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This paper presents a study of using neural networks to model the viscosity of simulated vitrified highly active waste over a range of temperatures and compositions. Vitrification is the process of incorporating the highly active liquid waste into the glass by chemically changing the structure of the glass for nuclear fuel reprocessing. A methodology is needed to determine how the viscosity will change as a result of a new feed. Feed forward neural networks are used to model the viscosity of new product glasses. The results are very promising, with a Mean Squared Error (MSE) of 1.8x10 on the scaled unseen validation data, highlighting the high accuracy of the model. Sensitivity analysis of the developed model provides insight on the impact of composition on viscosity.

MODELLING VITRIFIED GLASS VISCOSITY IN A NUCLEAR FUEL REPROCESSING PLANT USING NEURAL NETWORKS