Extração enzimática de óleo e produção in situ de biodiesel a partir da Moringa oleífera Lam

With the growth and development of modern society, arises the need to search for new raw materials and new technologies which present the "clean" characteristic, and do not harm the environment, but can join the energy needs of industry and transportation. The Moringa oleifera Lam, plant originating from India, and currently present in the Brazilian Northeast, presents itself as a multi-purpose plant, can be used as a coagulant in water treatment, as a natural remedy and as a feedstock for biodiesel production. In this work, Moringa has been used as a raw material for studies on the extraction and subsequently in the synthesis of biodiesel. Studies have been conducted on various techniques of Moringa oil extraction (solvents, mechanical pressing and enzymatic), being specially developed an experimental design for the aqueous extraction with the aid of the enzyme Neutrase© 0.8 L, with the aim of analyzing the influence variable pH (5.5-7.5), temperature (45-55°C), time (16-24 hours) and amount of catalyst (2-5%) on the extraction yield. In relation to study of the synthesis of biodiesel was initially carried out a conventional transesterification (50°C, KOH as a catalyst, methanol and 60 minutes reaction). Next, a study was conducted using the technique of in situ transesterification by using an experimental design variables as temperature (30-60°C), catalyst amount (2-5%), and molar ratio oil / ethanol (1:420-1:600). The extraction technique that achieved the highest extraction yield (35%) was the one that used hexane as a solvent. The extraction using 32% ethanol obtained by mechanical pressing and extraction reached 25% yield. For the enzymatic extraction, the experimental design indicated that the extraction yield was most affected by the effect of the combination of temperature and time. The maximum yield obtained in this extraction was 16%. After the step of obtaining the oil was accomplished the synthesis of biodiesel by the conventional method and the in situ technique. The method of conventional transesterification was obtained a content of 100% and esters by in situ technique was also obtained in 100% in the experimental point 7, with a molar ratio oil / alcohol 1:420, Temperature 60°C in 5% weight KOH with the reaction time of 1.5 h. By the experimental design, it was found that the variable that most influenced the ester content was late the percentage of catalyst. By physico-chemical analysis it was observed that the biodiesel produced by the in situ method fell within the rules of the ANP, therefore this technique feasible, because does not require the preliminary stage of oil extraction and achieves high levels of esters

Aplicação de sistemas multi-classificadores no diagnóstico de falhas em motores de indução trifásicos

Equipment maintenance is the major cost factor in industrial plants, it is very important the development of fault predict techniques. Three-phase induction motors are key electrical equipments used in industrial applications mainly because presents low cost and large robustness, however, it isn t protected from other fault types such as shorted winding and broken bars. Several acquisition ways, processing and signal analysis are applied to improve its diagnosis. More efficient techniques use current sensors and its signature analysis. In this dissertation, starting of these sensors, it is to make signal analysis through Park s vector that provides a good visualization capability. Faults data acquisition is an arduous task; in this way, it is developed a methodology for data base construction. Park s transformer is applied into stationary reference for machine modeling of the machine s differential equations solution. Faults detection needs a detailed analysis of variables and its influences that becomes the diagnosis more complex. The tasks of pattern recognition allow that systems are automatically generated, based in patterns and data concepts, in the majority cases undetectable for specialists, helping decision tasks. Classifiers algorithms with diverse learning paradigms: k-Neighborhood, Neural Networks, Decision Trees and Naïves Bayes are used to patterns recognition of machines faults. Multi-classifier systems are used to improve classification errors. It inspected the algorithms homogeneous: Bagging and Boosting and heterogeneous: Vote, Stacking and Stacking C. Results present the effectiveness of constructed model to faults modeling, such as the possibility of using multi-classifiers algorithm on faults classification