fevereiro 2015 vol. 1 num. 2 - XX Congresso Brasileiro de Engenharia Química

Artigo - Open Access.

Idioma principal

NEURAL NETWORK MODEL FOR PREDICTION OF THE PERMEATE FLUX OF MACROMOLECULAR SOLUTIONS WITH GUAR AND XANTHAN GUMS IN CROSSFLOW MICROFILTRATION PROCESSING

FILLETTI, E. R.; SILVA, J. M.;

Artigo:

The purpose of this research work is to develop an artificial neural network model that predicts the permeate flux of macromolecular solutions with guar and xanthan gums in ceramic membrane of nominal pore size of 0.2 μm and 0.4 μm to two different temperatures. The neural network has been trained through a selected set of experimental data already published. The experimental data were obtained for the concentration in turbulent flow. Few experimental series were considered to construct a database applied to neural model parameters that could be adjusted. The input variables of neural model were temperature, nominal pore size and microfiltration time. The results show that the neural model can be trained in a reasonable computational time and it is able to predict real values of the permeate flux.

Artigo:

Palavras-chave:

DOI: 10.5151/chemeng-cobeq2014-2029-16295-176882

Referências bibliográficas
  • [1] CHELLAM, S. Artificial neural network model for transiente crossflow microfiltration of polydis-persed suspensions. J. Membr. Sc., 258, 35-42, 2005.
  • [2] CURCIO, S., SCILINGO, G., CALABRÒ, V., IORIO, G. Ultrafiltration of BSA in pulsating Área temática: Engenharia das Separações e Termodinâmica 7conditions: an artificial neural networks approach. J. Membr. Sc., 246, 235-247, 2005.
  • [3] CURCIO, S., CALABRÒ, V., IORIO, G. Reduction and control of flux decline in cross-flow membrane processes modeled by artificial neural networks. J. Membr. Sc., 286, 125-132, 2006.
  • [4] GUADIX, A., ZAPATA, J.E., ALMECIJA, M.C., GUADIX, E.M. Predicting the flux decline in milk cross-flow ceramic ultrafiltration by artificial neural networks. Desalination, 250, 1118-1120, 2010.
  • [5] FONTES, S. R., QUEIROS, V. M. S., LONGO, E., ANTUNES, M. V. Tubular microporous alumina structure for demulsi fying vegetable oil/water emulsions and concentrating macromolecular suspen-sions. Sep. Purif. Technol., 44, 235-241, 200
  • [6] HAYKIN S. Neural Networks: a comprehensive foundation. 2nd ed. New Jersey: Prentice Hall, 1999.
  • [7] HILAL, N., OGUNBIYI, O.O., AL-ABRI, M. Neural network modeling for separation of bentonite in tubular ceramic membranes. Desalination, 228, 175-182, 2008.
  • [8] JENKINS, W.M. An introduction to neural computing for the structural engineer. The Structural En-gineering, 75(3), p.38–41, 1997.
  • [9] LIU, Q.F., KIM, S.H., LEE, S. Prediction of microfiltration membrane fouling using artificial neural networks models. Sep. Purif. Technol, 70, 96-102, 200
  • [10] NAFEY, A. S. Neural network based correlation for critical heat flux in steam-water flows. Int. J. Thermal Sc., 48, 2264-2270, 2009.
  • [11] NIEMI, H., BULSARI, A., PALOSAARI, S. Simulation of membrane separation by neural networks. J. Membr. Sc., 102, 185-191, 1995.
  • [12] QUEIROZ, V.M.S., FONTES, S.R. Experimental analysis of structural change and rheological behavi-or of macromolecular solutions with guar and xanthan gums in crossflow microfiltration processing. Food and Bioprocess Tech., 1, 180-186, 2008.
  • [13] RAZAVI, M.A., MORTAZAVI, A., MOUSAVI, M. Application of neural networks for crossflow milk ultrafiltration simulation. Int. Dairy J., 14, 69-80, 2004.
  • [14] SAHOO, G.B., RAY, C. Predicting flux decline in crossflow membranes using artificial neural net-works and genetic algorithms. J. Membr. Sc., 283, 147-157, 2006.
  • [15] SILVA, I.N., FLAUZINO, R.A. An approach based on neural networks for estimation and generaliza-tion of crossflow filtration processes. Applied Soft Computing, 8, 590-598, 2008.
  • [16] SHETTY, G.R., CHELLANM, S. Prediction membrane fouling during municipal drinking drinking water nanofiltration using artificial neural networks. J. Membr. Sc., 217, 69-86, 2003.
  • [17] VALLE, D. B., ARAUJO, P. B. Utilização de redes neurais artificiais para o ajuste dos parâmetros do controlador POD do dispositivo FACTS IPFC. Proceedings of the 9th Latin-American Congress on Electricity Generatiom and Transmission - CLAGTEE 2011, 2011.
Como citar:

FILLETTI, E. R.; SILVA, J. M.; "NEURAL NETWORK MODEL FOR PREDICTION OF THE PERMEATE FLUX OF MACROMOLECULAR SOLUTIONS WITH GUAR AND XANTHAN GUMS IN CROSSFLOW MICROFILTRATION PROCESSING", p. 16390-16397 . In: Anais do XX Congresso Brasileiro de Engenharia Química - COBEQ 2014 [= Blucher Chemical Engineering Proceedings, v.1, n.2]. São Paulo: Blucher, 2015.
ISSN 2359-1757, DOI 10.5151/chemeng-cobeq2014-2029-16295-176882

últimos 30 dias | último ano | desde a publicação


downloads


visualizações


indexações