Optimization of the Supercritical Carbon Dioxide Separation of Bergapten from Bergamot Essential Oil

The possibility of following traditional cold-press extraction with the post process continuous separation of bergapten from bergamot essential oil was investigated. A fractionation tower was used in an experiment in which cold-pressed bergamot oil was extracted in a continuous countercurrent process by supercritical carbon dioxide under different conditions. Bergapten is fairly soluble in CO2 in its supercritical phase, in particular at a density of 277.90 kg⋅m–3, corresponding to a pressure of 8 MPa and temperature of 40°C. Under these conditions, an extract with 0.198% bergapten was obtained, a figure slightly below the percentage of bergapten contained in cold-pressed oil (0.21%). However, at densities below 200 kg⋅m–3, the amount of bergapten in the extracted oil was negligible. Of all tested conditions for separation, the best was found to be at a pressure of 8 MPa and temperature of 70°C, conditions under which bergapten was not detected. The results of the experiment showed that bergapten, and the non-volatile fraction in general, was extracted only in small quantities and was not extracted at all with at a CO2 pressure of 8 MPa.

BP Neural Network Application Research in Petrochemical Tower System Corrosion Prediction

This paper takes the monitoring data sample from the top of fractionation tower system of one petrochemical company and uses prediction model which is constructed by BP neural network to study the corrosion prediction of catalytic fractionation tower top system. It uses min-max and z-score standardized method to deal with the original data and compare the impacts. The result shows that the BP neural constructing prediction model can provide basis of corrosion control for refinery. It also shows that better accuracy can be achieved by using min-max standardized method and when the number of training data quantity is over 20, the prediction result is more accurate and stable.