ARTIFICIAL NEURAL NETWORKS BASED ON COMMITTEE MACHINE TO PREDICT THE AMOUNT OF SULFUR AND PHOSPHORUS IN THE HOT METAL OF A BLAST FURNACE

Resumo

Steel is an alloy of iron and carbon containing less than 2% carbon and small amounts of elements such
as silicon, manganese, phosphorus, and sulfur, which together do not exceed 1% of the total. Sulfur and phosphorus
are undesirable elements in steel because they cause brittleness. The best way to control sulfur and phosphorus
content is during the production of cast iron in blast furnace. In the field of simulation and modeling, several
models have been proposed for the simulation of blast furnace, which allow progress and detailed information
about the fluid flow and mass and heat balances of the blast furnace. However, there are few mathematical models
for the prediction of sulfur and phosphorus content. In this context, the main objective of this work was to develop
an artificial neural network for predicting the sulfur and phosphorus content in cast iron. A mathematical model
was developed based on a committee machine using 8 different artificial neural networks simultaneously. The
artificial neural networks with a single hidden layer had neurons varying in 10, 20, 25, 30, 40, 50, 75 and 100
neurons per layer. Pearson's correlation coefficients, RMSE and MAE confirmed that the hidden layer with 25
neurons gave the best results. The conclusion is that high values of mathematical correlation demonstrate the good
statistical performance of ANN and show that the mathematical model is an effective predictor of sulfur and
phosphorus.