Chapter 10. Advanced Control of Glass Tanks Using Simulation Models and Fuzzy Control

  1. John Kieffer
  1. H. P. H. Muysenberg1,
  2. R. A. Bauer1 and
  3. E. G. J. Peters2

Published Online: 26 MAR 2008

DOI: 10.1002/9780470294468.ch10

A Collection of Papers Presented at the 58th Conference on Glass Problems: Ceramic Engineering and Science Proceedings, Volume 19, Issue 1

A Collection of Papers Presented at the 58th Conference on Glass Problems: Ceramic Engineering and Science Proceedings, Volume 19, Issue 1

How to Cite

Muysenberg, H. P. H., Bauer, R. A. and Peters, E. G. J. (1998) Advanced Control of Glass Tanks Using Simulation Models and Fuzzy Control, in A Collection of Papers Presented at the 58th Conference on Glass Problems: Ceramic Engineering and Science Proceedings, Volume 19, Issue 1 (ed J. Kieffer), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470294468.ch10

Author Information

  1. 1

    TNO-TPD-Glass Technology Research, Eindhoven, Netherlands

  2. 2

    TNO Glass Technology USA, Columbus, Ohio

Publication History

  1. Published Online: 26 MAR 2008
  2. Published Print: 1 JAN 1998

ISBN Information

Print ISBN: 9780470375563

Online ISBN: 9780470294468

SEARCH

Keywords:

  • glass tanks;
  • simulation models;
  • fuzzy control;
  • advanced control;
  • optimal glass quality

Summary

With the current improvements in the capabilities of mathematical simulation models, these models can be used for control applications, opening a new methodology for controlling glass furnaces for optimal glass quality. The method is based on three steps. From classical theories a set of basic models can be derived that roughly describes the relationship between furnace design, glass properties, and operation, with settings on the one hand and the glass quality on the other. These relations are simplified, but they form a good basis for designing control strategies. This type of model can be fine-tuned using a detailed mathematical simulation model. Existing nonlinearities, can be built into these simplified models by employing adaptive neuro-fuzzy interference system techniques. The resulting controller is very transparent and robust. For setpoint changes the controller is employed as a feed-forward controller. An example for color change is shown.