Burning state recognition of rotary kiln using ELMs with heterogeneous features

Image based burning state recognition plays an important role in sintering process control of rotary kiln. Although many efforts on dealing with this problem have been made over the past years, the recognition performance cannot be satisfactory due to the disturbance from smoke and dust inside the kiln. This work aims to develop a reliable burning state recognition system using extreme learning machines with heterogeneous features. The recorded flame images are firstly represented by various low-level features, which characterize the distribution of the temperature field and the flame color, the local and global configurations. To learn the merits of our proposed flame image-based burning state recognition system, four learner models (ELM, MLP, PNN and SVM) are examined by a typical flame image database with 482 images. Simulation results demonstrate that the heterogeneous features based ELM classifiers outperform other classifiers in terms of both recognition accuracy and computational complexity.