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Biodiesel plays an important role in reducing the dependency of petroleum fuels and reduce environmental pollution. Biodiesel has attracted attention as a renewable, non-toxic, and biodegradable fuel. In the past years, researchers have expanded their work to new methods of obtaining biodiesel. In this work, biodiesel production using soybean oil from the industry is described. Biodiesel was further analyzed and compared with the EN biodiesel specifications. The characterization of biodiesel was performed in order to obtain density, viscosity and flash point. Moreover, the study was focused in optimized the biodiesel yield, obtain from soybean oil using Artificial Neural Networks (ANN). The variable parameters were molar ratio between methanol and oil, reaction temperature and catalyst quantity. The paper concludes that the ANN can be successfully used to optimize the biodiesel yield.

Lubricants are influenced over their lifetimes by various factors such as temperature, oxygen, water contamination, etc. which affect the chemical structure and implicitly their specific characteristics. If the degradation of the oils is significant and the characteristic values exceed certain limits that are defined for the safe operation, the lubricants should be replaced. The main purpose of the present study is to offer an efficient and predictive method for an adequate quality control of lubricant oils in service and implicitly for their adequate and safe operation. The method developed in this study consists in a numerical algorithm (the multiple regression) and is based on the monitoring of lubricant oils� representative characteristics in time and allows estimating the evolution of the oils characteristics during the service period as well as the prediction of the life-time of the oils.

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Liquid -liquid equilibrium at temperatures between 293.16K and 353.1K for the mixture of 2,2, propoxy] -1-propanol was determined using the cloud point method. The measured data was used to estimate the binary interaction parameters of NRTL thermodynamic model, through non-linear regression using MATLAB® software. The binary interaction parameters resulting from regression were used further in a chemical simulation software (PRO/II 9.3) to determine the LLE for the studied mixture. The LLE calculation results obtained with the NRTL model were compared with the results of LLE calculations using the predictive thermodynamic model-UNIFAC. It was determined that the results of the calculation of the LLE using binary interaction parameters obtained through regression have a smaller deviation from the experimental data than the results of the calculation performed using the UNIFAC model. Moreover, the binary interaction parameters obtained from regression were utilized for the estimation of the solvency properties of tripropylene glycol considering the extraction of C8 aromatics from a mixture containing 2,2,4-trimethyl pentane, ethylbenzene and xylenes.