Development of an intelligent flame monitoring system for steel reheating burners

Abstract

This article describes the development of a system to indirectly monitor the combustion characteristics of individual burners based on measurement and analysis of the signals detected from photodiodes detecting flame radiation signals. A series of experiments were conducted on a 500 kW pilot-scale furnace and on two 4 MW industrial burners located in two steel reheating furnaces. The flame radiation signals were monitored using a lens that transmitted the flame radiation to ultraviolet, visible and infrared photodiodes through a trifurcated optical fibre. The experiments covered a wide range of burner operating conditions including; variations in the burner load and excess air levels and simulations of burner imbalance. The relationships between the dynamic flame radiation signals and the burner operating parameters and conditions were made off-line using neural network models. The present work indicates that the measurement of flame radiation characteristics, coupled with neural networks, provides a promising means of monitoring and adjusting burner performance.