Advanced Control Strategy To A Fermentation Process To Obtain Ethanol

dc.creatorDuarte E.R.
dc.creatorEnder L.
dc.creatorMaciel Filho R.
dc.date2006
dc.date2015-06-30T18:03:12Z
dc.date2015-11-26T14:18:43Z
dc.date2015-06-30T18:03:12Z
dc.date2015-11-26T14:18:43Z
dc.date.accessioned2018-03-28T21:20:00Z
dc.date.available2018-03-28T21:20:00Z
dc.identifier0816910057; 9780816910052
dc.identifierAiche Annual Meeting, Conference Proceedings. , v. , n. , p. - , 2006.
dc.identifier
dc.identifier
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-77953028171&partnerID=40&md5=556a30040c9a71c365726d07e5a2a9b1
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/102855
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/102855
dc.identifier2-s2.0-77953028171
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1243716
dc.descriptionThe aim of this work is to evaluate the performance of a non linear control strategy based on Artificial Neural Networks (ANN's) for an extractive alcoholic fermentation process (MIMO 3x3). The process is simulated through a validated deterministic model which takes into account the main phenomena taking place in the system. The control is build-up by a multilayered and multivariable ANN that represents the inverse dynamics of the system, which is on-line trained through an optimization routine. The optimization routine adjusts the weight of neural controller using the estimate global error of closed loop. The global error is obtained using a dynamic model of the process, to represent a prediction for the next sampling time. This process model is on-line trained with the process data. The obtained results have shown the efficiency of control strategy in multivariable system and the potential of proposed on-line learning.
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dc.languageen
dc.publisher
dc.relationAIChE Annual Meeting, Conference Proceedings
dc.rightsfechado
dc.sourceScopus
dc.titleAdvanced Control Strategy To A Fermentation Process To Obtain Ethanol
dc.typeActas de congresos


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