Neural network modelling of an electrochemical process

Artificial Neural Network (ANN) methodology has gained popularity in chemistry in recent years as a result of its ability to solve problems for various purposes. In particular, ANN shows a very high performance in the modelling of the experimental measurements. To this aim, the trained ANN is used to predict unknown values of measurement system. For a given trial set of parameters, the experimental response may be predicted by the model. In recent decades, several investigations were based on the electrooxidation of D-glucose owing to its many applications such as detection systems (glucose sensors), fuel cells and synthesis of economically interesting products. In the present work based on the electrochemical process modelling, the estimation of peak current densities of the D-glucose electrooxidation on palladium electrode in alkaline medium was investigated as a function of potential sweep rate and D-glucose concentration by using a three layer feed-forward ANN with error propagation learning algorithm.