"Utilização da RMN associada a métodos quimiométricos na caracterização de petróleos e derivados"

• Autor(a) principal: Oliveira, Emanuele Catarina da Silva
• Data de Publicação: 2016
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
• Texto Completo: http://repositorio.ufes.br/handle/10/7359
• topic: Química; Petróleo - Derivados - Análise; Ressonância magnética nuclear; Quimiometria; 54

Petroleum products are in general highly complex andconsiderable effort is needed to characterize their chemical and physical properties. Frequently the results of several analyses are urgent and this is compromised by the way the analyses are carried out. Thus, NMR (Nuclear Magnetic Resonance) associated with chemometrics methods have been generating alternative methods to characterize and evaluate physical and chemical properties of petroleum and its derivatives with high precision, reliability and promptitude. Considering its applicability, this work proposes the use of NMR as spectroscopic method and from the integration areas of 1H and 13C NMR’s spectra associated to the chemometric calibration tools (Partial Least Square Method,PLS, and Multiple Linear Regression, MLR) it were determined the following properties in petroleum samples: API gravity, Maximum Pour Point (°C), Cinematic Viscosity (mm2/s at 50°C) and SARA-saturated, aromatic, resins and asphaltenes (%m/m),in order to evaluate the obtained models’ performance. This evaluation was obtained through determination and analyses of the coefficient of determination (R2)of several calculated errors for the calibration and prediction sets. The models were still exposed to statistical tests and their figures of merit calculated. The chemical properties with direct influence on the spectral profile, such as, API gravity, kinematic viscosity and saturated and aromatic contents in SARA models, had generated more robust models and with better predictive capacity, when compared to the models selected for the prediction of the remaining properties, maximum pour point, resins and asphaltenes. A standard recognition tool (partial least squares method with discriminant analysis, PLS-DA) was used to classify petroleum samples into light, medium or heavy oils according to API gravity. In this work, PCA (Principal Component Analysis) models were also constructed aiming the exploratory analysis of a set of spectra of oil derivative samples in order to determine acidity of the original oil considering the structural parameters of the fractions..