Ozonation Kinetics of Acid Red 27 Azo Dye: A novel methodology based on artificial neural networks for the determination of dynamic kinetic constants in bubble column reactors

Abstract

A procedure for the determination of initial parameter values for quadratically convergent optimization methods is proposed using artificial neural networks coupled with a non-stationary gas-liquid reaction model. The evaluation of the regression and the mean squared error coefficients of the neural network during its training process allow the parameter sensitivity analysis of the gas-liquid model. This analysis examines how many and which parameters of the model will be available depending on the observable information of the mathematical model. Numerical simulations show the relevance of the initial values and the non-linearity of the objective function. The methodology has been applied to the study of the reaction of the azo-dye Acid Red 27 with ozone in acid media. The rate constant is in the order of (1.6 +-0.1) 10^3M^(-1) s ^(-1) under the experimental conditions.J. Ferre-Aracil acknowledges the support of the doctoral fellowship from the Universitat Politecnica de Valencia (UPV-PAID-FPI-2010-04).Ferre Aracil, J.; Cardona, SC.; Navarro-Laboulais, J. (2015). Ozonation Kinetics of Acid Red 27 Azo Dye: A novel methodology based on artificial neural networks for the determination of dynamic kinetic constants in bubble column reactors. Chemical Engineering Communications. 202(3):279-293. https://doi.org/10.1080/00986445.2013.841146S279293202