# 산세라인 자동화를 위한 농도 측정 시스템 개발

• 박형국 (POSCO 시스템설계연구그룹) ;
• 이종현 (POSCO 시스템설계연구그룹) ;
• 노일환 (POSCO 시스템설계연구그룹)
• Accepted : 2013.09.02
• Published : 2013.10.01

#### Abstract

This paper proposes the development of a new method for online analysis which measured acid concentration in a pickling line. Pickling is the most important step to remove surface scale layers and is strongly depending on the exactly controlled pickling liquor composition. Today, there is no feasible system available for the online control of pickling lines. Within this paper, new methods for online analysis of pickling liquors have been tested and implemented into an overall pickling process control tool. This method measured simultaneously the hydrochloric acid and iron ion concentration in a solution of hydrochloric acid by measuring the ultrasonic speed, the solution temperature, and the electrical conductivity. Experimental results showed excellent precision and the measurement error was ${\pm}2g/l$ compared with the neutralization titration measurement.

#### References

1. H. K. Park, J. H. Lee, and I. H. Noh, "Automatic measurement of acid concentration in pickling line," Automatica (in Korean), vol. 42, no. 5, pp. 328-329, 2013.
2. A. Chattopadhyay and T. Chanda, "Role of silicon on oxide morphology and pickling behavior of automotive steels," Scripta Materialia, vol. 58, pp. 882-885, 2008. https://doi.org/10.1016/j.scriptamat.2008.01.006
3. B. J. Goode, R. D. Jones, and J. N. H. Howells, "Kinetics of pickling of low carbon steel," Ironmaking and Steelmaking, vol. 23, no. 2, pp. 164-170, 1996.
4. W. Daosud, P. Thitiyasook, A. A. Wichanop, P. Kittisupakorn, and M. A. Hussain, "Neural network inverse model-based controller for the control of a steel pickling process," Computers & Chemical Engineering, vol. 29, no. 15, pp. 2110-2119, 2005. https://doi.org/10.1016/j.compchemeng.2005.06.007
5. P. Kittisupakorn, P. Thitiyasook, M. A. Hussain, and W. Daosud, "Neural network based model predictive control for a steel pickling process," Journal of Process Control, vol. 19, pp. 579- 590, 2009. https://doi.org/10.1016/j.jprocont.2008.09.003
6. Z. He, Fei "Acid concentration prediction model of steel pickling process based on orthogonal signal correction and robust regression" Journal of University of Science and Technology Beijing, vol. 35, no. 2, pp. 242-248, Feb. 2013.
7. B. Sohlberg, "Hybrid grey box modeling of a pickling process," Control Engineering Practice, vol. 13, pp. 1093-1102, 2005. https://doi.org/10.1016/j.conengprac.2004.11.005
8. S.-M. Jung, Y.-M. Cho, H.-G. Na, and D.-J. Min, "Quantitative chemical analysis of pickling solutions by X-ray fluorescence spectrometry," X-ray Spectrometry, vol. 38, no. 3, pp. 185-190, 2007.
9. G. M. Kang, K. Lee, H. Park, J. Lee, Y. Jung, K. S. Kim, B. H. Son, and H. K. Park, "Quantitative analysis of mixed hydrofluoric and nitric acids using Raman spectroscopy with partial least squares regression," Talanta, vol. 81, no. 15, pp. 1414-1417, 2010.
10. D. D. Weerstra, "On-line rolling oil and pickling acid concentration measurement using ultrasonic," Iron and Steel Engineer, vol. 75, no. 12, pp. 35-37, Dec. 1998.
11. N. Toshihiko, "Continuous acid control system for steel strip pickling lines," SEAISI Quarterly, vol. 31, no. 4, pp. 10, Dec. 2002.

#### Cited by

1. Estimation of Acid Concentration Model of Cooling and Pickling Process Using Volterra Series Inputs vol.21, pp.12, 2015, https://doi.org/10.5302/J.ICROS.2015.15.0088