. At the same time, obtaining detailed information on the internal structure of a packed bed, and any porous material, requires the use of costly and not readily available techniques [6, 7].

In this work, we propose a method of determining the internal structure of a packed bed from inexpensive and easily obtainable laboratory and real-time process metrics. In particular, we use the average, bulk void fraction, hydraulic permeability, known pellet size, inlet and outlet feed temperatures, and product yields to obtain a statistically accurate description of the reactor structure. We use a combination of direct numerical simulations and an optimization algorithm to obtain an approximation of the reactor with spherical pellets that gives us the distribution of pore sizes, pore conductivities, and the fraction of open vs. closed pores. Equipped with this knowledge, we demonstrate potential avenues for process improvement that would not be accessible without a suitable representation of the pore space.

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[3] Zhou, D., Zhou, R., Zhou, R., Liu, B., Zhang, T., Xian, Y., Cullen, P.J., Lu, X. and Ostrikov, K.K., 2021. Sustainable ammonia production by non-thermal plasmas: Status, mechanisms, and opportunities. Chemical Engineering Journal, 421, p.129544.

[4] Haase, S., Tolvanen, P. and Russo, V., 2022. Process intensification in chemical reaction engineering. Processes, 10(1), p.99.

[5] Stegehake, C., Riese, J. and Grünewald, M., 2019. Modeling and Validating Fixed‐Bed Reactors: A State‐of‐the‐Art Review. ChemBioEng Reviews, 6(2), pp.28-44.

[6] Jurtz, N., Kraume, M. and Wehinger, G.D., 2019. Advances in fixed-bed reactor modeling using particle-resolved computational fluid dynamics (CFD). Reviews in Chemical Engineering, 35(2), pp.139-190.

[7] Chen, L., He, A., Zhao, J., Kang, Q., Li, Z.Y., Carmeliet, J., Shikazono, N. and Tao, W.Q., 2022. Pore-scale modeling of complex transport phenomena in porous media. Progress in Energy and Combustion Science, 88, p.100968.