EM - Escola de Minas

URI permanente desta comunidadehttp://www.hml.repositorio.ufop.br/handle/123456789/6

Notícias

A Escola de Minas de Ouro Preto foi fundada pelo cientista Claude Henri Gorceix e inaugurada em 12 de outubro de 1876.

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Filtros

2018 - 20192

Configurações

Autor: search.filters.author.Carvalho, Leonard de Araújo

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
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    Artificial neural network-based committee machine for predicting fuel rate and sulfur contents of a coke blast furnace.
    (2019) Assis, Paulo Santos; Carvalho, Leonard de Araújo; Irgaliyev, A.
    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.
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    Artificial neural networks to prediction fuel rate in the blast furnace operation.
    (2018) Carvalho, Leonard de Araújo; Assis, Paulo Santos
    This paper proposes the use of artificial neural networks for the prediction of fuel consumption in the blast furnace. For this purpose, a dataset of 270 records, with 19 input variables were considered, based on the historical data of operation from the years 2014 to 2017 of a blast furnace of a Brazilian steel mill, and it was verified that model presented good results with correlation coefficient of 0.837, consisting of an input layer with 19 neurons, intermediate layer with 19 neurons and output layer with 1 neuron.