Open AccessModeling of artificial neural networks for silicon prediction in the cast iron production process
Author(s) -
Wandercleiton Cardoso,
Renzo Di Felice,
Bruunes Dos Santos,
Arthur Nascimento Schitine,
Thiago Augusto Pires Machado,
André Gustavo de Sousa Galdino,
Pedro Vitor Morbach Dixini
Publication year - 2022
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 7
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v11.i2.pp530-538
Subject(s) - artificial neural network , silicon , mean squared error , blast furnace , computer science , ground granulated blast furnace slag , biological system , phase (matter) , dolomite , process (computing) , materials science , process engineering , metallurgy , artificial intelligence , mathematics , chemistry , organic chemistry , cement , engineering , biology , operating system , statistics
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.