Hybrid Neural Modeling Of Bioprocesses Using Functional Link Networks

The objective of this work was to develop a model for an extractive ethanol fermentation in a simple and rapid way. This model must be sufficiently reliable to be used for posterior optimization and control studies. A hybrid neural model was developed, combining mass and energy balances with neural networks, which describe the process kinetics. To determine the best model, two structures of neural networks were compared: the functional link networks and the feedforward neural networks. The two structures are shown to describe well the process kinetics, and the advantages of using the functional link networks are discussed.98-10010091023Bhat, N., Mcavoy, T.J., (1990) Comp. Chem. Eng., 14, pp. 573-583Hussain, M.A., (1999) Artif. Intell. Eng., 13, pp. 55-68Psichogios, D.C., Ungar, L.H., (1992) AIChE J., 38, pp. 1499-1511Costa, A.C., Alves, T.L.M., Henriques, A.W.S., Maciel Filho, R., Lima, E.L., (1998) Comp. Chem. Eng., 22, pp. S859-S862Costa, A.C., Henriques, A.S.W., Alves, T.L.M., Maciel Filho, R., Lima, E.L., (1999) Braz. J. Chem. Eng., 16, pp. 53-63Zorzetto, L.F.M., Maciel Filho, R., Wolf-Maciel, M.R., (2000) Comp. Chem. Eng., 24, pp. 1355-1360Chen, S., Billings, S.A., (1992) Int. J. Control, 56, pp. 319-346Silva, F.L.H., Rodrigues, M.I., Maugeri, F., (1999) J. Chem. Tech. Biotechnol., 74, pp. 176-182Costa, A.C., Dechechi, E.C., Silva, F.L.H., Maugeri Filho, F., Maciel Filho, R., (2000) Appl. Biochem. Biotechnol., 84, pp. 577-593Andrietta, S.R., Maugeri, F., (1994) Advances in Bioprocess Engineering, pp. 47-52. , Galindo, E. and Ramirez, O. T., eds., Kluwer Academic, DordrechtAlves, J.G.L.F., (1996), MSc thesis, Faculdade de Engenharia de Alimentos, UNICAMP, Campinas, SP, BrazilRumelhart, D.E., Hinton, G.E., Williams, R.J., (1986) Nature, 323, pp. 533-536Henrique, H.M., Lima, E.L., Seborg, D.E., (2000) Chem. Eng. Sci., 55, pp. 5457-5469Henrique, H.M., (1999), MSc thesis, PEQ/COPPE/UFRJ, Rio de Janeiro, RJ, BrazilBillings, S.A., Chen, S., Korenberg, M.J., (1989) Int. J. Control, 49, pp. 2157-2189Cleran, Y., Thibault, J., Cheruy, A., Corrieu, G., (1991) J. Ferm. Bioeng., 71, pp. 356-36

Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling

Neural networks and hybrid models were used to study substrate and product inhibition observed in the enzymatic hydrolysis of cellobiose at 40ºC, 50ºC and 55ºC, pH 4.8, using cellobiose solutions with or without the addition of exogenous glucose. Firstly, the initial velocity method and nonlinear fitting with Statistica<FONT FACE=Symbol>Ò</FONT> were used to determine the kinetic parameters for either the uncompetitive or the competitive substrate inhibition model at a negligible product concentration and cellobiose from 0.4 to 2.0 g/L. Secondly, for six different models of substrate and product inhibitions and data for low to high cellobiose conversions in a batch reactor, neural networks were used for fitting the product inhibition parameter to the mass balance equations derived for each model. The two models found to be best were: 1) noncompetitive inhibition by substrate and competitive by product and 2) uncompetitive inhibition by substrate and competitive by product; however, these models’ correlation coefficients were quite close. To distinguish between them, hybrid models consisting of neural networks and first principle equations were used to select the best inhibition model based on the smallest norm observed, and the model with noncompetitive inhibition by substrate and competitive inhibition by product was shown to be the best predictor of cellobiose hydrolysis reactor behavior