Multistep Forecasting Models of the Liquid Level in a Blast Furnace Hearth

Gomes, Flávio Silva Vitorino; Côco, Klaus Fabian; Salles, José Leandro Félix

doi:10.1109/tase.2016.2538560

Cited by 26 publications

(8 citation statements)

References 28 publications

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“…It could also be used for calibration of alternative estimation techniques. Other methods to track the liquid levels include measuring the electromotive force (emf) between two electrodes in the hearth lining or on the shell . The mechanisms behind the generation of the voltage are not fully understood, but in many furnaces the emf signals show a time evolution that presumably reflects the iron or the slag level in the hearth.…”

Section: Introductionmentioning

confidence: 99%

On‐Line Estimation of Liquid Levels in the Blast Furnace Hearth

Roche

Helle

Stel

et al. 2018

steel research int.

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An on-line model of the liquid levels in the blast furnace hearth is developed based on mass balances, estimates of the production rates, and measurements of the outflow rates of iron and slag. To consider differences arising in the hearth when different tapholes are operated, the hearth is divided into m regions, characterized by individual iron and slag levels. The pools communicate with each other by cross-flow of iron and slag. To prevent drift in the liquid level estimates, a correction procedure is developed to make the levels stay within reasonable bounds. The model is applied to measurement data from a three-taphole blast furnace and is demonstrated to provide an on-line view of the in-furnace conditions. The liquid level estimates as well as the required corrections are illustrated and analyzed for cases with different model parameters, and an explanation of the required corrections is provided. The model is also evaluated with respect to its ability to predict the end of the taps. Finally, conclusions concerning the validity of the model are drawn and possible future developments of it are outlined.

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Section: Introductionmentioning

confidence: 99%

On‐Line Estimation of Liquid Levels in the Blast Furnace Hearth

Roche

Helle

Stel

et al. 2018

steel research int.

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“…Comparing with other hearth state monitoring methods, the EMF technique maintains its own characteristics in the development process, for example the signal of the EMF sensor, which is believed to originate from the thermal and chemical reactions occurring in the hearth [5][6][7], while the signal of the thermocouple originates from the thermoelectric effect; the above is the electromotive force generative mechanism difference between the EMF technique and the thermocouple. Comparing with the other methods reported in References [4,[11][12][13][14], the former concentrates on the generation of EMF noise and the solution of the noise, the latter are all mathematical models including CFD and (or) DEM to simulate the tapping procedure. Therefore, the EMF technique can obtain the liquid level information more quickly and actually.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Liquid Level of Blast Furnace Hearth and Torpedo Ladle by Electromotive Force Signal

Liu

Zan

et al. 2018

Metals

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The state of a blast furnace hearth, especially the liquid level of hot metal and slag during the tapping process, is of crucial importance with respect to a long campaign blast furnace. In practice, the state of the hearth is evaluated mainly by the experience of operators. In this paper, the electromotive force (EMF) is used to monitor the liquid level of a laboratory scale of blast furnace hearth and the effect of liquid level, EMF sensors position and the thickness of refractory on EMF signals are tested using a single layer of water and double layers of water and oil. After laboratory experiments, the electromotive force (EMF) is used to monitor the liquid level of torpedo ladle successfully. Laboratory experimental results show that the change in liquid level can be characterized by EMF signal. The state of liquid surface and local thermal state cause the EMF signal to vary in the circumferential direction of the vessel. Furthermore, the EMF signal magnitude decreases with the decrease of the thickness of the graphite crucible. Finally, the main conclusions of the laboratory experiment are supported by the torpedo ladle experiment.

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“…The quality of the produced molten iron affects the cost of the production of the steel and defines for which ultimate purpose it can be sold [2]. However, the adverse conditions presented inside a blast furnace (high pressure, temperature, and erosive environment) may impair the direct measurements of some essential variables for quality control of the molten iron [3]. Thus, the laboratory analysis of some chemical variables of the molten iron samples is fundamental for monitoring the blast furnace operating state [4].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Model of Silicon Content in Molten Iron Using Wavelet Decomposition and Artificial Neural Networks

Diniz

Côco

Gomes

et al. 2021

Metals

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Silicon content forecasting models have been requested by the operational team to anticipate necessary actions during the blast furnace operation when producing molten iron, to control the quality of the product and reduce costs. This paper proposed a new algorithm to perform the silicon content time series up to 8 h ahead, immediately after the molten iron chemical analysis is delivered by the laboratory. Due to the delay of the laboratory when delivering the silicon content measurement, the proposed algorithm considers a minimum useful forecasting horizon of 3 h ahead. In a first step, it decomposes the silicon content time series into different subseries using the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT). Next, all subseries forecasts were determined through Nonlinear Autoregressive (NAR) networks, and finally, these forecasts were summed to furnish the long-term forecast of silicon content. Using data from a real industry, we showed that the prediction error was within an acceptable range according to the blast furnace technical team.

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Multistep Forecasting Models of the Liquid Level in a Blast Furnace Hearth

Cited by 26 publications

References 28 publications

On‐Line Estimation of Liquid Levels in the Blast Furnace Hearth

On‐Line Estimation of Liquid Levels in the Blast Furnace Hearth

Monitoring Liquid Level of Blast Furnace Hearth and Torpedo Ladle by Electromotive Force Signal

Forecasting Model of Silicon Content in Molten Iron Using Wavelet Decomposition and Artificial Neural Networks

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