چكيده به لاتين
In recent years, extraction by supercritical fluids has been given more attention to extracting high-purity pharmaceuticals and nutrients at low temperatures, which tends to maintain the structure of these materials. One of the unique properties of supercritical fluids is the diffusion coefficient of these fluids. The diffusion coefficient of these fluids is higher than the liquids, so they have a higher liquid solubility than liquids, which increases the extraction efficiency. Also, the density of these fluids is generally higher than the density of gases.
Given the fact that the design and development of the extraction by supercritical fluids process is highly dependent on models and their proper prediction of the solubility of materials in supercritical fluids, In recent years, much attention has been paid to supercritical extraction process modeling. But the existing models have many weaknesses These include non-precision in the supercritical area, especially for pharmaceutical and polar materials. Also, many of the existing models are not usable for increasing scale due to the lack of attention to physical issues. Therefore, in this field, the equation of state is felt to be precise and reliable. Hence, in this research, we tried to use the equation of state Perturbed Hard-Sphere-Chain The prediction of the solubility of different materials in supercritical carbon dioxide and compare the results with experimental results and the results of other equations and the precision of this equation in the supercritical region.
Also, considering that the neural network is a suitable method for data interpolation. In this study, this method was also used to predict solubility in the supercritical region for existing data and the results of the state equation and neural network were compared.
