Application of Neural Networks for Viscosity Index Prediction: an evaluation of biodiesel quality
Abstract
To measure the biodiesel quality in Brazil, several parameters are established for this purpose. Some of them, such as viscosity, iodine number, cetane number, density are important because they characterize important functions of how biodiesel will react in the engines. In this study, Artificial Neural Networks (ANNs) were used to predict the viscosity value of biodiesel. For this purpose, 13 compounds of fatty acid esters were used as input to the ANNs, with several feedforward convergence algorithms having viscosity indexes as output from the networks.
References
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García, M., Alba, J. J., Gonzalo, A., Sánchez, J. L., and Arauzo, J. (2013). Density of alkyl esters and its mixtures: A comparison and improvement of predictive models. Fuel, 103:232–238.
Hoekman, S. K., Broch, A., Robbins, C., Ceniceros, E., and Natarajan, M. (2012). Review of biodiesel composition, properties, and specifications. Renewable and Sustainable Energy Reviews, 16(1):143–169.
Jahirul, M. I., Senadeera, W., Brooks, P., Brown, R. J., Situ, R., Pham, P. X., and Masri, A. R. (2013). An Artificial Neutral Network (ANN) Model for Predicting Biodiesel Kinetic Viscosity as a Function of Temperature and Chemical Compositions. (December):1–6.
Kivevele, T. T., Mbarawa, M. M., Bereczky, a., Laza, T., and Madarasz, J. (2011). Impact of antioxidant additives on the oxidation stability of biodiesel produced from Croton Megalocarpus oil. Fuel Processing Technology, 92(6):1244–1248.
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Kohavi, R. et al. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI, volume 14, pages 1137–1145.
Meng, X., Jia, M., and Wang, T. (2014). Neural network prediction of biodiesel kinematic viscosity at 313K. Fuel, 121:133–140.
Moser, B. R. (2009). Biodiesel production, properties, and feedstocks. In Vitro Cellular & Developmental Biology - Plant, 45(3):229–266.
Nogueira, C. A. (2010). Densities and Viscosities of Binary Mixtures of Babassu Biodiesel Cotton Seed or Soybean Biodiesel at Different Temperatures. 55(11):5305–5310.
Padhi, S. K. (2010). Preparation and Characterization of Biodiesel from Non-Edible Oils. PhD thesis, Doctoral dissertation, PhD thesis. National Institute of Technology, Rourkela.
Piloto-Rodríguez, R., Sánchez-Borroto, Y., Lapuerta, M., Goyos-Pérez, L., and Verhelst, S. (2013). Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression. Energy Conversion and Management, 65:255–261.
Ramírez-Verduzco, L. F., Rodríguez-Rodríguez, J. E., and Jaramillo-Jacob, A. D. R. (2012). Predicting cetane number, kinematic viscosity, density and higher heating value of biodiesel from its fatty acid methyl ester composition. Fuel, 91(1):102–111.
Ramos, M. J., Fernández, C. M., Casas, A., Rodríguez, L., and Pérez, A. (2009). Influence of fatty acid composition of raw materials on biodiesel properties. Bioresource technology, 100(1):261–8.
Rezaee, M., Basri, M., Rahman, R. N. Z. R. A., Salleh, A. B., Chaibakhsh, N., and Masoumi, H. R. F. (2014). A multivariate modeling for analysis of factors controlling the particle size and viscosity in palm kernel oil esters-based nanoemulsions. Industrial Crops and Products, 52(0):506–511.
Saldana, D. A., Starck, L., Mougin, P., Rousseau, B., Ferrando, N., and Creton, B. (2012). Prediction of Density and Viscosity of Biofuel Compounds Using Machine Learning Methods. Energy & Fuels, 26(4):2416–2426.
Sanford, S. D., White, J. M., Shah, P. S., Wee, C., Valverde, M. A., Meier, G. R., Group, R. E., and Report, C. (2009). Feedstock and Biodiesel Characteristics Report. pages 1–136.
Santos, J. d. J. (2008). Biodiesel of Babassu: Thermal Verification, Oxidative and Binary Blends. PhD thesis, D. Sc. Thesis. Federal University of Paraí¿ ba, Brazil.
Sharon, H., Jayaprakash, R., Karthigai selvan, M., Soban kumar, D., Sundaresan, a., and Karuppasamy, K. (2012). Biodiesel production and prediction of engine performance using SIMULINK model of trained neural network. Fuel, 99:197–203.
Silva, L. N. d. C. (1998). Analise e sintese de estrategias de aprendizado para redes neurais artificiais. PhD thesis, Dissertação de mestrado. Universidade Estadual de Campinas. Brasil. Pag. 210.
Allen, C., Watts, K., Ackman, R., and Pegg, M. (1999). Predicting the viscosity of biodiesel fuels from their fatty acid ester composition. Fuel, 78(11):1319–1326.
Baptista, P., Felizardo, P., Menezes, J. C., and Neiva Correia, M. J. (2008). Multivariate near infrared spectroscopy models for predicting the iodine value, CFPP, kinematic viscosity at 40 degrees C and density at 15 degrees C of biodiesel. Talanta, 77(1):144–51.
Candeia, R. A. (2008). Soybean Biodiesel: Synthesis, Degradation and Binary Mixtures. PhD thesis, Doctoral dissertation, D. Sc. Thesis. Federal University of Paraíba. Brazil. Pag. 138.
Çay, Y., Korkmaz, I., Çiçek, A., and Kara, F. (2013). Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network. Energy, 50:177–186.
