Neural Modeling and Control of a 13C Isotope Separation Process

Vlad Muresan, Mihail Abrudean, Honoriu Valean, Tiberiu Coloşi, Mihaela-Ligia Unguresan, Valentin Sita, Iulia Clitan, Daniel Moga

2015

Abstract

The paper presents a solution for the 13C isotope concentration control inside and at the output of a separation column, solution based on the Internal Model Control strategy. The 13C isotope results from a chemical exchange process carbon dioxide – carbamate, which is a distributed parameter process. In order to model the mentioned process, an original form of the approximating analytical solution which describes the process work in transitory regime is determined. The evolution of the approximating solution depends both on time and on the position from the column height. The reference model of the fixed part of the control structure is implemented using neural networks, representing an original solution due to the fact that a neural model is determined for a distributed parameter process. The controller is, also, implemented using neural networks, its main parameter being adapted in relation to the transducer position change in the separation column. The advantages of using the proposed concentration control strategy consist of: the possibility of controlling the value of the 13C isotope concentration in any point from the separation column height; the improvement of the system performance regarding the settling time; the possibility to reject the effect of the disturbances.

References

  1. Axente, D., Abrudean, M., Bâldea, A., 1994. 15N, 18O, 10B, 13C Isotopes Separation trough Isotopic Exchange, Science Book House.
  2. Borges, R. V., 2011. Learning and Representing Temporal Knowledge in Recurrent Networks. In IEEE Transactions on Neural Networks, Vol. 22, Issue 12, pp. 2409 - 2421.
  3. Colosi, T., Abrudean, M., Unguresan, M.-L., Muresan, V., 2013. Numerical Simulation of Distributed Parameter Processes, Springer.
  4. Dang, H., Rochelle, G. T., 2003. CO2 absorption rate and solubility in monoethanolamine/ piperazine/ water. In Separation Sci. & Tech., Vol. 38 (2), pp. 337-357.
  5. Dugas, R., Rochelle, G., 2009. Absorption and desorption rates of carbon dioxide with monoethanolamine and piperazine. In Energy Procedia, Vol. 1 (1), pp. 1163- 1169.
  6. Golnaraghi, F., Kuo, B. C., 2009. Automatic Control Systems, 9th edition, Wiley Publishing House.
  7. Haykin, S., 2009. Neural Networks and Learning Machines, Third Edition, Pearson Int. Edition.
  8. Li, H.-L., Ju, Y.-L., Li, L.-J., Xu D.-G., 2010. Separation of isotope 13C using high-performance structured packing. In Chemical Engineering and Processing: Process Intensification, Vol. 49 (3), pp. 255-261.
  9. Li, H.-X., Qi, C., 2011. Spatio-Temporal Modeling of Nonlinear Distributed Parameter Systems: A Time/Space Separation Based Approach, 1st Edition, Springer.
  10. Love, J., 2007. Process Automation Handbook, 1 edition, Springer.
  11. Maren, A., Harston, C., Pap, R., 1990. Handbook of Neural Computing Applications, Academic Press.
  12. Muresan, V., Abrudean, M., 2010. Temperature Modelling and Simulation in the Furnace with Rotary Hearth. In Proc. of IEEE AQTR-17th ed., Cluj-Napoca, Romania, pp. 147-152.
  13. Norgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K., 2000. Neural Networks for Modelling and Control of Dynamics Systems, Springer.
  14. Smyshlyaev, A., Krstic, M., 2005. Control design for PDEs with space-dependent diffusivity and timedependent reactivity. In Automatica, Vol. 41, pp. 1601-1608.
  15. Valean H., 1996. Neural Network for System Identification and Modelling. In Proc. of Automatic Control and Testing Conference, Cluj-Napoca, Romania, 23-24 May, pp. 263-268.
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Paper Citation


in Harvard Style

Muresan V., Abrudean M., Valean H., Coloşi T., Unguresan M., Sita V., Clitan I. and Moga D. (2015). Neural Modeling and Control of a 13C Isotope Separation Process . In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-122-9, pages 254-263. DOI: 10.5220/0005549002540263


in Bibtex Style

@conference{icinco15,
author={Vlad Muresan and Mihail Abrudean and Honoriu Valean and Tiberiu Coloşi and Mihaela-Ligia Unguresan and Valentin Sita and Iulia Clitan and Daniel Moga},
title={Neural Modeling and Control of a 13C Isotope Separation Process},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2015},
pages={254-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005549002540263},
isbn={978-989-758-122-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Neural Modeling and Control of a 13C Isotope Separation Process
SN - 978-989-758-122-9
AU - Muresan V.
AU - Abrudean M.
AU - Valean H.
AU - Coloşi T.
AU - Unguresan M.
AU - Sita V.
AU - Clitan I.
AU - Moga D.
PY - 2015
SP - 254
EP - 263
DO - 10.5220/0005549002540263