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dc.creatorBarradas Filho, Alex Oliveira
dc.creatorAmorim Viegas, Isabelle Moraes
dc.date.accessioned2021-01-21T17:58:52Z
dc.date.available2021-01-21T17:58:52Z
dc.identifier.otherhttps://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks/applications-of-artificial-neural-networks-in-biofuels
dc.identifier.urihttp://hdl.handle.net/20.500.12010/16826
dc.format.extent21 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherIntechOpenspa
dc.subjectIngeniería civilspa
dc.titleApplications of Artificial Neural Networks in Biofuelsspa
dc.subject.lembRedes neuronales artificiales (RNA)spa
dc.subject.lembAplicaciones de las redes neuronalesspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.localAbierto (Texto Completo)spa
dc.identifier.doi10.5772/intechopen.70691
dc.relation.referencesAlex Oliveira Barradas Filho and Isabelle Moraes Amorim Viegas (December 20th 2017). Applications of Artificial Neural Networks in Biofuels, Advanced Applications for Artificial Neural Networks, Adel El-Shahat, IntechOpen, DOI: 10.5772/intechopen.70691.spa
dc.description.abstractenglishThis chapter is focused on the application of artificial neural networks (ANNs) in the development of alternative methods for biofuel quality issues. At first, the advances and the proliferation of models and architectures of artificial neural networks are highlighted in the text by the characteristics of robustness and fault tolerance, learning capacity, uncertain information processing and parallelism, which allow the application in problems of complex nature. In this scenario, biofuels are contextualized and focused on issues of quality control and monitoring. Therefore, this chapter leads to a study of prediction and/or classification of biofuels quality parameters by the description of published works on the topic under discussion. Afterwards, a case study is performed to demonstrate, in a practical way, the steps and procedures to build alternative models for predicting the oxidative stability of biodiesel. The procedure goes from the processing of the data obtained by the near infrared until the evaluation of the alternative method developed by the neural network. In addition, some evaluation parameters are described for the assessment of the alternative method built. As a result, the feasibility and practicality of the application of neural networks to the quality of biofuels are proven.spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2f33spa
dc.rights.creativecommonshttps://creativecommons.org/licenses/by-nc/4.0/legalcode


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