-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.
The prediction by a mathematical model of the separation of uranium isotopes using a gas centrifuge process is a hard task. The gas motion can be described by analytical or numerical solutions of the system of equations defined by the equation of continuity, the Navier-Stokes equation and the equation of energy. However, these calculations cannot be performed for actual centrifuges.Neural networks are an alternative for modelling complex problems that show too many difficulties to be solved by phenomenological models.The authors propose the use of neural networks for the simulation and prevision of the separative and operational parameters of a gas centrifuge separating uranium isotopes. The results from the uranium separation experiments (Zippe data) are compiled and presented to the neural network in the learning and testing processes. The prediction using the neural network model shows good agreement with the experimental data.
-Neural networks are an attractive alternative for modeling complex problems with too many difficulties to be solved by a phenomenological model. A feed-forward neural network was used to model a gas-centrifugal separation of uranium isotopes. The prediction showed good agreement with the experimental data. An optimization study was carried out. The optimal operational condition was tested by a new experiment and a difference of less than 1% was found.
The prediction by a mathematical model of the separation of uranium isotopes using a gas centrifuge process is a hard task. The gas motion can be described by analytical or numerical solutions of the system of equations defined by the equation of continuity, the Navier-Stokes equation and the equation of energy. However, these calculations cannot be performed for actual centrifuges.Neural networks are an alternative for modelling complex problems that show too many difficulties to be solved by phenomenological models.The authors propose the use of neural networks for the simulation and prevision of the separative and operational parameters of a gas centrifuge separating uranium isotopes. The results from the uranium separation experiments (Zippe data) are compiled and presented to the neural network in the learning and testing processes. The prediction using the neural network model shows good agreement with the experimental data.
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