Avaliação do petróleo por termogravimetria para simulação de curva PEV, fator de caracterização e resíduo de carbono

Petroleum evaluation is analyze it using different methodologies, following international standards to know their chemical and physicochemical properties, contaminant levels, composition and especially their ability to generate derivatives. Many of these analyzes consuming a lot of time, large amount of samples , supplies and need an organized transportation logistics, schedule and professionals involved. Looking for alternatives that optimize the evaluation and enable the use of new technologies, seven samples of different centrifuged Brazilian oils previously characterized by Petrobras were analyzed by thermogravimetry in 25-900° C range using heating rates of 05, 10 and 20ºC per minute. With experimental data obtained, characterizations correlations were performed and provided: generation of true boiling point curves (TBP) simulated; comparing fractions generated with appropriate cut standard in temperature ranges; an approach to obtain Watson characterization factor; and compare micro carbon residue formed. The results showed a good chance of reproducing simulated TBP curve from thermogravimetry taking into account the composition, density and other oil properties. Proposed correlations for experimental characterization factor and carbon residue followed Petrobras characterizations, showing that thermogravimetry can be used as a tool on oil evaluation, because your quick analysis, accuracy, and requires a minimum number of samples and consumables

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