Application of steady-state and dynamic modeling for the prediction of the BOD of an aerated lagoon at a pulp and paper mill - Part II. Nonlinear approaches

dc.creatorOliveira-Esquerre, KP
dc.creatorSeborg, DE
dc.creatorMori, M
dc.creatorBruns, RE
dc.date2004
dc.dateDEC 15
dc.date2014-11-16T05:10:32Z
dc.date2015-11-26T16:19:56Z
dc.date2014-11-16T05:10:32Z
dc.date2015-11-26T16:19:56Z
dc.date.accessioned2018-03-28T23:02:37Z
dc.date.available2018-03-28T23:02:37Z
dc.identifierChemical Engineering Journal. Elsevier Science Sa, v. 105, n. 41671, n. 61, n. 69, 2004.
dc.identifier1385-8947
dc.identifierWOS:000225612000007
dc.identifier10.1016/j.cej.2004.06.012
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/54775
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/54775
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/54775
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1267753
dc.descriptionNeural networks can provide effective predictive models for complex processes that are poorly described by first principle models, such as wastewater biological treatment systems. In this paper multilayer perceptron (MLP) and functional-link neural networks (FLN) are developed to predict inlet and outlet biochemical oxygen demand (BOD) of an aerated lagoon operated by International Paper of Brazil. In Part 1, predictive models for both inlet and outlet BOD for the aerated lagoon were developed using linear multivariate regression techniques. For the current case study, MLP networks are the best choice for the prediction models. When only a relatively small number of samples is available, substantial improvement in inlet and outlet BOD prediction is shown for both FLN and MLP modeling using a reduced input variable set that was generated using partial least squares (PLS). Thus, this paper provides a novel approach for developing PLS-FLN model structures. (C) 2004 Elsevier B.V. All rights reserved.
dc.description105
dc.description41671
dc.description61
dc.description69
dc.languageen
dc.publisherElsevier Science Sa
dc.publisherLausanne
dc.publisherSuíça
dc.relationChemical Engineering Journal
dc.relationChem. Eng. J.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectbiochemical oxygen demand
dc.subjectmodeling
dc.subjectartificial neural networks
dc.subjectaerobic process
dc.subjectbioprocess monitoring
dc.subjectwastewater treatment
dc.subjectArtificial Neural-networks
dc.subjectNutrient Dynamics
dc.subjectBatch Reactor
dc.subjectIdentification
dc.subjectOptimization
dc.subjectUnification
dc.subjectSimulation
dc.titleApplication of steady-state and dynamic modeling for the prediction of the BOD of an aerated lagoon at a pulp and paper mill - Part II. Nonlinear approaches
dc.typeArtículos de revistas


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