Artificial Neural Network for Predicting Silicon Content in the Hot Metal Produced in a Blast Furnace Fueled by Metallurgical Coke

Abstract

The main production route for cast iron and steel is through the blast furnace. The silicon content in cast iron is an important indicator of the thermal condition of a blast furnace. High silicon contents indicate an increase in the furnace's thermal input and, in some cases, may indicate an excess of coke in the reactor. As coke costs predominate in the production of cast iron, tighter control of the silicon content therefore has economic advantages. The main objective of this article was to design an artificial neural network to predict the silicon content in hot metal, varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, 100, 125 , 150, 170 and 200 neurons. In general, all neural networks showed excellent results, with the network with 30 neurons showing the best results among the 12 modeled networks. The validation of the models was confirmed using the Mean Square Error (MSE) and Pearson's correlation coefficient. The cross-validation technique was used to re-evaluate the performance of neural networks. In short, neural networks can be used in practical operations due to the excellent correlations between the real values ​​and those calculated by the neural network.