Application of Hybrid Predictive Tools for Prediction and Simulation in Supercritical Fluid Extraction – An Overview

Abstract

Abstract Supercritical fluid technology (SFT) has been applied in many areas, such as in pharmaceutical and food sectors, due to its outstanding features. SFT is an efficient technology that performs extraction and leaves no or less organic residues compared to conventional processes. Recently, the simulation and prediction of the process output from supercritical fluid extraction was determined using intelligent system predictive tools. The prediction of the set of results from supercritical fluid extraction for designing and scale up purposes is crucial because it can not only reduce the usage of extraction solvent and the energy and time of the process but it can also solve the problem that the complex mathematical model cannot solve. A neural network is considered as one of the artificial intelligent systems and is a key technology in industry 4.0. The use of hybrid predictive tools is also a developing area in the prediction and simulation of supercritical fluid extraction and therefore will be further discussed in this paper.