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A regularity has been found: a larger hydrophilic head gives rise to a lower interfacial tension. A relationship was found between the size of the hydrophilic head of lipid and the isoelectric point pH value. With larger hydrophilic heads, the isoelectric point appears at lower pH.

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Vol7_suppl_05_39.pdf

The interfacial tension (IFT) of hydrocarbon fluids is commonly predicted by either the parachor method or the scaling law. The methods require equilibrium liquid and vapor phase composition and density. An equation of state would normally be required if experimental values are not available. However, the computation of density for simple hydrocarbons and reservoir fluids, despite the important advances achieved by cubic equations of state, still remains a weak link in these types of calculations. Thus, there exists a need to investigate the qualitative and quantitative effects, of such inaccuracies in the density, on IFT predictions. Moreover, the study presented in this work would be useful in reservoir engineering and enhanced oil recovery calculations. The results presented in this work indicate that the methods are highly sensitive to the inaccuracies in the density of both the liquid and the vapor phases. An error of around 10% in the liquid or the vapor density can result in an error of up to 200% in the estimated IFT. Two binary and one ternary mixture for which measured data on IFT, composition and density is reported in the literature form the basis of this study.

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Using (1) solubility, (2) molecular weight, and (3) density, a three-layer feed-forward neural network was constructed and tested to predict the IFT at the crystal/solution interface. The concentration of solute in liquid phase, (1) concentration of solute in solid phase, (4) temperature, (3) density and (2) molecular weight of crystal were used as inputs to predict the interfacial tension at the crystal/liquid interface (σ_SL). The network was trained using the solubility information for 28 systems to predict the σ_SL value and was validated with 29 new systems. Despite the limited number of data used for training, the neural network was capable of predicting σ_SL successfully for the new inputs, which are kept unaware during the training process. The σ_SL value that is predicted by the artificial neural network during the training and testing process was compared with σ_SL predicted from the widely used empirical expression. For most of the systems, ANN better predicts IFT.

abstract here

The test procedures to assure ASTM biodiesel quality are not being widely implemented because of the lengthy procedures and laboratory equipment requirements. A critical need in the increasingly emerging biodiesel industry right now is a reliable, affordable and rapid test method for determining the blends of biodiesel in diesel fuel. As an effort to explore a reliable and rapid method, a spectrophotometer was used to scan the blends of #2 fossil diesel and biodiesel for spectrums in the wavelength range of 190-1100 nm.

The shape of the spectrum curve was found to be different for different biodiesel feedstock where as relative absorbance and characteristic peaks of absorbance curve was attenuated with increasing amount of diesel in the blend. Shape characteristics were fed into neural network to predict the biodiesel feedstock and blend level in biodiesel-diesel mixture.

http://asae.frymulti.com/abstract.asp?aid=19829&t=2