A robust hybrid algorithm (neural networks-AGDC) applied to non-isothermal kinetics of consecutive chemical reactions

Abstract

This paper is concerned with the application of a Hybrid Algorithm (HA) to the determination of the Thermodynamic Activation Parameters (ATP) of a kinetic system of first order consecutive reactions. The 8 ATP’s parameters involved in the Arrhenius and Eyring equations have been directly determined from the non-isothermal kinetic data without prior knowledge of the rate constants. AH is constituted by a combination of two algorithms based on different mathematical principles which are sequentially applied. In a first step, a “soft modeling” method of Artificial Neural Networks (ANN) is applied and the obtained values of ATP’s parameters are used as initial estimates of a new optimization algorithm (AGDC) applied in a second stage to improve the values of the final parameters. The great success of HA is the efficient resolution of the ambiguity of the results obtained by ANN. In addition, comparing with the classic algorithms, which present the known weak points, HA offers important advantages: (a) the lack of necessity to know a priori the initial estimates since they are calculated from ANN application, (b) the low probability of being trapped at local minima, saddle points, etc. by means of the exhaustive control and suitable correction of the movement vector during the optimization process, and (c) the simultaneous determination of a higher number of parameters endowed with very different orders of magnitude.