Optimization of large-scale Ranque-Hilsch vortex tubes for enhanced CO2 separation in carbon capture and storage

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 ${CO}_2$ 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 ${CO}_2$. 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 $60.5°C$. 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.