Extração do óleo e caracterização dos resíduos da borra de petróleo para fins de reuso

The petroleum industry, in consequence of an intense activity of exploration and production, is responsible by great part of the generation of residues, which are considered toxic and pollutants to the environment. Among these, the oil sludge is found produced during the production, transportation and refine phases. This work had the purpose to develop a process to recovery the oil present in oil sludge, in order to use the recovered oil as fuel or return it to the refining plant. From the preliminary tests, were identified the most important independent variables, like: temperature, contact time, solvents and acid volumes. Initially, a series of parameters to characterize the oil sludge was determined to characterize its. A special extractor was projected to work with oily waste. Two experimental designs were applied: fractional factorial and Doehlert. The tests were carried out in batch process to the conditions of the experimental designs applied. The efficiency obtained in the oil extraction process was 70%, in average. Oil sludge is composed of 36,2% of oil, 16,8% of ash, 40% of water and 7% of volatile constituents. However, the statistical analysis showed that the quadratic model was not well fitted to the process with a relative low determination coefficient (60,6%). This occurred due to the complexity of the oil sludge. To obtain a model able to represent the experiments, the mathematical model was used, the so called artificial neural networks (RNA), which was generated, initially, with 2, 4, 5, 6, 7 and 8 neurons in the hidden layer, 64 experimental results and 10000 presentations (interactions). Lesser dispersions were verified between the experimental and calculated values using 4 neurons, regarding the proportion of experimental points and estimated parameters. The analysis of the average deviations of the test divided by the respective training showed up that 2150 presentations resulted in the best value parameters. For the new model, the determination coefficient was 87,5%, which is quite satisfactory for the studied system

Caracterização físico-química, reológica e sensorial de iogurte obtido pela mistura dos leites bubalino e caprino

Milk from different animals can be used for dairy production. Yoghurt is a popular fermented milk product and considered to be one of the greatest importance in terms of consumer acceptance and consumption. The present research deals with the production of strawberry set-type yoghurt by mixing goat and buffalo s milk and it has the objective of taking advantage of the intrinsic characteristics of each milk to produce a final product with desirable attributes. It was conducted by analyzing five experimental groups with different proportions of goat and buffalo s milk: C 100% goat s milk; 7C3B - 70% goat s milk and 30% buffalo s milk, 5C5B - 50% goat s milk and 50% buffalo s milk, 3C7B 30% goat s milk and 70% buffalo s milk; B - 100% buffalo s milk. Each group was evaluated for total solids content and the acidification profile was monitored every 30 minutes by pH analysis. The yoghurt samples were analyzed for physical-chemical (pH, acidity, protein, fat, total and reducing sugars, ash and total solids), rheological (syneresis and viscosity) and sensory characteristics (appearance, odor, consistency and flavour). Samples with higher percentual of bubaline milk reached Vm faster, but the time necessary for pH 4.6 (Te) were similar between groups. Statistical differences (p<0.05) were observed for fat and total solids content of yoghurt, with superior values for groups higher proportions of buffalo s milk. The parameters of behavior reached by the model of Ostwald of Waale pointed yoghurt samples as non-Newtonian and pseudoplastic fluids. Yoghurt made only with goat s milk (C) had higher values (p<0.05) for syneresis, which can be explained by its fragile coagulum. Additionally, this group also had the lowest sensory scores for the attributes consistence and taste, while bubaline yoghurt (B) obtained the best acceptance indexes for all of the appraised parameters