Title

APPLIED MULTIVARIATE ANALYSIS FOR SINTER FEO PREDICTION

APPLIED MULTIVARIATE ANALYSIS FOR SINTER FEO PREDICTION

Authorship

Lopes, Paulo; Domingues, Alei; Nakandakari, Bianca

Lopes, Paulo

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Domingues, Alei

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Nakandakari, Bianca

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DOI

10.5151/2594-357X-39579

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Abstract

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.

Keywords

Machine Learning; Sintering process; Iron ore, Sinter feed

Machine Learning; Sintering process; Iron ore, Sinter feed