ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF KINETIC ANALYTICAL PARAMETERS

The suitability of artificial neural networks for estimating kinetic a nalytical parameters for first-order reactions by using real kinetic d ata acquired after a short reaction time is demonstrated. The optimal reaction time region and its associated number of inputs are the two k ey parameters for obtaining as suitable network as possible. Noise in the transient signal was found to affect the performance of the neural network as well as the size of the training set. The trained network estimated kinetic analytical parameters with a % SEP of 2.14, which is much smaller than those provided by parametric methods such as NLR an d PCR.