Experimental Study on the Throttling Effect of SC-CO 2 Containing Ethanol System Flowing Through the Coaxial Annular Nozzle and the Prediction Based on Artificial Neural Network

Supercritical fluid process has been used in several industrial fields as a novel technique to produce nanoparticles. Throttling effect may occur when supercritical CO 2 and ethanol passes through a coaxial annular nozzle together in supercritical antisolvent, exerting considerable negative effects on particle size and morphology, thus, it is imperative to study the throttling effect. A new experimental system was developed to study the effects of inlet temperature, inlet pressure and ethanol content on the throttling effect of supercritical CO 2 system using a 100 μm diameter coaxial annular nozzle in this paper. Supercritical CO 2 and desired amount of ethanol were mixed in the coaxial annular nozzle and the temperature and pressure at the inlet and outlet of the nozzle were recorded by the data acquisition system. The results show that high inlet temperature and ethanol content can acquire higher throttling temperature while high inlet pressure enhances the throttling effect, obtaining a lower throttling temperature. The initial density and phase state were confirmed to be the key factors to affect the throttling effect. In order to accurately predict the throttling effect, a back-propagation neural network model with the Correlation Coefficient of 0.99 531 and the Mean Retive Error ranging from 1.0841 % to 1.3209 % was proposed based on the experimental data, which demonstrated that it can be used as a powerful tool to predict the throttling effect of supercritical CO 2 containing ethanol system.