Blucher Chemical Engineering Proceedings

Setembro 2018, vol.1, num.5 - XXII Congresso Brasileiro de Engenharia Química
Pôster - Open Access Idioma principal | Segundo principal

Experimental and prediction of physics and chemical properties of biodiesel

Experimental and prediction of physics and chemical properties of biodiesel

GUIMARÃES, D. H. P; ARCE, P. F. C; AGUIRRE, L. R

Pôster:

The proposal of present research includes producing biodiesel fromblends of virgin castor oil (VCO) and waste frying oil (WFO). Physicochemicalcharacterization of VCO (B1), WFO (B2) and three different blends (B3:50%VCO+50%WFO; B4:25%VCO+75%WFO; B5:75%VCO+25%WFO) wereevaluated. The results indicated that the content of waste frying oils of the blendsinfluenced the physical-chemical properties of the biofuel, but did not influence therheological behavior of the biodiesel, which tended to the Newtonian. By anotherhand, physical and chemical properties of biodiesel were predicted by using moleculardescriptors and several architectures of artificial neural networks. The optimumarchitecture (configuration 10-2-20-3) was found for the lowest deviations in the threedependent variables (fuel acidity, ketone index and kinematic viscosity). 

The proposal of present research includes producing biodiesel fromblends of virgin castor oil (VCO) and waste frying oil (WFO). Physicochemicalcharacterization of VCO (B1), WFO (B2) and three different blends (B3:50%VCO+50%WFO; B4:25%VCO+75%WFO; B5:75%VCO+25%WFO) wereevaluated. The results indicated that the content of waste frying oils of the blendsinfluenced the physical-chemical properties of the biofuel, but did not influence therheological behavior of the biodiesel, which tended to the Newtonian. By anotherhand, physical and chemical properties of biodiesel were predicted by using moleculardescriptors and several architectures of artificial neural networks. The optimumarchitecture (configuration 10-2-20-3) was found for the lowest deviations in the threedependent variables (fuel acidity, ketone index and kinematic viscosity). 

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DOI: 10.5151/cobeq2018-PT.0017

Referências bibliográficas
  • [1] A.S.T.M. Standard Test Method for Saponification Number (Empirical) of Synthetic and Natural Waxes. COSTA NETO PR, Rossi LFS. The utilization of used frying oil for the production of biodiesel. Quim Nova, 23, 531-537, 2000. DANTAS, M.B. Obtenção, caracterização e estudo termoanalítico de biodiesel de milho. 2006. 205 f. Dissertação (Mestrado em Química), Universidade Federal da Paraíba. DEMIRBRAS A, DEMIRBRAS MF. Importance of Algae as a Source of Biodiesel. Energ Convers Manage. 52, 163-179, 2011. KODE SRL. Dragon (Software for Molecular Descriptor Calculation) version 7.0, http://chm.kode-solutions.net, 2016. LÔBO IP, FERREIRA SL, CRUZ RS. Biodiesel: quality parameters and analytical methods. Quim Nova. 32, 1596-1608, 2009. MORETTO E, FETT R. Tecnologia de Óleos e Gorduras Vegetais na Indústria de Alimentos. Varela Editora e Livraria Ltda, São Paulo, 1998. SILVA LT, GOUVEIA L, REIS SA. Integrated Microbial Processes for Biofuels and High Value-Added Products: The Way to Improve the Cost Effectiveness of Biofuel Production. Appl Microbiol Biot. 98, 1043-1053, 2014. SILVA TAR, NETO WB. Study of Reduction Acidity from Residual Oil for Biodiesel Production Using Fractional Factorial Design. Rev Virt Quim. 5, 828-839, 2013. TODESCHINI R, CONSONNI V. Handbook of Molecular Descriptors, Wiley-VCH, 2000. ZHANG Y, DUBE MA, MCLEAN DD, KATES M. Biodiesel production from waste cooking oil: 1. Process design and technology assessment. Bioresource Technol. 89, 1–16, 2003.
Como citar:

GUIMARÃES, D. H. P; ARCE, P. F. C; AGUIRRE, L. R; "Experimental and prediction of physics and chemical properties of biodiesel", p-60-63. In: . São Paulo: Blucher, 2018.
ISSN 23591757, DOI 10.5151/cobeq2018-PT.0017

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