Abstract
Numerous investigations have shown that the performance of control systems in the process industries is not satisfactory. This particularly applies for the steel industry, where it is the norm to perform controller tuning only at the commissioning stage and then never again. A loop that worked well at one time is prone to degradation over time unless regular check and maintenance is undertaken. The field of metal processing continues to provide challenges in the application of process control and supervision at every level of the automation hierarchy, enterprise optimisation and system integration. Techniques successfully used in other process industries have to be adapted to the specific properties and conditions of steel processing, particularly rolling mills, showing high sample rates, varying time delays and semi-continuous operation. This chapter provides a contribution in this direction. Successfully completed industrial case studies and tailored CPM tools are presented. The studies involve the application of different CPM methods to different plants in the rolling area.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The main analysis work was undertaken by Andreas Wolff.
- 2.
The tools described in this section were mainly created by Martina Thormann.
References
Bilkhu TM (2001) Dynamic control of tension, thickness and flatness for a tandem cold mill. AISE Steel Technol 78:49–54
Björklund S (2003) A survey and comparison of time delay estimation methods in linear systems. PhD thesis, Lund Institute of Technology, Sweden
Duncan SR, Allwood JM, Garimella SS (1998) The analysis and design of spatial control systems in strip metal rolling. IEEE Trans Control Syst Technol 6:220–232
Forsman K, Stattin A (1999) A new criterion for detecting oscillations in control loops. In: Proc Europ control confer, Karlsruhe, Germany
Ginzburg WB (1989) Steel-rolling technology: theory and practice. Marcel Dekker, New York
Gorgels F, Jelali M, Lathe R, Mücke G, Müller U, Ungerer W, Wolff A (2003) State of the art and future trends in metal processing control. In: Proc METEC congress (Europ rolling confer), Düsseldorf, Germany, pp 393–402
Jelali M (2006b) Regelkreisüberwachung in der Metallindustrie Teil 2: Anwendungskonzept und Fallstudie. Automatisierungstechnik 54:93–99
Jelali M, Totz O, Börgens R (1998) A new dynamic simulator and an open CACSD environment for rolling mills. Preprints IFAC symp automation in mining, mineral and metal processing, Cologne, Germany, pp 205–209
Jelali M, Müller U, Wolff A, Ungerer W (2001a) Einsatz moderner Regelkonzepte zur besseren Nutzung anlagentechnischer Potentiale. Stahl und Eisen 122(8):35–39
Jelali M, Müller U, Wolff A, Ungerer W (2001b) Advanced control strategies in rolling mills. MPT, Metall Plant Technol Int 3:54–58
Jelali M, Müller U, Wolff A, Ungerer W (2001c) Advanced control strategies for rolling mills. MPT Int 3:54–57
Jelali M, Thormann M, Wolff A, Müller T, Loredo LR, Sanfilippo F, Zangari G, Foerster P (2008) Enhancement of product quality and production system reliability by continuous performance assessment of automation systems (AUTOCHECK). Final report to Contract No RFS-CR-03045, EUR 23205. Office for Official Publications of the European Communities, Luxembourg
Jelali M, Wolff A, Sonnenschein D (2009) Internal Model Control zur Regelung der Bandplanheit in Kaltwalzwerken. Automatisierungstechnik 57:208–216
Keck R, Neuschütz E (1980) German system brings accuracy to flatness measurement. Iron Steel Int 53:215–220
Lackinger C, Nettelbeck H-J, Oemkes H (2002) Die neue Tandemstraße von ThyssenKrupp Stahl im Kaltwalzwerk Beeckerwerth. Stahl und Eisen 122(2):25–32
Markworth M, Polzer J, Ungerer W (2003) Höhere Qualität von Walzprodukten durch komplexe Überwachung. In: Proc VDI-Schwingungstagung “Schwingungsüberwachung und diagnose von Maschinen und Anlagen”, Magdeburg, Germany
Mücke G, Gorgels F (2007) Flatness measurement for high quality cold strip production. MPT Int 1:70–75
Müller T (2005) Regelung Glühofen VZA1. Internal report, EKO Stahl
Polzer J, Markworth M, Ungerer W (2003) New developments in monitoring & diagnosis for rolling mills. In: Proc METEC congress (Europ rolling confer), Düsseldorf, Germany, pp 81–90
Rath G (2000) Model based thickness control of the cold strip process. Diss, University of Leoben, Austria
Rigler GW, Aberl HR, Staufer W, Aistleitner K, Weinberger KH (1996) Improved rolling mill automation by means of advanced control techniques and dynamic simulation. IEEE Trans Ind Appl 32:599–607
Ringwood JV (2000) Shape control systems for Sendzimir steel mills. IEEE Trans Control Syst Technol 8:70–86
Roberts WL (1978) Cold rolling of steel. Marcel Dekker, New York
Sendzimir M (1993) Shape control in cluster mills. www.sendzimir.com
Thornhill NF, Oettinger M, Fedenczuk MS (1999) Refinery-wide control loop performance assessment. J Process Control 9:109–124
Wolff A, Jelali M, Sonnenschein D, Kothe H, Mintken M (2006) New flatness control system at the tandem cold mill of EKO Stahl. In: Internat ATS steelmaking confer, Paris, pp 58–59
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this chapter
Cite this chapter
Jelali, M. (2013). Performance Monitoring of Metal Processing Control Systems. In: Control Performance Management in Industrial Automation. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-4546-2_16
Download citation
DOI: https://doi.org/10.1007/978-1-4471-4546-2_16
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4545-5
Online ISBN: 978-1-4471-4546-2
eBook Packages: EngineeringEngineering (R0)