Artificial Neural Network Model for Predict of Silicon Content in Hot Metal Blast Furnace
| Autor: | Sayd Farage David, Marcelo Lucas Pereira Machado, Felipe Farage David |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Blast furnace
Materials science Silicon Artificial neural network business.industry Mechanical Engineering 020208 electrical & electronic engineering Metallurgy Process (computing) chemistry.chemical_element 02 engineering and technology Condensed Matter Physics Adiabatic flame temperature Metal chemistry Mechanics of Materials visual_art Content (measure theory) 0202 electrical engineering electronic engineering information engineering visual_art.visual_art_medium 020201 artificial intelligence & image processing General Materials Science Reduction (mathematics) Process engineering business |
| Zdroj: | Materials Science Forum. 869:572-577 |
| ISSN: | 1662-9752 |
| Popis: | The growing focus on the efficiency of the reduction process in blast furnace generates an alteration in the way they operate. This modifies the conditions of transfer of silicon for the hot metal and can cause problems in the added value of your product. To evaluate the changes of the operational parameters of the reduction on the conditions of transfer of silicon process a mathematical model based on artificial neural networks has been implemented. Through this model it was possible to predict the silicon content to determine the influence of each operational parameter. Artificial neural networks were able to predict the silicon content through parameters of the reduction in blast furnace process, and this was verified by the precision of this model. The ANN showed that Theoretical flame temperature, Pressure blow and Coke rate have a positive influence on the silicon content in hot metal, and the Hot metal rate is inversely proportional to the silicon content of the hot metal. |
| Databáze: | OpenAIRE |
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