Flame detection for the steam boiler using neural networks and image information in the Ulsan steam power generation plant

Several types of detectors such as ultraviolet (UV), infrared (IR), visible light (VL), different pressure, flame rod, and others are employed to detect a fire flame in power generation plants. However, these flame detectors have some performance problems. Therefore, this paper describes the image-processing method of fire detection as well as the neural-network modeling. Nowadays, the image-processing technique is broadly applied in the industrial fields. An extracted image information is taken into the inputs of the neural-network model. The neural-network model has strong adaptability and learning capability; therefore, this model can be suitable for pattern classification. The Ulsan Steam Power Generation Plant in Korea is employed as the test field. If this technique can be implemented in physical plants, the boilers can be operated economically and effectively.