Prediction of combustion states from flame image in a domestic coal burner

Coal is still a strategic fuel for many developing countries. The environmental impact of emissions resulting from the widespread use of coal worldwide is a matter of serious debate. In this perspective, clean coal burning technologies are in demand. In this study, a measurement system that estimates emission from flame images in a domestic coal burner is proposed. The system consists of a charge-coupled device camera, image processing software (real time image acquisition, noise reduction and extracting features) and artificial intelligence elements (classification of features by neural networks). In feature extraction stage, only five flame region features ( G x , G y , trace, L 2 and L ∞ norm) is extracted. G cx and G cy are the instantaneous change of the horizontal and vertical components of center mass of the flame image. These features are new concepts for emission estimation from the flame image. The proposed system makes a difference with its simpler structure and higher accuracy compared to its counterparts previously presented in the literature.