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 n eural networks (ANNs) is proposed in the optimization of a new flow injecti on system for the spectrophotometric determination of Al(III) with Arsenate DBM, which has for the first time been used as chromogenic reagent in the quantitative analysis of aluminium. An orthogonal design is utilized to des ign the experimental protocol, in which three variables are varied simultan eously. Feedforward-type neural networks with faster back propagation (BP) algorithm are applied to model the system, and then optimization of the exp erimental conditions is carried out in the neural network with 3-7-1 struct ure, which have been confirmed to be able to provide the maximum performanc e. In contrast to traditional methods, the use of this methodology has adva ntages in terms of a reduction in analysis time and an improvement in the a bility of optimization. The method has been applied to the determination of Al(III) in steel samples and provided satisfactory results.