Neural networks for the identification and control of blast furnace hot metal quality

The operation and control of blast furnaces poses a great challenge because of the difficult measurement and control problems associated with the unit. The measurement of hot metal composition with respect to silica and sulfur are critical to the economic operation of blast furnaces. The measurement of the compositions require spectrographic techniques which can be performed only off line. An alternate technique for measuring these variables is a Soft Sensor based on neural networks. In the present work a neural network based model has been developed and trained relating the output variables with a set of thirty three process variables. The output variables include the quantity of the hot metal and slag as well as their composition with respect to all the important constituents. These process variables can be measured on-line and hence the soft sensor can be used on-line to predict the output parameters. The soft sensor has been able to predict the variables with an error less than 3%. A supervisory control system based on the neural network estimator and an expert system has been found to substantially improve the hot metal quality with respect to silicon and sulfur. (C) 2000 Elsevier Science Ltd. All rights reserved.