| Authors | Seyyed majid Malek Jafarian,vahid gholami |
| Journal | Gas Science and Engineering |
| Page number | 0-0 |
| Paper Type | Full Paper |
| Published At | 2025 |
| Journal Type | Typographic |
| Journal Country | Netherlands |
| Journal Index | JCR،Scopus |
Abstract
Due to the direct relationship between the global warming phenomenon and the atmospheric
concentration of carbon dioxide, scientists are working on new gas purification methods for carbon
capture and storage devices (CCS). Direct mass separation of this gas from the air is hard in the typical
methods, due to the need to reduce the temperature of the to the freezing point, and not suitable for
large-scale use. Ranque-Hilsch vortex tube (RHVT) has the potential to reduce the air temperature near
the freezing point of carbon dioxide. Most optimization is done on commercial ones that are smaller
than the size needed to provide the air volume for large-scale direct mass separation of . The optimized
geometric and operating conditions of the device change with size, the present work aims to optimize
the larger vortex tubes for this purpose. The genetic algorithm (GA) coupled with an artificial neural
network (ANN) was used to perform the optimization from the numerical simulation data. After
validating the result with experimental works, the effective parameters on the thermal separation of
the vortex tubes, including inlet pressure, cold mass fraction, length to diameter, and cold outlet orifice
diameter to the tube diameter, were optimized to achieve the temperature separation about . The
findings contribute to a deeper understanding of mass separation phenomenon in vortex tubes and the
feasibility, scalability of mass separation by RHVTs in CCS methods.
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