Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace.
No Thumbnail Available
Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Being developed over the centuries, it currently occupies a prominent role in the world production scenario, being the stage
of the process related to the obtaining of hot metal an element of great importance to establish the competitiveness of national steel.
From this perspective, the control of the process of obtaining hot metal is relevant to ensure competitive prices and a sustainable
process. Considering the presented situation, this research developed a committee machine, being three networks to predict each of the
study variables, namely: i) fuel rate; ii) sulfur content in hot metal. The committee machine was developed to model the hot metal during
the operation of a coke blast furnace, according to the input parameters provided. The results obtained by the committee machine were
lower than those of the neural networks acting alone, and the following RMSE values were verified: i) fuel rate: 4.88 (network 1), 4.74
(network 2), 6.14 (network 3) and 4.67 (committee); ii) sulfur content: 0.00915 (network 1), 0.00917 (network 2), 0.00974 (network 3)
and 0.00726 (committee). Considering the results obtained, the model can be used to provide important support in monitoring and
decision making during the operation.
Description
Keywords
Modeling
Citation
ASSIS, P. S.; CARVALHO, L. de A.; IRGALYEV, A. Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace. International Journal of Science and Research, v. 8, n. 12, p. 1492-1495, dez. 2019. Disponível em: <https://www.ijsr.net/archive/v8i12/ART20203638.pdf>. Acesso em: 10 mar. 2020.