Modeling the Transformation of Olive Tree Biomass into Bioethanol with Reg-CO\(^2\)RBFN
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.
KeywordsRegression models Data mining Enzymatic hydrolisis Olive tree biomass
This work is partially supported by the Spanish Ministry of Science and Technology under project TIN2015-68454-R.
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