Principal Component Analysis of Blast Furnace Drainage Patterns

Roche, Mauricio; Helle, Mikko; Saxén, Henrik

doi:10.3390/pr7080519

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…This includes observations concerning the conditions under which the iron-slag level may stay at or above the taphole at the end of the tap. Further experiments will be undertaken for gaining deeper understanding of the behavior of iron and slag in the hearth of the ironmaking blast 29 furnace during tappings, e.g., to explain the observed complex outflow patterns [12,26]. The image-based detection of the liquid levels and volumes can also be used to design experiments where the two phases are automatically supplied at the same rate as the outflows, making it possible to study the bending of stagnant interfaces under different liquid through-flow rates.…”

Section: Discussionmentioning

confidence: 99%

An image analysis-based method for automatic data extraction from pilot draining experiments

Liu

Mondal

Hermanson

et al. 2020

Ironmaking & Steelmaking

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An image analysis-based method is developed to automatically interpret videos of a transparent Hele-Shaw model in studies of blast furnace hearth drainage in pilot scale. By the approach, information from experiments can be extracted for quantitative assessment of the draining phenomena. The algorithm consists of modules for (1) image pre-processing, (2) interface tracking, and (3) bendingpoint detection. The pre-processing step makes preparatory estimates for the next module that extracts the liquid-gas and liquid-liquid interfaces. Finally, bending points of the interfaces are detected and parameters quantifying the interface bending are calculated. The algorithm is illustrated by applying it to a video of a draining experiment, demonstrating how information can be extracted. The method can be used to compile information from a large set of experiments to gain a deeper understanding of the complex two-phase flow in the blast furnace hearth and to quantify the findings for a validation of computational models.

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Section: Discussionmentioning

confidence: 99%

An image analysis-based method for automatic data extraction from pilot draining experiments

Liu

Mondal

Hermanson

et al. 2020

Ironmaking & Steelmaking

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“…[14], using transient 3D CFD based on the volume of fluid (VOF) model. As for the outflows of iron and slag, Roche et al [15] developed a strategy for compressing the information about the outflow rates from a large BF by principal component analysis.…”

Section: Blast Furnacementioning

confidence: 99%

Special Issue on “Process Modeling in Pyrometallurgical Engineering”

et al. 2021

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“…PCA and PCR can possibly be regarded as the most popular and most powerful chemometric tools for process monitoring and quantitative analyses [3][4][5][6]. Initially employed by statisticians to describe the variance and covariance of random variables, PCA is more commonly used in chemometrics to describe determinist relationships among variables, especially in cases where a high degree of collinearity exists or in cases of process datasets with missing values [7].…”

Section: Introductionmentioning

confidence: 99%

A Numerical Procedure for Multivariate Calibration Using Heteroscedastic Principal Components Regression

Monteiro

Feital

Pinto³

2021

Processes

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Many methods have been developed to allow for consideration of measurement errors during multivariate data analyses. The incorporation of the error structure into the analytical framework, usually described in terms of the covariance matrix of measurement errors, can provide better model estimation and prediction. However, little effort has been made to evaluate the effects of heteroscedastic measurement uncertainties on multivariate analyses when the covariance matrix of measurement errors changes with the measurement conditions. For this reason, the present work describes a new numerical procedure for analyses of heteroscedastic systems (heteroscedastic principal component regression or H-PCR) that takes into consideration the variations of the covariance matrix of measurement fluctuations. In order to illustrate the proposed approach, near infrared (NIR) spectra of xylene and toluene mixtures were measured at different temperatures and stirring velocities and the obtained data were used to build calibration models with different multivariate techniques, including H-PCR. Modeling of available xylene–toluene NIR data revealed that H-PCR can be used successfully for calibration purposes and that the principal directions obtained with the proposed approach can be quite different from the ones calculated through standard PCR, when heteroscedasticity is disregarded explicitly.

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Principal Component Analysis of Blast Furnace Drainage Patterns

Cited by 8 publications

References 21 publications

An image analysis-based method for automatic data extraction from pilot draining experiments

An image analysis-based method for automatic data extraction from pilot draining experiments

Special Issue on “Process Modeling in Pyrometallurgical Engineering”

A Numerical Procedure for Multivariate Calibration Using Heteroscedastic Principal Components Regression

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