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.
Neural networks; Enzymes; Modeling; Product inhibition; Substrate inhibition; Cellobiose
