@article {289,
	title = {Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass},
	journal = {Computers \& Chemical Engineering},
	volume = {101},
	year = {2017},
	note = {ENE2014-60090-C2-2-R,TIN2015-68454-R},
	pages = {23{\textendash}30},
	abstract = {The production of biofuels is a process that requires the adjustment of multiple parameters. Performing experiments in which these parameters are changed and the outputs are analyzed is imperative, but the cost of these tests limits their number. For this reason, it is important to design models that can predict the different outputs with changing inputs, reducing the number of actual experiments to be completed. Response Surface Methodology (RSM) is one of the most common methods for this task, but machine learning algorithms represent an interesting alternative. In the present study the predictive performance of multiple models built from the same problem data are compared: the production of bioethanol from lignocellulosic materials. Four machine learning algorithms, including two neural networks, a support vector machine and a fuzzy system, together with the RSM method, are analyzed. Results show that Reg-CO2RBFN, the method designed by the authors, improves the results of all other alternatives.},
	doi = {10.1016/j.compchemeng.2017.02.008},
	author = {Francisco Charte and Inmaculada Romero and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Eulogio Castro}
}
@conference {310,
	title = {Modeling the Transformation of Olive Tree Biomass into Bioethanol with Reg-CO2RBFN},
	booktitle = {14th International Work-Conference on Artificial Neural Networks (IWANN 2017)},
	year = {2017},
	note = {TIN2015-68454-R},
	month = {6},
	pages = {733{\textendash}744},
	address = {C{\'a}diz (Spain)},
	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-CO2RBFN, 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-CO2RBFN, other well-known data mining regression methods are used to model the transformation process.},
	isbn = {978-3-319-59152-0},
	doi = {10.1007/978-3-319-59153-7_63},
	author = {Francisco Charte and Inmaculada Romero and A.J. Rivera-Rivas and Eulogio Castro}
}
