Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation of Acid Concentration Model of Cooling and Pickling Process Using Volterra Series Inputs

볼테라 시리즈 입력을 이용한 냉연 산세 라인 산농도 모델 추정

  • 박찬은 (포항공과대학교 전자전기공학과) ;
  • 송주만 (포항공과대학교 전자전기공학과) ;
  • 박태수 (포항공과대학교 전자전기공학과) ;
  • 노일환 (포스코 제어계측연구그룹) ;
  • 박형국 (포스코 제어계측연구그룹) ;
  • 최승갑 (포항공과대학교 엔지니어링대학원) ;
  • 박부견 (포항공과대학교 전자전기공학과)
  • Received : 2015.06.02
  • Accepted : 2015.11.09
  • Published : 2015.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper deals with estimating the acid concentration of pickling process using the Volterra inputs. To estimate the acid concentration, the whole pickling process is represented by the grey box model consists of the white box dealing with known system and the black box dealing with unknown system. Because there is a possibility of nonlinear term in the unknown system, the Volterra series are used to estimate the acid concentration. For the white box modeling, the acid tank solution level and concentration equations are used, and for the black box modeling, the acid concentration is estimated using the Volterra Least Mean Squares (LMS) algorithm and Least Squares (LS) algorithm. The LMS algorithm has the advantage of the simple structure and the low computation, and the LS algorithm has the advantage of lowest error. The simulation results compared to the measured data are included.

Keywords

References

  1. B. Sohlberg, "Hybrid grey box modelling of a pickling process," Control Engineering Practice, vol. 13, no. 9, pp. 1093-1102, 2005. https://doi.org/10.1016/j.conengprac.2004.11.005
  2. G. Budura and C. Botoca, "Nonlinearities identification using The LMS Volterra filter," Communications Department Faculty of Electronics and Telecommunications Timisoara, Bd. V. Parvan, no. 2, 2005.
  3. P. Kittisupakorn, P. Thitiyasook, M. A. Hussain, and W. Daocud, "Neural network based model predictive control for a steel pickling process," Journal of Process Control, vol. 19, no. 4, pp. 579-590, 2009. https://doi.org/10.1016/j.jprocont.2008.09.003
  4. R. D. Nowak, "Volterra filter identification using penalized least squares," Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on, vol. 5, pp. 2813-2816, May 1996.
  5. R. D. Nowak, "Penalized least squares estimation of Volterra filters and higher order statistics," Signal Processing, IEEE Transactions on, vol. 46, no. 2, pp. 419-428, Feb. 1998. https://doi.org/10.1109/78.655426
  6. H. Jang, S.-h. Choi, and J. H. Lee, "A survey on stste estimation of nonlinear systems," Journal of Institute of Control, Robotics and System (in Korean), vol. 20, no. 3, pp. 277-288. https://doi.org/10.5302/J.ICROS.2014.14.9015
  7. H. K. Park, J. H. Lee, and I. H. Noh, "Development of a concentration measurement system for pickling line control," Journal of Institute of Control, Robotics and System (in Korean), vol. 19, no. 10, pp. 891-895, 2013. https://doi.org/10.5302/J.ICROS.2013.13.8007
  8. C. E. Park, J.-M. Song, H. K. Park, P. Park, and S. G. Choi, Cooling and "Estimation model for acid concentration in cooling pickling process," Proc. of 2015 30th ICROS Annual Conference (in Korean), Daejeon, Korea, pp. 316-317, May 2015.