Analytical supercritical fluid extraction (SFE) was used to investigate the chemical constituents of Curcuma zedoaria, a very useful Chinese herbal medicine. The qualitative and quantitative analysis of extracts was performed by gas chromatography with mass spectrometry. The effects of pressure, temperature and flow rate of the extracting fluid on the extraction efficiency of SFE were investigated quantitatively. It was found that at a fixed density, changing temperature in a given experimental range had no great influence on the distribution of SFE products, but that an appropriate increase of flow rate and fluid volume improves the extraction efficiency. SFE using supercritical carbon dioxide was a more efficient extraction technique than steam distillation.

Many factors influence the wear of friction material performance such as formulation, manufacturing condition and operating regimes, and so on. In this paper, the wear rate variation has been modeled by means of artificial neural network, the network have been developed with all these relevant factors taking into consideration. 16 influence factors and wear rate selected as input and output respectly, 16 [10-8]2 1 is regarded as the best architecture of neural network, the Levenberg-Marquardt algorithm is used for training the network. The result shows that the model is valid to predict the wear property, as well as that it is useful for optimizing the formulation and manufacturing conditions, the relatively excellent combination of the ingredients and the appropriate manufacturing condition parameters can be obtained by this approach.