Application of artificial neural networks in multifactor optimization of an FIA system for the determination of aluminium

A methodology based on the coupling of experimental design and artificial neural networks (ANNs) is proposed in the optimization of a new flow injection system for the spectrophotometric determination of Al(III) with Arsenazo DBM, which has for the first time been used as chromogenic reagent in the quantitative analysis of aluminium. An orthogonal design is utilized to design the experimental protocol, in which three variables are varied simultaneously. Feedforward-type neural networks with faster back propagation (BP) algorithm are applied to model the system, and then optimization of the experimental conditions is carried out in the neural network with 3-7-1 structure, which have been confirmed to be able to provide the maximum performance. In contrast to traditional methods, the use of this methodology has advantages in terms of a reduction in analysis time and an improvement in the ability of optimization. The method has been applied to the determination of Al(III) in steel samples and provided satisfactory results.