Chapter 41. Numerical Methods for Ceramic Reformulation

  1. John B. Wachtman Jr.
  1. Richard L. Lehman

Published Online: 26 MAR 2008

DOI: 10.1002/9780470313916.ch41

Materials & Equipment/Whitewares: Ceramic Engineering and Science Proceedings, Volume 13, Issue 1/2

Materials & Equipment/Whitewares: Ceramic Engineering and Science Proceedings, Volume 13, Issue 1/2

How to Cite

Lehman, R. L. (1994) Numerical Methods for Ceramic Reformulation, in Materials & Equipment/Whitewares: Ceramic Engineering and Science Proceedings, Volume 13, Issue 1/2 (ed J. B. Wachtman), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470313916.ch41

Author Information

  1. Center for Ceramics Research Rutgers University Piscataway, NJ 08855

Publication History

  1. Published Online: 26 MAR 2008
  2. Published Print: 1 JAN 1994

ISBN Information

Print ISBN: 9780470375129

Online ISBN: 9780470313916

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Keywords:

  • reformulation technology;
  • whiteware compositions;
  • ceramics;
  • increasing sophistication;
  • numerical analysis

Summary

Reformulation technology continues to be an essential approach to the modification of whiteware compositions and batch formula. Furthermore, this technology has expanded into other applications in advanced ceramics and glass composition/property relationships. The increasing sophistication and size of whiteware applications requires the solution of ever-larger matrix systems. In the present work, numerical analysis methods suitable for solving reformulation problems are considered and the most efficient methodologies are identified. Most modern reformulation problems can no longer be solved efficiently by direct calculations. Of the many numerical methods available, including Gauss elimination and a host of iterative methods such as Newton's method, the most suitable approach for most whiteware reformulation problems remains linear programming. Recent advances in personal computer software make on-line linear programming analyses possible without leaving the spreadsheet databases which are commonly used for raw material data collection and management. Nonlinear systems require different numerically analysis methods, nearly all of which require iteration and for which convergence is not assured.