13 research outputs found
Neural network modeling of the photocatalytic degradation of 2,4-dihydroxybenzoic acid in aqueous solution
Artificial neural networks have been used for modeling the TiO photocatalytic degradation of 2,4-dihydroxybenzoic acid, chosen as a model water contaminant, as a function of the
concentrations of substrate and catalyst. The experimental design methodology was applied to the choice of an appropriate set of experiments well distributed in the experimental region (Doehlert
uniform array). Contrary to a classical treatment of the data, based on apparent rate constants modeled by a quadratic polynomial function, neural network analysis of the same experimental data
does not require the use of any kinetic or phenomenological equations and allows the simulation and the prediction of the pollutant degradation as a function of irradiation time, as well as
prediction of reaction rates, under varying conditions within the experimental region
Detection of outliers in a gas centrifuge experimental data
Isotope separation with a gas centrifuge is a very complex process. Development and optimization of a gas centrifuge requires experimentation. These data contain experimental errors, and like other experimental data, there may be some gross errors, also known as outliers. The detection of outliers in gas centrifuge experimental data is quite complicated because there is not enough repetition for precise statistical determination and the physical equations may be applied only to control of the mass flow. Moreover, the concentrations are poorly predicted by phenomenological models. This paper presents the application of a three-layer feed-forward neural network to the detection of outliers in analysis of performed on a very extensive experiment
Using global optimization for a microparticle identification problem with noisy data
“The original publication is available at www.springerlink.com”. Copyright Springer. DOI: 10.1007/s10898-004-1943-0Peer reviewe