Experimental Study And Numerical Simulation Of Supercritical Fluid Extraction

In this dissertation, the rules of influence of technical conditions on the supercritical extraction (SCFE) of soybean oils with C02 are researched based on experiments. According to the principle of mass conservation, the kinetic model of SCFE is set up founded on differential extraction, and the mass transfer in SCFE is analyzed. The artificial neural network is used to simulate the differential extraction.The paper is composed of three parts:(1).A lot of orthogonal and contrast experiments, with supercritical C02 as solvent and soybean as materiel, have been done to study the influence of pressure, temperature, fluid flow rate and the pretreatment of materiel on SCFE.(2). To avoid the disadvantage of the traditional methods, the mass transfer coefficient is calculated based on the results of differential extraction. The kinetic model of SCFE bases on differential extraction is established according to the principle of mass conservation. A integral bed is divided into some slices, and the process of integral bed extraction can be simulated based on the results of differential extraction. This is a primary research of scale-up design of SCFE. A visual simulation program of SCFE has been accomplished.(3).The BP artificial neural network is introduced to simulate the influence of pressure and temperature on differential extraction. The BP artificial neural network is trained with the results of differential extraction, and after being trained it is used to predict the differential extraction under other conditions. The effects of net structure and pretreatment of train data on train and prediction of neural network are investigated.