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

Katy Ferguson, Jie Zhang, Carl Steele, Colin Clarke, Julian Morris

2011

Abstract

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 1.8x10-4 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.

Paper Citation

in Harvard Style

Ferguson K., Zhang J., Steele C., Clarke C. and Morris J. (2011). MODELLING VITRIFIED GLASS VISCOSITY IN A NUCLEAR FUEL REPROCESSING PLANT USING NEURAL NETWORKS . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 322-325. DOI: 10.5220/0003654003220325

in Bibtex Style

@conference{ncta11,
author={Katy Ferguson and Jie Zhang and Carl Steele and Colin Clarke and Julian Morris},
title={MODELLING VITRIFIED GLASS VISCOSITY IN A NUCLEAR FUEL REPROCESSING PLANT USING NEURAL NETWORKS},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={322-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003654003220325},
isbn={978-989-8425-84-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - MODELLING VITRIFIED GLASS VISCOSITY IN A NUCLEAR FUEL REPROCESSING PLANT USING NEURAL NETWORKS
SN - 978-989-8425-84-3
AU - Ferguson K.
AU - Zhang J.
AU - Steele C.
AU - Clarke C.
AU - Morris J.
PY - 2011
SP - 322
EP - 325
DO - 10.5220/0003654003220325