dc.creatorFrattini Fileti A.M.
dc.creatorPedrosa L.S.
dc.creatorPereira J.A.F.R.
dc.date1999
dc.date2015-06-30T15:19:09Z
dc.date2015-11-26T15:24:15Z
dc.date2015-06-30T15:19:09Z
dc.date2015-11-26T15:24:15Z
dc.date.accessioned2018-03-28T22:33:08Z
dc.date.available2018-03-28T22:33:08Z
dc.identifier
dc.identifierComputers And Chemical Engineering. , v. 23, n. SUPPL. 1, p. S261 - S264, 1999.
dc.identifier981354
dc.identifier10.1016/S0098-1354(99)80064-7
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79954533764&partnerID=40&md5=6707c405e326e3638b508f05a5afbfcf
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/100880
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/100880
dc.identifier2-s2.0-79954533764
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1260644
dc.descriptionMulticomponent batch distillation is an operation difficult to control not only for its nonlinear and transient behaviour, but because the product quality cannot be measured rapidly and with reliability. In the present work, a computational system for direct digital control is developed for a pilot plant batch distillation column. The development of a self tuning regulator and a soft sensor of composition based on a neural network is described. Top and reboiler temperature measurements are the basis for the on-line composition inference. The computational system was experimentally tested in a computer operated pilot column. It could be seen that the neural network soft sensor is a feasible and a reliable tool to solve on-line operational problems of the control engineering systems. The developed control system permits to operate the batch distillation column efficiently and is easy to be implemented and operated. © 1999 Elsevier Science Ltd.
dc.description23
dc.descriptionSUPPL. 1
dc.descriptionS261
dc.descriptionS264
dc.descriptionFrattini Fileti, A.M., Pereira, J.A.F.R., The development and experimental testing of two adaptive control strategies for batch distillation (1997) IChemE Symposium Series no, 142 (1), pp. 249-257
dc.descriptionQuintero-Marmol, E., Luyben, W., Inferential Model-Based Control of Multlcomponent Batch Distillation (1992) Chem. Engng Sci, 47 (4), pp. 887-898
dc.descriptionSorensen, E., Skogestad, S., Comparison of inverted and regular batch distillation (1996) Chem: Engng Sci, 51 (22), pp. 4949-4962
dc.descriptionTham, M.T., Morris, A.J., Montague, G.A., Landt, P.A., Soft sensors for process estimation and inferential control (1991) J. Process Cantrall, pp. 3-14
dc.descriptionWillis, M.J., Montague, G.A., Di Massimo, C., Tham, M.T., (1992) Artificial Neural Networks in Process Estimation and Control. Automatica 28(6), pp. 1181-1187
dc.descriptionZhang, E.B.M., Morris, A.J., Kiparissides, C., Inferential Estimation of Polymer QUality using Stacked Neural Networks (1997) Camp. Chern. Engng, 21 (S), pp. SI025-S1030
dc.languageen
dc.publisher
dc.relationComputers and Chemical Engineering
dc.rightsfechado
dc.sourceScopus
dc.titleA Self Tuning Controller For Multicomponent Batch Distillation With Soft Sensor Inference Based On A Neural Network
dc.typeActas de congresos


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