Abstract
A methodology based on the coupling of experimental design and artificial neural networks (ANNs) was proposed in the optimization of a new on-line microwave flow injection system (FIA) for the determination of ruthenium, grounded on its catalytic effect on the oxidation of dibromocarboxyarsenazo (DBM-AsA) by potassium periodide under the microwave irradiation. The response function (RF) used was a weighted linear combination of two variables related to sensitivity and sampling rate. A neural network with extended delta-bar-delta (EDBD) learning algorithm was applied to predict the maximal RF, according to which the optimized conditions were obtained. The optimized new on-line microwave FIA system is able to determine ruthenium in 5–200 ng ml −1 range with a detection limit of 2.1 ng ml −1 and a recovery of 94.6%. A sampling rate of 58 h −1 was obtained. 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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
