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The traditional artificial recognition methods for the blast furnace dust composition have several disadvantages, including a great deal of information to dispose, complex operation, and low working efficiency. In this article, a multifeature analysis method based on comprehensive image-processing techniques was proposed to automatically recognize the blast furnace dust composition. First, the artificial recognition and feature analysis, which included image preprocessing, Harris corner feature, Canny edge feature, and Ruffle feature analysis, was designed to build the template image, so that any unknown dust digital image could be tested. Second, the composition of coke, microvariation pulverized coal, vitric, ash, and iron from dust would be distinguished according to their different range of values based on the multifeature analysis. The method is valid for recognizing the blast furnace dust composition automatically, and it is fast and has a high recognition accuracy.

Harsh environment in Blast Furnace (BF) poses a big challenge for metallurgical industrial radar measurement of burden surface for ore and coke. The improved signal processing algorithm enhances the real-time performance. Antenna is specially designed for continuous stable signal acquirement, high temperature resistance and anti-dust ability. The experiment results illustrate that the single radar meets the accuracy requirement of solid bulk material. A new BF Burden Surface Measuring System is developed for real-time BF 3-D imaging. Distributed 6-radars array mounted on the top of 2500 m 3 BF. A virtual 3-D imaging is achieved with a reconstruction algorithm of 6-radars array according to the actual shape of BF. 3-D imaging system with 6-radars array provides an improved technique for intelligent control of burden distribution during BF iron making process.