Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/106971
Type: Artigo de evento
Title: A Self-tuning Adaptive Control Applied To An Industrial Large Scale Ethanol Production
Author: Meleiro L.A.C.
Maciel Filho R.
Abstract: In this work, a multivariable adaptive self-tuning controller (STC) was developed for a biotechnological process application. Due to the difficulties involving the measurements or the excessive amount of variables normally found in industrial process, it is highly recommended to develop 'soft-sensors' which, in this work, were based fundamentally on artificial neural networks (ANN). These methods are especially suitable for the identification of time-varying and nonlinear models. An advanced control strategy based on STC was applied to a fermentation process to produce ethanol (ethyl alcohol) in industrial scale. The reaction rate considered for substratum consumption, cells and ethanol productions are validated with industrial data for typical operating conditions. The results obtained show that the procedure proposed in this work has a great potential for application. (C) 2000 Elsevier Science Ltd.In this work, a multivariable adaptive self-tuning controller (STC) was developed for a biotechnological process application. Due to the difficulties involving the measurements or the excessive amount of variables normally found in industrial process, it is highly recommended to develop 'soft-sensors' which, in this work, were based fundamentally on artificial neural networks (ANN). These methods are especially suitable for the identification of time-varying and nonlinear models. An advanced control strategy based on STC was applied to a fermentation process to produce ethanol (ethyl alcohol) in industrial scale. The reaction rate considered for substratum consumption, cells and ethanol productions are validated with industrial data for typical operating conditions. The results obtained show that the procedure proposed in this work has a great potential for application.
Editor: Elsevier Science Ltd, Exeter, United Kingdom
Rights: fechado
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-0342322861&partnerID=40&md5=23d305a8f4b4d5967584e84cf30a8f80
Date Issue: 2000
Appears in Collections:Unicamp - Artigos e Outros Documentos

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