Predição de propriedades de gasolinas a partir das suas composições

Detalhes bibliográficos
Ano de defesa: 2006
Autor(a) principal: Buarque, Hugo Leonardo de Brito
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/7499
Resumo: Commercial gasolines are normally produced by blendin g hydrocarbon fractions obtained from the distillation of crude oil or from o ther petrochemical or refining processes, and carried through in order to comply with a variety of legal and ambient specifications at minimum cost. The quality for the use a nd commercialization of gasolines is evaluated through certain characteristics specified by governmental regulation. Such characteristics are usually determined by different methodologies and experimental techniques, since those depend on the ir constituents and their respective concentrations with a high complexity. Thus, blending of gasolines in petrochemical and refining industries is sometimes a very laborious procedure. The prediction of fuel properties from composition data is growing in importance in the last few years. Methods of group contribution have been usedin the last decades to predict properties of pure organic compounds and some mix ture parameters (e.g.,UNIFAC). However, most of the recent studies use artificial neural networks as a technique for prediction for fuel properties using the composition of classes of constituents or key-compounds as input data. The main a dvantage of a neural network is its capacity to extract general and unknown in formation for certain series of data (training), supplying useful and fast models for prediction. However, the use of neural networks trained to predict properties of fue ls produced from one given combination of petroleum fractions can not be suitable in the prediction of the characteristics of other gasolines produced from other orig ins due to the complexity and variability of gasoline composition. In this study, methods of multiple linear regression and artificial neural networks have been eval uated in the correlation and prediction of gasoline properties from information of composition obtained by gas chromatography, as well as a methodology for prediction of properties using a hybrid method composed of neural networks and group contribut ion. The developed model is evaluated and compared to other methods, revealing to be sufficiently promising for prediction of properties of pure components and com plex mixtures.