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Polynomial and ANN models applied to the formation of gums in Brazilian ethanol–gasoline blends—impact of gasoline composition, ethanol concentration, storage temperature, and aging duration

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Abstract

This work aims to define the influence of different parameters (such as ethanol concentration, type (regular or homologation fuel), and formulation (olefin and aromatic contents) of gasoline, temperature, and aging duration) in gum formation in Brazilian ethanol–gasoline blends. As a result, a database with more than 500 cases was built gathering experimental measures of unwashed and washed gum contents from the literature and original experimental data. Two approaches considered to define the mathematical models capable of predicting gum formation are compared: a linear equation with rectangular interaction terms and artificial neural network (ANN) models. Different ANN topologies were investigated and the most robust models were compared to polynomial equations. Then, response surfaces were plotted to verify the consistency of the model in the entire experimental domain. The ANN models performed better. Indeed, the coefficient of determination reached values as high as 0.953 and 0.984, for the testing data of washed and unwashed gum content, respectively, and lower differences with experimental data were observed, up to 0.5 and 0.2%, respectively. Additionally, the ANN models were more robust than the specific quadratic model available in the literature. In terms of the impact of the ethanol, it is possible to confirm a catalytic effect after aging at medium or high temperatures for washed gum formation at low concentrations of ethanol in gasoline. Without aging or after storage at low temperatures, ethanol has a simple dilution effect. Such conclusions are in agreement with previous literature and explain why some authors observed either catalytic or dilution effects of ethanol.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was financially supported by Peugeot Citroën do Brasil Automóveis Ltda (currently Stellantis). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. The authors would like to thank the CNPq/MCTIC for the financial support to the Department of Mechanical Engineering (DEM) at the Pontifical Catholic University of Rio de Janeiro (PUC-Rio) and the FAPERJ for the Jovem Cientísta do Nosso Estado (JCNE) grant awarded to Florian Pradelle.

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Authors and Affiliations

  1. Department of Mechanical Engineering (DEM), Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rua Marquês de São Vicente 225, Gávea, Rio de Janeiro, RJ, 22451-900, Brazil

    José Eduardo S. P. Carvalho, Sergio L. Braga & Florian Pradelle

  2. Department of Chemical and Materials Engineering (DEQM), Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rua Marquês de São Vicente 225, Gávea, Rio de Janeiro, RJ, 22451-900, Brazil

    Brunno F. Santos & Ana Rosa F. A. Martins

  3. Stellantis, Centre d’Expertise Métiers Et Régions, 2-10 Boulevard de L’Europe, 78092 Cedex 9, Poissy, France

    Renata N. C. Pradelle, Franck Turkovics, Béatrice Perrier & François Maire

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S. P. Carvalho, J.E., F. Santos, B., F. A. Martins, A. et al. Polynomial and ANN models applied to the formation of gums in Brazilian ethanol–gasoline blends—impact of gasoline composition, ethanol concentration, storage temperature, and aging duration. Neural Comput & Applic 35, 16267–16284 (2023). https://doi.org/10.1007/s00521-023-08396-1

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