Forecasting heat levels in blast furnaces using a neural network model

Heat level is one of the important factors influencing the stable operation of blast furnaces, and it is especially important to accurately forecast d ecreasing heat levels in order to stabilize the heat level. A forecasting model for decreasing heat levels which occur accompanied with a sudden rising of wall temperatures has been developed using neural netwo rk technology. Wall temperatures are measured at various points in the vert ical and circular directions. Temperature rising points are measured as a d istributed pattern, and neural network technology is used in order to recog nize this distributed pattern. Neural network models are classified into two groups according to their lea rning style, one is called the supervised learning model and the other, the unsupervised learning model. The operators notice that a decrease in heat level sometimes occurs after a rise in wall temperature, but there is no kn owledge of what patterns cause the heat level decrease, which means there i s no teaching data for the supervised model. The forecasting model is built using one of the unsupervised neural network models, the self organization feature maps model, which recognizes and classifies the wall temperature r ising patterns. A new method of shift invariant recognition has been develo ped in order to put circularly shifted wall temperature rising patterns tog ether in a class. It has been established that the heat level forecasting model using the cla ssified wall temperature pattern gives better forecasting accuracy for heat level decrease than a forecasting model using the total amount of wall tem perature rising points. Furthermore, this heat level forecasting model, whi ch uses a classified wall temperature pattern and solution loss C, has suff icient accuracy for heat level operation guidance.