ISSN 2594-357X
9° Simpósio Brasileiro de Aglomeração de Minérios — Vol. 9, Num. 9 (2023)
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This work is one of the results of a successful proof of concept, with the objective to analyze and model the iron ore sintering process on a pilot plant scale, aiming at predicting the final FeO content in the sinter, as a function of the ore blend, fuel, fluxes and other process parameters. The model was developed with real data from a sintering pilot plant, considering around 300 tests with different iron ore mixtures. Multivariate analysis and machine learning techniques were applied, and a final mathematical model with R² greater than 0.92 was obtained, confirming the strength of the proposed methodology.
This work is one of the results of a successful proof of concept, with the objective to analyze and model the iron ore sintering process on a pilot plant scale, aiming at predicting the final FeO content in the sinter, as a function of the ore blend, fuel, fluxes and other process parameters. The model was developed with real data from a sintering pilot plant, considering around 300 tests with different iron ore mixtures. Multivariate analysis and machine learning techniques were applied, and a final mathematical model with R² greater than 0.92 was obtained, confirming the strength of the proposed methodology.
Palavras-chave
Machine Learning; Sintering process; Iron ore, Sinter feed
Machine Learning; Sintering process; Iron ore, Sinter feed
Como citar
Nakandakari, Bianca; Paulo Lopes; Domingues, Alei.
APPLIED MULTIVARIATE ANALYSIS FOR SINTER FEO PREDICTION,
p. 282-295.
In: 9° Simpósio Brasileiro de Aglomeração de Minérios,
São Paulo,
2023.
ISSN: 2594-357X, DOI 10.5151/2594-357X-39579
