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Modeling the Transformation of Olive Tree Biomass into Bioethanol with Reg-CO\(^2\)RBFN

  • Francisco Charte Ojeda
  • Inmaculada Romero Pulido
  • Antonio Jesús Rivera RivasEmail author
  • Eulogio Castro Galiano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10305)

Abstract

Research in renewable energies is a global trend. One remarkable area is the biomass transformation into biotehanol, a fuel that can replace fossil fuels. A key step in this process is the pretreatment stage, where several variables are involved. The experimentation for determining the optimal values of these variables is expensive, therefore it is necessary to model this process. This paper focus on modeling the production of biotehanol from olive tree biomass by data mining methods. Notably, the authors present Reg-CO\(^2\)RBFN, an adaptation of a cooperative-competitive designing method for radial basis function networks. One of the main drawbacks in this modeling is the low number of instances in the data sets. To compare the results obtained by Reg-CO\(^2\)RBFN, other well-known data mining regression methods are used to model the transformation process.

Keywords

Regression models Data mining Enzymatic hydrolisis Olive tree biomass 

Notes

Acknowledgments

This work is partially supported by the Spanish Ministry of Science and Technology under project TIN2015-68454-R.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Francisco Charte Ojeda
    • 1
  • Inmaculada Romero Pulido
    • 2
  • Antonio Jesús Rivera Rivas
    • 1
    Email author
  • Eulogio Castro Galiano
    • 2
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Department of Chemical, Environmental and Materials EngineeringUniversity of JaénJaénSpain

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