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Authors: Katy Ferguson 1 ; Jie Zhang 1 ; Carl Steele 2 ; Colin Clarke 3 and Julian Morris 1

Affiliations: 1 School of Chemical Engineering and Advanced Materials and Newcastle University, United Kingdom ; 2 Sellafield Ltd, United Kingdom ; 3 National Nuclear Laboratory, United Kingdom

ISBN: 978-989-8425-84-3

Keyword(s): Viscosity, Neural networks, Vitrification, Nuclear reprocessing, Modelling, Application.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Domain Applications and Case Studies ; Enterprise Information Systems ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

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.

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Paper citation in several formats:
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

@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},
}

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

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