In the rotary kiln alumina production process

because of the complexity and variability of rotary kiln burning zone conditions

some important quality index related process parameters can not be detected continuously on-line. Detecting the different burning zone conditions on-line is a key factor for the whole process automation of alumina industry. The current method depends on flame observation by naked eye. In order to realize automated recognition of burning zone conditions

a method which learned experience and knowledge from naked eye observation was proposed to recognize burning zone conditions by utilizing the image processing technique and pattern classification method. At first

features were extracted from flame images of rotary kiln burning zone and were combined with some important process parameters to constitute a hybrid feature vector. Then a model with a binary tree based SVM (support vector machine) was constructed. At last

a flame image recognition system was developed. The system was successfully applied to a domestic alumina plant

and good economic benefit was realized.