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Experimental Data and Prediction of the Physical and Chemical Properties of Biodiesel.

Pedro Felipe Arce Castillo Daniela Helena Pelegrine Guimarães; Lucas R Aguirre

Chemical Engineering Communications v.206, n.10, p.1273-1285, 2019

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Título:
Experimental Data and Prediction of the Physical and Chemical Properties of Biodiesel.
Autor: Pedro Felipe Arce Castillo
Daniela Helena Pelegrine Guimarães; Lucas R Aguirre
Assuntos: REDES NEURAIS; BIODIESEL; NOZES; Artificial Neural Networks; Waste Cooking Oil; Biodiesel; Castor Beans; Molecular Descriptors
É parte de: Chemical Engineering Communications v.206, n.10, p.1273-1285, 2019
Notas: Disponível em: https://doi.org/10.1080/00986445.2018.1555533. Acesso em: 04 jan. 2024
Descrição: In this work, production of biodiesel from blends of virgin castor oil (VCO) and waste frying oil (WFO) was studied. Initial blends and the biodiesel obtained were characterized and the properties were analyzed statistically. Results indicated that the content of WFOs of the blends influenced the physical and chemical properties of the biofuel, but did not influence the rheological behavior of the biodiesel. Physical and chemical properties of biodiesel were also predicted using molecular descriptors (MDs) using several architectures of artificial neural networks (ANNs). Optimum architecture (configuration 10-2-20-3) was found for the lowest deviations in the three dependent variables (fuel acidity, ketone index, and kinematic viscosity).
Editor: Berkeley Taylor & Francis
Data de criação/publicação: 2019
Formato: p. 1273-1285.
Idioma: Inglês

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