Thermal properties of ethylic biodiesel blends and solid fraction prediction using artificial neural networks

Autor(es)

Magalhaes, Ana M. S. ; Brentan, Bruno M. ; Meirelles, Antonio J. A. ; Maximo, Guilherme J.

• Tipo de documento: Artigo Científico
• Fonte: Fluid Phase Equilibria; v. 574, p. 11-pg., 2023-07-03.

Resumo

The cold flow properties are known to be a significant limiting factor for the use of a vegetable oil source for biodiesel production, as well as their use worldwide. However, the use of ethylic biodiesel, new sources and blends have been shown to improve these properties. Looking for vegetable sources alternatives and use of ethanol, this work evaluated the thermal properties (solid fraction content - SFC - behavior and cold flow properties - CFP) of model ethylic biodiesel from Brazilian biodiversity oils such coconut, ucuuba, macauba, and babassu, including their blends with other commercial sources such as palm, sunflower, rapeseed, and jatropha. Artificial Neural Networks (ANN) was used as a tool for prediction of the biodiesels' SFC behavior, looking for the best alternative to produce biofuels with good CFP. Together with the mentioned biodiesels, the database used was composed by SFC data from literature for soybean, palm stearin, palm olein, palm kernel, beef tallow, macauba kernel, including their blends. A Multilayer Perceptron Feed Forward Neural Network with the Sigmoid function as activation function and the Levenberg Marquardt method as the convergence algorithm was used. The search of the best topology was performed by a Central Composite Rotatable Design considering 15 different topologies (3 hidden layers from 2 to 18 neurons each layer), the best of which was 10.18.10 (10 neurons in the first layer, 18 in the second, and 10 in the third) presenting a Mean Square Error value, of 0.0356 and 0.0519 for training and testing. From the results obtained, the present study demonstrates the ability of blends in lowering the CFP (cloud point, pour point and cold filter plugging point). The ANN could simulate the SFC curves behavior of ethylic biodiesel with high accuracy, considering their ethyl ester composition and temperature, expanding the range of possibilities for biodiesel formulation and improvement of biodiesel behavior in cold weather. (AU)

• Processo FAPESP: 14/21252-0 - Equilíbrio e processos de produção de biocombustíveis e bioprodutos
• Beneficiário: Antonio José de Almeida Meirelles
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/10636-8 - Da fábrica celular à biorrefinaria integrada Biodiesel-Bioetanol: uma abordagem sistêmica aplicada a problemas complexos em micro e macroescalas
• Beneficiário: Roberto de Campos Giordano
• Modalidade de apoio: Auxílio à Pesquisa - Programa BIOEN - Temático
• Processo FAPESP: 21/11380-5 - CPTEn - Centro Paulista de Estudos da Transição Energética
• Beneficiário: Luiz Carlos Pereira da Silva
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Ciência para o Desenvolvimento