A soft computing approach to optimize the production of biodiesel

  1. Bobadilla, M.C. 1
  2. Martinez, R.F. 2
  3. Lorza, R.L. 1
  4. Gomez, F.S. 1
  5. Vergara Gonzalez, E.P. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Revista:
Lecture Notes in Computer Science

ISSN: 0302-9743

Año de publicación: 2017

Volumen: 10334 LNCS

Páginas: 507-518

Tipo: Artículo

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DOI: 10.1007/978-3-319-59650-1_43 SCOPUS: 2-s2.0-85021764595 GOOGLE SCHOLAR

Otras publicaciones en: Lecture Notes in Computer Science

Objetivos de desarrollo sostenible

Resumen

There is an increasing global concern for environmental protection for the conservation of non-renewal natural resources. It needs to be obtain an alternative, renewable and biodegradable combustible like biodiesel. Waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Biodiesel is synthesized by direct transesterification of vegetable oils, which is controlled by several inputs or process variables, including the dosage of catalyst, process temperature, mixing speed, mixing time, humidity and impurities of waste cooking oil. This study proposes a methodology to improve the production of biodiesel based on the use of soft computing techniques to predict several features of biodiesel production. The method selected a group of regression models based on Support Vector Machines (SVM) techniques to perform a prediction of several properties of a biodiesel sample taking into account a configuration of 7 test inputs. This test inputs were: molar ratio, dosage of catalyst, temperature, mixing speed, mixing time, humidity and impurities. Then and based on these inputs, the features to predict were: yield, turbidity, density, viscosity and high heating to obtain a better understanding of the process. Finally, considering the samples of the design of experiments studied, it has been observed that SVM models, based on a radial basic function kernel, record accurate results, with the best performance in four of the five features, improving in all the cases the accuracy obtained using linear regression. © Springer International Publishing AG 2017.