Monitoring regenerative steel reheating burners using an intelligent flame diagnostic system

Abstract

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.