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.

References

  1. Bhat, N. V. and T. J. McAvoy (1990) “Use of neural nets for dynamical modelling and control of chemical process systems”, Computers & Chemical Engineering, 14, 573-583.
  2. Bishop, C. (1995) Neural Networks for Pattern Recognition. Oxford University Press: Oxford.
  3. Bulsari, A. B., (Ed), (1995) Computer-Aided Chemical Engineering, Vol.6, Neural Networks for Chemical Engineers, Elsevier: Amsterdam.
  4. Cybenko, G. (1989) “Approximation by superposition of a sigmoidal function”, Math. Control Signal Systems, 2, 303-314.
  5. Duchesne, M. A., Macchi, A., Lu, D.Y., Hughes, R. W., McCalden, D., J. Anthony, E. J., (2010) Artificial neural network model to predict slag viscosity over a broad range of temperatures and slag compositions - Fuel Processing Technology, Volume 91, Issue 8, Pages 831-836
  6. Girosi, F. and T. Poggio (1990) “Networks and the best approximation property”, Biological Cybernetics, 63, 169-179.
  7. Lennox, B., Rutherford, P., Montague, G. A., Haughin, C., (1998) Case study investigating the application of neural networks for process modelling and condition monitoring - Computers & Chemical Enginerring, Volume 22, Issue 11, Pages 1573-1579
  8. Marquardt, D. (1963) “An algorithm for least squares estimation of nonlinear parameters”, SIAM J. Appl. Math., 11, 431-441.
  9. Narendra, K. S. and K., Parthasarathy (1990) “Identification and control of dynamical systems using neural networks”, IEEE Transactions on Neural Networks, 1, 4-27.
  10. Park, J. and I. W. Sandberg (1991) “Universal approximation using radial basis function networks”, Neural Computation, 3, 246-257.
  11. Pham, D. T., Liu, X., (1995) Neural Networks for Identification, Prediction and Control. Springer-Verlag London Limited. 4th edition
  12. Rumelhart, D. E., G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation”, in Parallel Distributed Processing, (Eds) D. E. Rumelhart and J. L. McClelland, MIT Press, 1986.
  13. Steele, C. J., Dunnet, B., Riley, A. D., Ferguson, K., Gribble, N., Short, R., (2011) Viscosity of simulated nuclear waste vitrified product International Conference on the Chemistry of Glasses and GlassForming Melts, To be published
  14. Zhang, J., Morris, A. J., Martin, E. B., Kiparissides, C., (1998) Prediction of polymer quality in batch polymerisation reactors using robust neural networks Chemical Engineering Journal 69, Pages 135-143
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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