Chavarria-Hernandez, J. C. and Pacheco-Catalán, D. E. (2014). Predicting the kinematic viscosity of {FAMEs} and biodiesel: Empirical models. Fuel, 124(0):212 – 220.
Da Silva, I. N., Spatti, D. H., and Flauzino, R. A. (2010). Redes Neurais Artificiais para Engenharia e Ciências Aplicadas: curso prático. Artliber.
Dantas, M. B. (2006). Biodiesel blends: flow properties, thermal and oxidative stability during storage and monitoring. PhD thesis, Doctoral dissertation, D. Sc. Thesis. Federal University of Paraíba. Brasil. Pag. 138.
Freitas, S. V., Segovia, J. J., Carmen Martín, M., Zambrano, J., Oliveira, M. B., Lima, A. S., and a.P. Coutinho, J. a. (2014). Measurement and prediction of high-pressure viscosities of biodiesel fuels. Fuel, 122:223–228.
García, M., Alba, J. J., Gonzalo, A., Sánchez, J. L., and Arauzo, J. (2013). Density of alkyl esters and its mixtures: A comparison and improvement of predictive models. Fuel, 103:232–238.
Hoekman, S. K., Broch, A., Robbins, C., Ceniceros, E., and Natarajan, M. (2012). Review of biodiesel composition, properties, and specifications. Renewable and Sustainable Energy Reviews, 16(1):143–169.
Jahirul, M. I., Senadeera, W., Brooks, P., Brown, R. J., Situ, R., Pham, P. X., and Masri, A. R. (2013). An Artificial Neutral Network (ANN) Model for Predicting Biodiesel Kinetic Viscosity as a Function of Temperature and Chemical Compositions. (December):1–6.
Kivevele, T. T., Mbarawa, M. M., Bereczky, a., Laza, T., and Madarasz, J. (2011). Impact of antioxidant additives on the oxidation stability of biodiesel produced from Croton Megalocarpus oil. Fuel Processing Technology, 92(6):1244–1248.
Knothe, G., Krahl, J., and Gerpen, J. V. (2010). Biodiesel Handbook. AOCS Press, 2nd edition.
Kohavi, R. et al. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI, volume 14, pages 1137–1145.
Meng, X., Jia, M., and Wang, T. (2014). Neural network prediction of biodiesel kinematic viscosity at 313K. Fuel, 121:133–140.
Moser, B. R. (2009). Biodiesel production, properties, and feedstocks. In Vitro Cellular & Developmental Biology - Plant, 45(3):229–266.
Nogueira, C. A. (2010). Densities and Viscosities of Binary Mixtures of Babassu Biodiesel Cotton Seed or Soybean Biodiesel at Different Temperatures. 55(11):5305–5310.
Padhi, S. K. (2010). Preparation and Characterization of Biodiesel from Non-Edible Oils. PhD thesis, Doctoral dissertation, PhD thesis. National Institute of Technology, Rourkela.
Piloto-Rodríguez, R., Sánchez-Borroto, Y., Lapuerta, M., Goyos-Pérez, L., and Verhelst, S. (2013). Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression. Energy Conversion and Management, 65:255–261.
Ramírez-Verduzco, L. F., Rodríguez-Rodríguez, J. E., and Jaramillo-Jacob, A. D. R. (2012). Predicting cetane number, kinematic viscosity, density and higher heating value of biodiesel from its fatty acid methyl ester composition. Fuel, 91(1):102–111.
Ramos, M. J., Fernández, C. M., Casas, A., Rodríguez, L., and Pérez, A. (2009). Influence of fatty acid composition of raw materials on biodiesel properties. Bioresource technology, 100(1):261–8.
Rezaee, M., Basri, M., Rahman, R. N. Z. R. A., Salleh, A. B., Chaibakhsh, N., and Masoumi, H. R. F. (2014). A multivariate modeling for analysis of factors controlling the particle size and viscosity in palm kernel oil esters-based nanoemulsions. Industrial Crops and Products, 52(0):506–511.
Saldana, D. A., Starck, L., Mougin, P., Rousseau, B., Ferrando, N., and Creton, B. (2012). Prediction of Density and Viscosity of Biofuel Compounds Using Machine Learning Methods. Energy & Fuels, 26(4):2416–2426.
Sanford, S. D., White, J. M., Shah, P. S., Wee, C., Valverde, M. A., Meier, G. R., Group, R. E., and Report, C. (2009). Feedstock and Biodiesel Characteristics Report. pages 1–136.
Santos, J. d. J. (2008). Biodiesel of Babassu: Thermal Verification, Oxidative and Binary Blends. PhD thesis, D. Sc. Thesis. Federal University of Paraí¿ ba, Brazil.
Sharon, H., Jayaprakash, R., Karthigai selvan, M., Soban kumar, D., Sundaresan, a., and Karuppasamy, K. (2012). Biodiesel production and prediction of engine performance using SIMULINK model of trained neural network. Fuel, 99:197–203.
Silva, L. N. d. C. (1998). Analise e sintese de estrategias de aprendizado para redes neurais artificiais. PhD thesis, Dissertação de mestrado. Universidade Estadual de Campinas. Brasil. Pag. 210.
Published
2017-07-02
How to Cite
DE SOUSA, Raquel Machado; LABIDI, Sofiane; LEOCADIO, Jailson Nunes.
Application of Neural Networks for Viscosity Index Prediction: an evaluation of biodiesel quality. In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 8. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 809-818.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2017.3440.
