Estudo cinético da degradação térmica e catalítica de petróleo pesado usando Al-MCM-41

The mesoporous nanostructured materials have been studied for application in the oil industry, in particular Al-MCM-41, due to the surface area around 800 to 1.000 m2 g-1 and, pore diameters ranging from 2 to 10 nm, suitable for catalysis to large molecules such as heavy oil. The MCM-41 has been synthesized by hydrothermal method, on which aluminum was added, in the ratio Si/Al equal to 50, to increase the generation of active acid sites in the nanotubes. The catalyst was characterized by X-ray diffraction (XRD), surface area by the BET method and, the average pore volume BJH method using the N2 adsorption, absorption spectroscopy in the infrared Fourier Transform (FT-IR) and determination of surface acidity with application of a probe molecule - n-butylamine. The catalyst showed well-defined structural properties and consistent with the literature. The overall objective was to test the Al-MCM-41 as catalyst and thermogravimetric perform tests, using two samples of heavy oil with API º equal to 14.0 and 18.5. Assays were performed using a temperature range of 30-900 ° C and heating ratios (β) ranging from 5, 10 and 20 °C min-1.The aim was to verify the thermogravimetric profiles of these oils when subjected to the action of the catalyst Al- MCM-41. Therefore, the percentage ranged catalyst applied 1, 3, 5, 10 and 20 wt%, and from the TG data were applied two different kinetic models: Ozawa-Flynn-Wall (OFW) and Kissinger-Akahrira-Sunose (KAS).The apparent activation energies found for both models had similar values and were lower for the second event of mass loss known as cracking zone, indicating a more effective performance of Al-MCM-41 in that area. Furthermore, there was a more pronounced reduction in the value of activation energy for between 10 and 20% by weight of the oil-catalyst mixture. It was concluded that the Al-MCM-41 catalyst has applicability in heavy oils to reduce the apparent activation energy of a catalyst-oil system, and the best result with 20% by weight of Al-MCM-41

Classificador máquina de suporte vetorial com análise de Fourier aplicada em dados de EEG e EMG

The classifier support vector machine is used in several problems in various areas of knowledge. Basically the method used in this classier is to end the hyperplane that maximizes the distance between the groups, to increase the generalization of the classifier. In this work, we treated some problems of binary classification of data obtained by electroencephalography (EEG) and electromyography (EMG) using Support Vector Machine with some complementary techniques, such as: Principal Component Analysis to identify the active regions of the brain, the periodogram method which is obtained by Fourier analysis to help discriminate between groups and Simple Moving Average to eliminate some of the existing noise in the data. It was developed two functions in the software R, for the realization of training tasks and classification. Also, it was proposed two weights systems and a summarized measure to help on deciding in classification of groups. The application of these techniques, weights and the summarized measure in the classier, showed quite satisfactory results, where the best results were an average rate of 95.31% to visual stimuli data, 100% of correct classification for epilepsy data and rates of 91.22% and 96.89% to object motion data for two subjects.