Computational particle fluid dynamics 3D simulation of the sorption-enhanced steam methane reforming process in a dual fluidized bed of bifunctional sorbent-catalyst particles

High-Purity Hydrogen Production by CO2 Addition for Sorption-Enhanced Steam Methane Reforming at a Temperature Below 600 °C

“…The y H 2 ,out curve can be divided into three periods: (i) The period of efficient CO 2 absorption, in which CO 2 is efficiently absorbed by CaO when the y H 2 ,out is high; (ii) the transition period, where the y H 2 ,out drastically decreases; and (iii) the low y H 2 ,out period, in which the sorbent loses the ability to absorb CO 2 . A similar pattern of y H 2 ,out curve for SE-SMR has been reported. − ,− Figure indicates the presence of a quasi-steady period from t = 0 s to a specific time point (∼450 s for 0% CO 2,in ). The duration of this quasi-steady-state period decreases as the CO 2 addition at the inlet increases.…”

“…SE-SMR uses solid sorbents to capture CO 2 in situ during the reforming process, leading to a substantial reduction in CO 2 emissions and a low CO concentration while producing high-purity hydrogen. Compared with traditional SMR, SE-SMR is operated at lower temperatures, typically ranging from 600 to 700 °C for CaO-based sorbents, − from 400 to 500 °C for MgO-based sorbents , and from 400 to 600 °C for hydrotalcite-based sorbents. − The SE-SMR process successfully produces high-purity hydrogen with a molar fraction (dry basis) of y H 2 ≥ 90% at the outlet.…”

“…They also showed that the lowest temperature for high-purity H 2 production ( y H 2 ,out ≥ 90%) was 550 °C for S/C = 4, and y H 2 ,out ≥ 90% was not obtained at 550 °C for S/C = 3 under the system parameters and operation conditions they used. A dynamic model for SE-SMR using CaO sorbent was used to evaluate the thermal behavior of the reforming step in a temperature swing reforming/regeneration process by Mostafa et al Di Nardo et al , numerically investigated the performances of SE-SMR using CaO sorbent in the fluidized bed reactors.…”

Artificial Intelligence for Hybrid Modeling in Fluid Catalytic Cracking (FCC)

“…This approach reduces computational effort while still accounting for catalyst-particle interactions. Moreover, since the CPFD method can work when using coarse meshes without loss of accuracy, long-time simulations are computationally affordable and relatively large time-steps can be adopted, which is not the case with conventional CFD methods [30].…”