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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Pedro Vitor Morbach Dixini
Federal Institute of Espirito Santo
Computer Science & IT Engineering

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Modeling of artificial neural networks for silicon prediction in the cast iron production process Wandercleiton Cardoso; Renzo di Felice; Bruna Nunes dos Santos; Arthur Nascimento Schitine; Thiago Augusto Pires Machado; André Gustavo de Sousa Galdino; Pedro Vitor Morbach Dixini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp530-538

Abstract

The main way to produce cast iron is in the blast furnace. In the production of hot metal, the control of silicon is important. Alumina and silica react chemically with limestone and dolomite to form blast furnace slag. In this work, 12 artificial neural networks (ANNs) were modeled with different numbers of neurons in each hidden layer. The number of neurons varied between 10 and 200 neurons. ANNs were used to predict the silicon content of hot metal produced. The ANN with 30 neurons showed the best performance. In the test phase, the mathematical correlation was 97.5% and the mean square error (MSE) was 0.0006, and in the cross-validation phase, the mathematical correlation was 95.5% while the MSE was 0.00035.
Co-Authors André Gustavo de Sousa Galdino Arthur Nascimento Schitine Bruna Nunes dos Santos Renzo di Felice Thiago Augusto Pires Machado Wandercleiton Cardoso