Monitoring regenerative steel reheating burners using an intelligent flame diagnostic system

Shee Meng Thai, Steven Wilcox, Chee Tan, Zyh Chong, J. Ward, Graham Andrews

Research output: Contribution to journalArticlepeer-review


The present paper describes the use of an intelligent Flame Monitoring System on regenerative steel reheating burners based on direct measurement and analysis of the flame radiation signals. A series of experiments were conducted on a 500 kW furnace fitted with two burners firing in a regenerative manner. The experiments covered a wide range of burner operating conditions including variations in the burner firing-rate and excess air levels. Gas supply to one of the burners was manually reduced in order to simulate burner imbalance. The flame radiation signals were acquired using a fibre-optic based optical instrument incorporating broad ultraviolet, visible and infra-red photodiodes. The correlation between the dynamic flame signals with respect to the excess air level and nitrogen oxides emissions were made using neural network models following off-line analysis of the acquired signals using different signal processing methods, to yield a set of flame features. The present work indicates that the measurement of flame radiation characteristics, coupled with advanced data modelling techniques such as neural network, provides a promising means of monitoring and optimising burner performance.

Original languageEnglish
Pages (from-to)48-58
JournalJournal of the Energy Institute
Issue number1
Publication statusPublished - 1 Feb 2014


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