氧化铝回转窑烧成带火焰图像识别系统

针对氧化铝回转窑烧成带工况变化复杂，与产品质量指标密切相关的关键过程变量无法实现在线连续检测的难题,模拟传统的人工看火操作过程，提出利用图像处理与模式分类技术对与回转窑工业过程自动控制密切相关的烧成带状态进行自动识别的方法。组建了系统的硬件、软件平台，设计并开发了基于混合数据特征识别的氧化铝回转窑烧成带工况识别软件。该系统成功应用于国内某氧化铝厂，为回转窑的优化控制提供了重要的辅助决策信息。

Flame image recognition system for alumina rotary kiln burning zone

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.