ESTIMATIVA DA MASSA ESPECÍFICA EM ETANOL COMBUSTÍVEL POR MODELOS DE REDES NEURAIS ARTIFICIAIS E DE REGRESSÃO POR MÍNIMOS QUADRADOS PARCIAIS

• Ano de defesa: 2013
• Autor(a) principal: Santos, Marcelo José Castro dos
• Orientador(a): MARQUES, Aldaléa Lopes Brandes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM QUÍMICA/CCET
• Departamento: QUIMICA
• País: BR
• Palavras-chave em Português: Etanol; Biocombustível; Predição; Massa específica; Redes neurais; Método de mínimos quadrados parciais
• Palavras-chave em Inglês: Ethanol; Biofuel; Predict; Density; Artificial neural networks; Partial least squares regression
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/951

The ethanol has continuously gained interests in many countries including Brazil due to the PROÁLCOOL program. The experimental determination of properties of ethanol and other fuels through official methods is very time consuming as well as tedious process. The estimation of these properties with the help of computational tools can be very useful. In the present work, the methods of partial least squares regression (PLS) and artificial neural network multilayer (ANN) were used to estimate one of the most important properties of fuel ethanol, density, using official quality parameters for ethanol, collected from LAPQAP/UFMA laboratory corresponding to 12 years (period: 2002-2013) of analyzes. A careful analysis of the data was performed to obtain a set of variables and data that best represents satisfactory performance of the two models. The estimates of both approaches were compared and validated. The predictive ability of the network obtained was very good for the parameters studied, consistent with the accuracy of the experimental measurements. The low mean square error, the randomness, the zero mean and the constant variance, obtained for the residues, indicated the suitability of the models, suggesting their use to estimate (predict) the density of ethanol. Results indicated that the model ANN was adequate, and the value of NMSE (normalized mean square error) of 0.0012, less than the PLS model of 0.2221. The result achieved is less than the range of measurement uncertainty of the equipment responsible for testing the density proving that the model used has satisfactory performance.