Classification of above burden profile using SOM and k-means

Abstract

Blast furnace operators mostly depend on the measurements taken from the inputs and outputs in the control of the process to interpret the information available for vertical and radial distribution of variables, such as, heat load on the boundary and the distribution of the gas across the section of the furnace. Gas distribution inside the furnace is largely dependent on the distribution of the burden inside the furnace and its estimation is a challenge to the blast furnace operators. However, the measurement from the above burden probes gives some insight to the radial gas distribution in the shaft. Design of methods for automatic classification of above burden probe (ABP) profile is thus warranted. The self-organising map (SOM) is an excellent tool in the exploratory phase of data mining. It projects input space on prototypes of a low dimensional regular grid that can be effectively utilised to visualise and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e. clustered. In the present investigation, a model for classification, visualisation and interpretation of ABP profiles has been developed by two stage procedure, i.e. SOM followed by k-means clustering. This classifier has the potential to be a useful tool for operator guidance in daily practice.