Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/57067
Type: Artigo de periódico
Title: Control of absorption columns in the bioethanol process: Influence of measurement uncertainties
Author: Eduardo, E
Fileti, AMF
Abstract: The alcohol lost by evaporation during the bioethanol fermentation process may be collected and recovered using an absorption column. This equipment is also used in the carbonic gas treatment, a by-product from the sugar cane fermentation. In the present work. the development of nonlinear feed forward-feedback controllers, based on neural network inverse models, was proposed and tested to manipulate the absorbent flow rates The control purposes are to keep low ethanol concentration in the effluent gas phase from the first absorption column (ethanol recovery column), and to reduce the residual water concentration in the CO(2) gas effluent from the second tower (CO(2) treatment column). Based oil simulation studies, the neural network (ANN) controller performance was compared with the conventional PID control scheme application. The best ANN architecture was set up according to the Foresse and Hagan (1997) criterion. while the PID parameters were found from the well-known Cohen-Coon Equations and trial-and-error fine tuning. Initially, performances were evaluated for the system without concentration measurement uncertainties. From these tests, the ANN controller presented the smallest response time and overshoot for regulator and servo problems Three uncertainty levels were applied afterwards: 5%, 10%, and 15%. The ANN controller Outperformed the PID for all uncertainty levels tested for the ethanol recovery column. For the CO(2) treatment column. the ANN controller proceeded successfully under uncertainties of 5% and 10%, while the PID did not deal properly with uncertainties above 5% The statistical F-test, besides the ITAE, ISE, and IAE performance criteria, were calculated for both controllers applications and then compared They proved the superiority of the ANN control scheme. Using appropriately the proposed well-controlled absorption columns increases the efficiency of rile bioethanol production plant and can also provide carbon credits by avoiding CO(2) emission into the atmosphere (C) 2009 Elsevier Ltd All rights reserved
Subject: Concentration measurement uncertainty
Artificial neural network control
Bioethanol
Fermentation
Absorption column
Country: Inglaterra
Editor: Pergamon-elsevier Science Ltd
Rights: embargo
Identifier DOI: 10.1016/j.engappai.2009.11.002
Date Issue: 2010
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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