State and parameter estimation based on a nonlinear filter applied to an industrial process control of ethanol production

dc.creatorMeleiro, LAC
dc.creatorMaciel, R
dc.date2000
dc.dateDEC
dc.date2014-07-30T20:02:43Z
dc.date2015-11-26T17:52:47Z
dc.date2014-07-30T20:02:43Z
dc.date2015-11-26T17:52:47Z
dc.date.accessioned2018-03-29T00:36:20Z
dc.date.available2018-03-29T00:36:20Z
dc.identifierBrazilian Journal Of Chemical Engineering. Brazilian Soc Chemical Eng, v. 17, n. 41824, n. 991, n. 1001, 2000.
dc.identifier0104-6632
dc.identifierWOS:000165740000062
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/74548
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/74548
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1290354
dc.descriptionMost advanced computer-aided control applications rely on good dynamics process models. The performance of the control system depends on the accuracy of the model used. Typically, such models are developed by conducting off-line identification experiments on the process. These experiments for identification often result in input-output data with small output signal-to-noise ratio, and using these data results in inaccurate model parameter estimates [1]. 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 proposed to develop "soft-sensors" which are based fundamentally on artificial neural networks (ANN). A second approach proposed was set in hybrid models, results of the association of deterministic models (which incorporates the available prior knowledge about the process being modeled) with artificial neural networks. In this case, kinetic parameters - which are very hard to be accurately determined in real time industrial plants operation - were obtained using ANN predictions. These methods are especially suitable for the identification of time-varying and nonlinear models. This advanced control strategy was applied to a fermentation process to produce ethyl alcohol (ethanol) 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 proposed procedure in this work has a great potential for application.
dc.description17
dc.description41824
dc.description991
dc.description1001
dc.languageen
dc.publisherBrazilian Soc Chemical Eng
dc.publisherSao Paulo
dc.publisherBrasil
dc.relationBrazilian Journal Of Chemical Engineering
dc.relationBraz. J. Chem. Eng.
dc.rightsaberto
dc.sourceWeb of Science
dc.subjectadaptive control
dc.subjectartificial neural networks
dc.subjecthybrid models
dc.subjectfermentation processes
dc.titleState and parameter estimation based on a nonlinear filter applied to an industrial process control of ethanol production
dc.typeArtículos de revistas


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