Modeling of supercritical fluid extraction using an integrated soft computing and conventional approach

Supercritical fluid extraction is a separation technique in food and chemical industry, which exploits the solvent properties of fluids near the critical point. Modeling of the yield and solubility of biomaterials is an essential issue in supercritical fluid extraction. In this paper, an integrated approach using neural networks and conventional Peng-Robinson equation is proposed for the modeling of the relationship between pressure and yield of biomaterials. The results using various models are compared. It shows that the proposed integrated model is generally better than those using the conventional model. The effectiveness of the proposed approach is demonstrated by simulation and comparison studies.