Slopping detection system for LD converters using sound signal digital and image processing

Batista, Leonardo Gonçalves; Salarolli, Pablo F.; Menezes, Ricardo Pimenta de; Ayres, Lucas M.; Pereira, Rogério Passos do Amaral; Cuadros, S. L. Marco Antonio de; Furtado, Henrique Silva

doi:10.1109/induscon.2018.8627206

Cited by 4 publications

(14 citation statements)

References 12 publications

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“…Although the study showed good accuracy in warning the operator, it did not necessarily provide a real-time slag height measurement during the blow. Batista et al created a slop detection method based on sound and image analysis [46]. They described the useful attribute of the sound signal as its ability to ensure the nonoccurrence of a slopping event when the bandwidth-filtered signal was above a threshold limit.…”

Section: Plant-based Studies On Dynamic Control Of Slag Foam Using Ac...mentioning

confidence: 99%

Acoustic Analysis of Slag Foaming in the BOF

Heenatimulla

Brooks

Dunn

et al. 2022

Metals

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The control of slag foam that is produced during the Basic Oxygen Furnace (BOF) process has been the subject of significant research. The behaviour of slag foams is complex. Hence, the control of slag foam in the dynamic process of the BOF is challenging. Acoustic analysis of the BOF is one of the most promising methods for the indirect measurement of slag foam height. This paper reviews different studies on the fundamental behaviour of acoustics in liquid foams and various acoustic studies related to determining the slag foam height during the BOF process. Studies on the BOF have been carried out using both cold water models and plant trials, where acoustic measurements taken directly from the process were analysed. These studies showed that the attenuation of sound through liquid foam was influenced mainly by factors such as viscosity, bubble size, and foam height. Current systems are said to be 70 to 87 per cent accurate in detecting and/or predicting slopping events in the BOF, though there is a lack of systematic data in the literature to fully quantify this accuracy. There have been various attempts to combine sound with vibration and image signals to improve the prediction of slopping events in BOFs. The review substantiates the lack of accuracy of the current systems in determining the slag foam height using acoustic analysis and the need to address fundamental questions about the behaviour of sound in dynamic foam, its reliance on different factors, and the relatability of comparing cold model data to industrial data.

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Section: Plant-based Studies On Dynamic Control Of Slag Foam Using Ac...mentioning

confidence: 99%

Acoustic Analysis of Slag Foaming in the BOF

Heenatimulla

Brooks

Dunn

et al. 2022

Metals

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“…In [3], an automatic system for detecting slopping is presented based on the simultaneous digital processing of acoustic signals from a microphone installed above the converter and an image from a VC installed 27.5 m from the mouth of the converter. The images of the converter's mouth images are captured at 10 frames per second.…”

Section: Complex Technical Control Systems and Information Control Complexesmentioning

confidence: 99%

“…The images of the converter's mouth images are captured at 10 frames per second. Image processing algorithm [3]: extraction of the region of interest (ROI) from the image, i.e., the area containing the slopping; converting an RGB image to HSL; converting the resulting image to black and white; application of the Roberts method to highlight the contours of the image; filtering small particles (noise) in the image; summation of informative image areas; and calculation of the normalized value of the slopping detection index. If this value is above the threshold, then an overshoot is possible.…”

Section: Complex Technical Control Systems and Information Control Complexesmentioning

confidence: 99%

“…In practice, a situation often occurs when the sound level is below the threshold value, and no slopping occurs, which leads to a large number of false alarms. Distortion of the audio signal by noise from external sources is also a problem; thus, the acoustic signal by itself is not considered a reliable indicator for detecting slopping [3].…”

Section: Complex Technical Control Systems and Information Control Complexesmentioning

confidence: 99%

“…A strong, chaotic flame with an area of saturated color close to white is an urgent problem for digital image processing. A strong flame with a low level of foam in the converter is often recognized by this system [3] as slopping, which leads to a false alarm.…”

Section: Complex Technical Control Systems and Information Control Complexesmentioning

confidence: 99%

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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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Slopping index for LD converters based on sound and image data fusion by fuzzy Kalman filter

Menezes

Salarolli

Batista

et al. 2021

Ironmaking & Steelmaking

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The LD (Linz Donawitz) steelmaking process is the most used in the steel industry due to its highvolume capacity and low cost per ton of steel produced. However, the basic oxygen steelmaking process in LD converters is subjected to potential steel charge overflows, often called 'slopping'. Besides yield losses, slopping events can damage the environment and expose employees to danger. More than ever, steelmaking plants need to avoid this type of event to keep producing as environmental impacts are no more tolerable by society. Steelmaking plants already use different methods to monitor and detect slopping events, but they are often limited and unreliable. Therefore, this paper proposes a multi-sensor data fusion process to generate a reliable slopping index to warn operators of potential slopping events and detect the triggered ones. The work is based on sound and image data (67 heats with 27 slopping events) collected on previous trials at a 350-ton LD converter. The Kalman filter was applied as a data fusion agent of two indexes, one resulted from computer vision analysis of the LD converter mouth (image data), the other resulted from digital signal analysis of sound captured on the converter's hood (sound data). Fuzzy sets were applied for adaptative tuning of the Kalman filter to improve the data fusion process. Besides the increase of alarm accuracy and heat classification, the data fusion index worked better on different scenarios and produced a more reliable indicator for a slopping prevention system.

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Slopping detection system for LD converters using sound signal digital and image processing

Cited by 4 publications

References 12 publications

Acoustic Analysis of Slag Foaming in the BOF

Acoustic Analysis of Slag Foaming in the BOF

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

Slopping index for LD converters based on sound and image data fusion by fuzzy Kalman filter

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