Application of artificial neural networks in multifactor optimization of an on-line microwave FIA system for catalytic kinetic determination of ruthenium (III).
A methodology based on the coupling of experimental design and artificial neural networks (ANNs) is proposed in the optimization of a flow injection system for the spectrophotometric determination of Ru (III) with m-acetylchlorophosphonazo (CPA-mA), which has been for the first time used for the optimization of high-performance capillary zone electrophoresis (J. Chromatogr. A 793 (1998) 317). And since it has been applied in many other regions like micellar electrokinetic chromatography, ion-interaction chromatography, HPLC, etc. (J. Chromatogr. A 850 (1999) 345; J. Chromatogr. A 799 (1998) 35; J. Chromatogr. A 799 (1998) 47). An orthogonal design is utilized to design the experimental protocol, in which five variables are varied simultaneously (Anal. Chim. Acta 360 (1998) 227). Feedforward-type neural networks with extended delta-bar-delta (EDBD) algorithm are applied to model the system, and the optimization of the experimental conditions is carried out in the neural network with 5-5-1 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. Under the optimum experimental conditions, Ru (III) can be determined in the range 0.040-0.60 mug ml(-1) with detection limit of 0.03 mug ml(-1) and the sampling frequency of 34 h(-1). The method has been applied to the determination of Ru (III) in refined ore as well as in secondary alloy and provided satisfactory results.
Zeng
Y B
YB
Department of Chemistry, Lanzhou University, Lanzhou 730000, People's Republic of China.
Xu
H P
HP
Liu
H T
HT
Wang
K T
KT
Chen
X G
XG
Hu
Z D
ZD
Fan
B T
BT