Genetic Algorithm based X-Ray Diffraction Analysis for Chemical Control of Aluminium Smelters Baths

Shakhnaz Akhmedova, Igor Yakimov, Aleksandr Zaloga, Sergey Burakov, Eugene Semenkin, Petr Dubinin, Oksana Piksina, Eugene Andryushenko

2015

Abstract

Aluminium production is based on the high-temperature electrolysis of alumina in molten fluoride salts. Part of the fluoride compounds continuously evaporates, which violates the optimal composition of the electrolyte in the electrolytic baths. It causes a technological necessity for regular adjustment of the electrolyte composition by the addition of fluorides according to results of automatic express analysis of the electrolyte. Control of the main composition characteristics is performed automatically by XRD phase analysis of crystallized electrolyte samples. The XRD method, usually used on aluminium smelters, requires periodic calibration with reference samples, whose phase composition is exactly known. The preparation of such samples is a rather complicated problem because samples include 5-6 different phases with variable microcrystalline structure. An alternative diffraction method is the Rietveld method, which does not require reference samples to be used. The method is based on the modelling of the experimental powder patterns of electrolyte samples as the sum of the phase of component powder patterns, calculated from their atomic crystal structure. The simulation includes a refinement of the profile parameters and crystal structure of phases by the nonlinear least squares method (LSM). The problem with the automation of this approach is the need to install a set of initial values of the parameters that can and should be automatically refined by LSM to exact values. To solve this problem, the article proposed an optimization method based on an evolutionary choice of initial values of profile and structural parameters using a genetic algorithm. The criterion of the evolution is the minimization of the profile R-factor, which represents the weighted discrepancy between the experimental and model powder patterns of the electrolyte sample. It is shown that this approach provides the necessary accuracy and complete automation of the electrolyte composition control.

References

  1. Young, R.A., 1993. The Rietveld Method. Oxford University Press, New York.
  2. Feret, F.R., 2008. Breakthrough in Analysis of Electrolytic Bath Using Rietveld-XRD Method. In: Light Metals. pp. 343-346.
  3. Karsten Knorr, 2012. Present progress in fast XRD analysis applying the Rietveld method for bath control. In: The 19th International Symposium and Exhibition of ICSOBA.
  4. Feng, Z.J., Dong, C., 2007. GEST: a program for structure determination from powder diffraction data using a genetic algorithm. In: J. Appl. Crystallogr. Vol. 40, 583 p.
  5. Kenneth , D., Harris, M., 2009. Structure Solution from Powder X-Ray Diffraction Data by Genetic Algorithm Techniques, Applied to Organic Materials Generated as Polycrystalline Products from Solid State Processes. In: Materials and Manufacturing Processes. Vol. 24, pp. 293-302.
  6. Wojciech Paszkowicz, 2013. Genetic Algorithms, a Nature-Inspired Tool: A Survey of Applications in Materials Science and Related Fields: Part II. In: Materials and Manufacturing Processes. Volume 28, Issue 7 (Genetic Algorithms), pp. 708-725.
  7. Yakimov, Y.I., Semenkin, E.S., Yakimov, I.S., 2009. Two-level genetic algorithm for a full profile fitting of X-ray powder patterns. In: Z. Kristallogr. Suppl.30, pp. 21-26.
  8. Solovyov, L.A., 2008. The Derivative Difference Minimization Method. Chapter 10. Powder Diffraction Theory and Practice, ed. R.E. Dinnebier and S.J.L. Billinge. In: Royal Society of Chemistry. 507 ?.
  9. Yakimov, I.S., Zaloga, A.N., Solov'ev, L.A., Yakimov, Y.I., 2012. Method of Evolutionary Structure_Sensitive Quantitative X-Ray Phase Analysis of Multiphase Polycrystalline Materials. In: Inorg. Materials. Vol.48, no.14, pp.1285-1290.
  10. Nicola V.Y. Scarlett et al., 2002. Round Robin on Quantitative phase analysis: samples 2. In: J. Appl. Cryst. Vol. 35, pp. 383-400.
  11. Yakimov, I.S., et al, 2008. Developing industry standard samples of electrolyte aluminum electrolytic cells. In: Standard samples. Vol. 4, pp.34-42.
  12. Electrolytic Bath Standards, Alcan International Ltd., Quebec, Canada (2005).
Download


Paper Citation


in Harvard Style

Akhmedova S., Yakimov I., Zaloga A., Burakov S., Semenkin E., Dubinin P., Piksina O. and Andryushenko E. (2015). Genetic Algorithm based X-Ray Diffraction Analysis for Chemical Control of Aluminium Smelters Baths . In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-122-9, pages 32-39. DOI: 10.5220/0005561900320039


in Bibtex Style

@conference{icinco15,
author={Shakhnaz Akhmedova and Igor Yakimov and Aleksandr Zaloga and Sergey Burakov and Eugene Semenkin and Petr Dubinin and Oksana Piksina and Eugene Andryushenko},
title={Genetic Algorithm based X-Ray Diffraction Analysis for Chemical Control of Aluminium Smelters Baths},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2015},
pages={32-39},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005561900320039},
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 - Genetic Algorithm based X-Ray Diffraction Analysis for Chemical Control of Aluminium Smelters Baths
SN - 978-989-758-122-9
AU - Akhmedova S.
AU - Yakimov I.
AU - Zaloga A.
AU - Burakov S.
AU - Semenkin E.
AU - Dubinin P.
AU - Piksina O.
AU - Andryushenko E.
PY - 2015
SP - 32
EP - 39
DO - 10.5220/0005561900320039