Extraction of quantitative and image information from flame images of steam boiler burners

Abstract

Several types of detector, such as ultraviolet, infrared, visible light, differential pressure, flame rod, and others, are employed to detect fire flame in power generation plants. However, these flame detectors have some performance problems. This article describes the image-processing method of fire detection as well as neural network modeling. Nowadays, the image-processing technique is broadly applied in industrial fields. The neural network model has strong adaptability and learning capability, and is suitable for pattern classification. The Ulsan Steam Power Generation Plant in Korea was employed as the test field. If this technique can be implemented, boilers will be able to operate more economically and effectively.