Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/86461
Type: Artigo de periódico
Title: Product Quality Monitoring Using Extreme Learning Machines And Bat Algorithms: A Case Study In Second-generation Ethanol Production
Author: Farias F.S.
Azevedo R.A.
Rivera E.C.
Herrera W.E.
Filho R.M.
Lima L.P.
Abstract: In this study, a new methodology for online monitoring of second-generation ethanol production is presented. The prediction of the concentration of ethanol, substrate and cells from secondary measurements (pH, turbidity, CO2 and temperature) is compared with experimental data from the fermentation of a mixture of molasses and hydrolyzed sugarcane bagasse from the alkaline hydrogen peroxide pre-treatment at 25% and 75% of volume. The Extreme Learning Machine algorithm (ELM) provided a very good alternative to traditional Multilayer Perceptron neural networks (MLP) and the BAT optimization technique applied to ELM algorithm provided a fast parallel search for the best solution. This new methodology offered a good alternative to the standard soft- sensor approach based on MLP and fast and reliable product quality estimates for key process variables as in second-generation ethanol production. © 2014 Elsevier B.V.
Editor: Elsevier
Rights: fechado
Identifier DOI: 10.1016/B978-0-444-63456-6.50160-5
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84902961691&partnerID=40&md5=0d05590e066842a68fd9306781a5c61b
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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