Production Control of a Polymerization Plant Based on Production Performance Indicators

• Authors: Dejan Gradišar , Sebastjan Zorzut , Vladimir Jovan
• Languages of publication: EN

Abstracts

EN

The specifics of process manufacturing have a great influence on production management. The focus of process-production control is to maintain stable and cost-effective production within given constraints. The synthesis of production-control structures is thus recognized as one of the most important design problems in process-production management. This article proposes a closed-loop control structure with the utilization of production-performance indicators (pPIs) as a possible solution to this problem. Suggested concept takes into account also economic issues of production. pPIs represent the translation of operating objectives, such as the minimization of production costs, to a reduced set of control variables that can then be used in a feedback control. The idea of production-feedback control using production pPIs as controlled variables was implemented on a procedural model of a production process for a polymerization plant. Preliminary results demonstrate the usefulness of the proposed methodology. At the implementation stage we must be aware that appropriate IT system has to be available which ensures needed online production data.

Keywords

EN

Production management; Production Control; Production performance indicators; Model-based control

SL

Upravljanje proizvodnje; Vodenje proizvodnje; Proizvodni kazalniki učinkovitosti; Prediktivno vodenje

• Publisher: Sciendo
• Journal: Organizacija
• Year: 2008
• Volume: 41
• Issue: 6
• Pages: 207-217

Dates

published

2008-11-01

online

2009-02-23

Contributors

author

Dejan Gradišar

• Institut Jožef Stefan, Jamova 39, 1000 Ljubljana, Slovenia

author

Sebastjan Zorzut

• Instrumentation Technologies, Velika pot 22, 5250 Solkan, Slovenia

author

Vladimir Jovan

• Institut Jožef Stefan, Jamova 39, 1000 Ljubljana, Slovenia

References

• Aller, F., Kandare, G., Blázquez, L.F., Kukanja, D., Jovan, V. & Georgiadis, M.C. (2007). Model-based optimal control of the production of polyvinyl acetate. European congress of chemical engineering - 6, Copenhagen, 16-20 sept. 2007.
• Ahmad, M. M. & Dhafr N. (2002). Establishing and improving manufacturing performance measures. Robotics and Computer Integrated Manufacturing, 18(3):171-176.
• Bemporad, A., Morari, M. & Ricker, L. (2006). Model Predictive Control Toolbox for Use with MATLAB, The Mathworks, Natwick.
• Dangelmaier, W., Fischer, M., Gausemeier, J., Grafe, M., Matysczok, C. & Mueck, B. (2005). Virtual and augmented reality support for discrete manufacturing system simulation. Computers in Industry, 56:371-383.
• Folan, P. & Brown, J. (2005). A review of performance measurement: Towards performance management. Computers in Industry, 56:663-680.
• Forza C. & Salvador, F. (2001). Information flows for high-performance manufacturing, International Journal of Production Economics, 70:21-36.[WoS]
• Ghalayini, A. M., Noble, J. S. & Crowe, T. J. (1996). An integrated dynamic performance measurement system for improving manufacturing competitiveness, International Journal of Production Economics, 48:207-225.
• Gradišar, D., Zorzut, S. & Jovan, J. (2007). Model-based production, Proceedings of the 6th EUROSIM Congress on Modelling and Simulation. Edited by Zupančič, B., Karba, R. & Blažič, S. Ljubljana 9-13 sept. 2007.
• Holt, K. (1999). Management and organization through 100 years. Technovation, 19:135-140.[Crossref]
• Jovan, V. (2001). The integration of management levels in process industries, Proc. of the 5th Italian Conference on Chemical and Process Engineering, Florence, Italy, May 20-23, 1:453-458.
• Larsson T. & Skogestad S. (2000). Plantwide control - a review and a new design procedure. Modeling, Identification and Control, 21(4):209-240.
• Maciejowski, J.M. (1989). Multivariable Feedback Design. Addison-Wesley Publishing Company, Wokingham.
• Morari, M., Arkun, Y. & Stephanopoulos G. (1980). Studies in the synthesis of control structures for chemical processes. AIChE Journal, 26(2):220-232.[Crossref]
• Morari, M. & Lee, J. H. (1999). Model predictive control: Past, present and future. Computers of Chemical Engineering, 23(4-5):667-682.
• Neely, A., Gregory, M. & Platts, K. (1995). Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management, 15(4):80-116.
• Qin, S.J. & Badgwell, T.A. (2003). A survey of industrial model predictive control technology. Control Engineering Practice, 11(7):733-764.[WoS][Crossref]
• Scherer, E. (1995). Approaches to Complexity and Uncertainty of Scheduling in Process Industries: Process Regulation in Highly Auto-mated Systems, Proceedings of IFAC Symposium on Automated Systems Based on Human Skills, 91-95, Berlin, Germany.
• Skogestad, S. (2000). Self-optimizing control: The missing link between steady-state optimization and control. Computers in Chemical Engineering, 24:569-575.
• Skogestad, S. (2002). Plantwide control: Towards a systematic procedure. In ESCAPE 12 Symposium, 12:57-69.
• Stephanopoulos, G. & Ng, C. (2000). Perspectives on the synthesis of plant-wide control structures. J. of Process Control, 10:97-111.
• Suwignjo, P., Bititci, U.S. & Carrie, A.S. (2000). Quantitative models for performance measurement system. Int. J. of Production Economics, 64(1):231-241.
• Tatsiopoulos, I. P. & Panayiotou, N. (2000). The integration of activity based costing and enterprise modelling for reengineering purposes, International Journal of Production Economics, 66:33-44.
• Vicens, E., Alemany, M. E., Andres, C. & Guarch, J. J. (2001). A design and application methodology for hierarchical production planning decision support system in an enterprise integration context, Int. Journal of Production Economics, 74:5-20.

Identifiers

DOI

10.2478/v10051-008-0022-6