Model-based slopping warning in the LD steel converter process

Evestedt, Magnus; Medvedev, Alexander

doi:10.1016/j.jprocont.2009.01.002

Cited by 19 publications

(20 citation statements)

References 22 publications

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“…Moreover, other methods were developed based on acoustic, as [21] and [22] present a method that employed signals of an audiometer. These signals were registered and analyzed for their measurement of slag levels, on having raised levels, the signal starts to be reduced in a gradual form.…”

Section: Related Workmentioning

confidence: 99%

Feature Descriptors Applied to Slag Characterization on Casting Process

Romero-González¹,

Herrera-Navarro²,

Jiménez-Hernández³

et al. 2020

IJISME

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Vision systems are increasingly entering the field of metallurgy, carrying out operations where a human operator is not possible due to the process conditions. The purpose of these systems is the monitoring and control of the process to improve the quality and manufacturing of the products. Nevertheless, the amount of slag, the presence of gases and high temperatures are the main problems that make this task difficult. In this proposal the characterization of the slag is treated, through the analysis of the light changes with the functions of Fourier and Gabor, which allow to identify or locate the location of the slag in the material, so that, in future works the slag It can be segmented, measured or used to detect the level of the metal in the refractory. In addition, results obtained when evaluating sensitivity and precision curves are presented, with which the information recovered by the algorithms is evaluated.

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Section: Related Workmentioning

confidence: 99%

Feature Descriptors Applied to Slag Characterization on Casting Process

Romero-González¹,

Herrera-Navarro²,

Jiménez-Hernández³

et al. 2020

IJISME

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“…Major attempts to diminish and prevent slopping have been focused in three main areas:  Theoretical characterization and modeling of slopping and occurrence probability  Measurement devices that detect the onset of slopping  Process changes in real time to diminish the effect of slopping Slopping is a complex phenomenon dependant on a long list of variables. Those that have been documented include [2,3]:…”

Section: Module 2: Slopping On a Bofmentioning

confidence: 99%

TENOVA’S INTELLIGENT i BOF® TECHNOLOGY, A MODULAR PACKAGE FOR BOF PROCESS IMPROVEMENT

Vázquez¹

2014

ABM Proceedings

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This paper will provide an overall technology update on Tenova's modular i BOF ® technology package for improved endpoint detection (Carbon and Temperature), slop detection and auto-tapping control, with a primary focus will be on Tenova's Slop Detection System (SDS). Accurate Slopping prediction is a critical tool in the operation of a BOF. It provides the steelmaker with an additional protective system to increase yield & productivity while reducing operating costs. Tenova Goodfellow's i BOF ® slop-detection technology uses lance vibration analysis with real-time alerts to give steel makers advance warning of the onset of a slop and a measurement of the slop severity. The system is designed to provide direct feedback control of lance position and oxygen flow rate, for rapid mitigation of potential slopping events. This paper will provide a summary on how the i BOF ® technology package has been implemented, including trials and general results.

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“…In [1] a method is described for the automatic detection of slopping based on indirect indications based on processing an acoustic signal to estimate the level of slag in the converter (noise during the measurement is considered as white noise, having a normal distribution law) and based on the use of a simplified model (linear regression), describing the relationship between the waste gas flow rate, pressure, and the resulting estimate of the foaming slag level. The parameters of this model are determined by the least squares method and updated in real time.…”

Section: Introductionmentioning

confidence: 99%

Designing an Intelligent Control System for a Basic Oxygen Furnace Based on Computer Vision

Trofimov

2021

J. Comput. Syst. Sci. Int.

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The structure of an intelligent control system for a converter, which includes a subsystem for recognizing and evaluating the slopping of a gas-slag-metallic emulsion in real time based on computer vision by explicit features and a subsystem for predicting slopping by indirect signs, which are most often used to monitor the oxygen-converter process, is proposed. Procedures for forecasting and detecting slopping based on artificial neural networks and a precedent approach are developed. The morphological signs of slopping and the factors influencing their occurrence are described.

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Model-based slopping warning in the LD steel converter process

Cited by 19 publications

References 22 publications

Feature Descriptors Applied to Slag Characterization on Casting Process

Feature Descriptors Applied to Slag Characterization on Casting Process

TENOVA’S INTELLIGENT i BOF® TECHNOLOGY, A MODULAR PACKAGE FOR BOF PROCESS IMPROVEMENT

Designing an Intelligent Control System for a Basic Oxygen Furnace Based on Computer Vision

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