Experiments and Numerical Simulations of Supercritical Fluid Extraction for Hippophae rhamnoides L Seed Oil Based on Artificial Neural Networks

Abstract

In this paper, a supercritical fluid extraction setup with an extraction volume of one liter was established with which Hippophae rhamnoides L seed oil was extracted using supercritical CO 2 . The experiments show that many factors have an impact on the oil yield, such as extraction pressure, temperature, and fluid flow rate, as well as seed particle size and filling quantity. For the extraction process of H. rhamnoides L seed oil, the recommended conditions were as follows: extraction pressure of 20-30 MPa, extraction temperature of 35-40°C, supercritical CO 2 flow rate of 0.15-0.3 m 3 /h, and extraction time of 4-5 h. Under such conditions, the oil obtained is very lucid and of good quality, and the yield can be greater than 90%. Gas chromatography analysis shows that the oil contains 12.3% saturated fatty acid and 87.7% unsaturated fatty acid. From the changes of oil yield with the extraction time, it can be concluded that the extraction process contains three stages: the fast extraction (line), transitional, and slow extraction stages. At the first stage, 75-80% of the oil has been extracted. On the basis of the experimental results, artificial neural network technology was applied to the simulation of the supercritical fluid extraction of vegetable oil. With a three-layer back-propagation network structure, the operation factors, such as pressure, temperature, and extraction time, were used as input variables for the network, and the oil yield was used as the output value. In the optimization of the topological structure of the net, six neurals from the middle hidden layer have been proven to be the optimum value, according to the minimum training and running time. With the normalization pretreatment of the initial input data, not only the convergence speed and accuracy has been improved greatly but also the problem of the derivative at zero has been solved. Therefore, the method is better than that of Fullana. On the basis of simplification of the extraction process, taking account of axial dispersion, a differential mass balance kinetic model has been proposed. With Matlab software as platform, an artificial neural networks-supercritical fluid extraction simulation system has been programmed. For the first time, the simulation for the supercritical fluid extraction process of H. rhamnoides L seed oil has been made, and the results show that the average absolute relative deviation is lower than 6%.

Optimization of supercritical carbon dioxide extraction of sea buckthorn (Hippophaë thamnoides L.) oil using response surface methodology

“…Thus, the predicted recovery rates for oil, vitamin E and carotenoids by supercritical CO 2 extraction under optimum conditions were 96.5%, 125.6% and 98.7%. Oil recoveries of 65-90% from the seed and pressed pulp of sea buckthorn by SC-CO 2 extraction have been reported (Yakilimishen et al, 2005;Yin, Xu, Wei, & Wang, 2005), which were lower than the predicted recovery under optimum conditions in the present study. The greater than 100% recovery for vitamin E by supercritical CO 2 extraction is likely due to the heatinduced loss of vitamin E occurred during the Soxhlet extraction which was conducted at 70 1C.…”

Supercritical extraction of borage seed oil coupled to conventional solvent extraction of antioxidants

“…A similar effect was previously reported for borage oil [4], and these authors found that, when operating at pressures under 200 bar, the extraction yield was higher at 283 K than at 313 and 333 K. Kotnik et al [2] found the same behavior for borage seeds when the extraction was carried out at 313 K and 200 bar; Roy et al [13] reported a decrease in the sunflower oil extraction yield with an increase in temperature at 200 bar, but at higher pressures (300, 400 bar) the extraction yield increased with increasing temperature. A decrease in the extraction yield with increasing temperature at lower pressures was also reported for the oil extraction from Hippophae rhamnoides [14], okra [15], and fennel [16] seeds. At lower pres- sures, lower temperatures are desirable since the change of solvent density with temperature is more marked than the change in the solute vapor pressure.…”

AN OVERVIEW OF THE Gynura procumbens LEAVES EXTRACTION AND POTENTIAL OF HYBRID PREDICTIVE TOOLS APPLICATION FOR PREDICTION AND SIMULATION IN SUPERCRITICAL FLUID EXTRACTION

“…There are various types of learning methods for neural networks, whereby one of the most applied methods is the backpropagation learning rule. Neural networks have been applied to several SFE processes in predicting the yield of extraction [46][47][48][49][50][51] , initial slope 49 , and solubility [52][53][54] . Table-2 lists some of the works that applied ANNs in the SFE of plants.…”