Hybrid Model of Molten Steel Temperature Prediction Based on Ladle Heat Status and Artificial Neural Network

He, Fei; He, Dingyong; Xu, Anjun; Wang, Hongbing; Nai-yuan, Tian

doi:10.1016/s1006-706x(14)60028-5

Cited by 49 publications

(26 citation statements)

References 5 publications

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“…There are also some studies on the optimum scheduling of torpedo cycling, in which thermal losses are identified as an important part of the cost structure, being a function of the elapsed time between pick up and drop of the hot metal [16] and also the empty time [17]. Concerning the effect of heat transfer on hot metal temperature at BOF shop, both theoretical [18] and experimental [19] research has been carried out, but a complete model, such as those for steel ladles [20], has not yet been published.…”

Section: Introductionmentioning

confidence: 99%

Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques

2019

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In oxygen steelmaking, the charge calculation strongly depends on hot metal temperature prediction. Although a hot metal temperature drop from the blast furnace in a steel plant may be too complex to be accurately modeled in detail, the combined use of sensors and statistical models can improve temperature estimation and result in better cost, quality and productivity, as well as lower emissions. In order to develop a simple but robust method for hot metal temperature forecasting, the suitability of infrared thermometry and time series forecasting has been studied. Simultaneous infrared thermometer measurement and video recording was used for designing the processing of the thermometer signal. The resulting temperature estimations are in good agreement with disposable thermocouple measurements giving an error of 11 °C with 60% reliability (chances of obtaining a successful output). Conversely, the time series approach was based mainly on the AutoRegressive Integrated Moving Average (ARIMA) model in which five additional process variables were introduced as exogenous predictors, as well as using a moving window of past observations for continuous model training. The resulting error was 15 °C with more than 90% reliability. Combining measuring and modeling approaches reduced the error to 13 °C with 100% reliability, thereby providing a hybrid procedure that has long-term stability and is self-adaptive to varying production scenarios.

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Section: Introductionmentioning

confidence: 99%

Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques

2019

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“…In order to define the main factors that most impact heat losses from ladles, Fredman (2000) reviewed part of the literature, starting in 1956, which deals with the control of the thermal status of liquid steel, not only in ladles, but also in all stages between the tapping of the converter and the continuous casting. From the beginning of the 21st century, the works found in the literature began to apply different types of models, especially physical, mathematical, empirical and statistical ones (Gupta and Chandra, 2004;Jormalainen and Louhenkilpi, 2006;Tian et al, 2008;Wu et al, 2012;Ahmad et al, 2014;He et al, 2014;Laha et al, 2015;Wang et al, 2018;Hou et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Heat Transfer in Steel Ladles: Models and Applications

Diniz

Ciarelli

Salles

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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This work presents a summary of the main tools and contributions, since the 2000s, to solve temperature control problems and to predict thermal temperatures in steel ladles. We discuss different modeling strategies implemented to several applications related to heat transfer in steel ladles and their most relevant contributions, as well we show some of the main process parameters. Finally, future perspectives are described, mainly the advantages of the implementations based on machine learning.

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“…and the carbon content of molten steel [3]. ANNs are also used to estimate the amount of oxygen and coolant required in the end-blow period [3][4][5][6][7]. In recent years, the support vector machine (SVM) model has also been employed to accurately estimate the endpoint parameter [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Estimation Methods of the Endpoint Temperature in Basic Oxygen Furnace Steelmaking Process with Selection of Input Parameters

Park¹,

Kim²,

Kim³

et al. 2018

Korean J. Met. Mater.

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The basic oxygen furnace (BOF) steelmaking process in the steel industry is highly complicated, and subject to variations in raw material composition. During the BOF steelmaking process, it is essential to maintain the carbon content and the endpoint temperature at their set points in the liquid steel. This paper presents intelligent models used to estimate the endpoint temperature in the basic oxygen furnace (BOF) steelmaking process. An artificial neural network (ANN) model and a least-squares support vector machine (LSSVM) model are proposed and their estimation performance compared. The classical partial least-squares (PLS) method was also compared with the others. Results of the estimations using the ANN, LSSVM and PLS models were compared with the operation data, and the root-mean square error (RMSE) for each model was calculated to evaluate estimation performance. The RMSE of the LSSVM model 15.91, which turned out to be the best estimation. RMSE values for the ANN and PLS models were 17.24 and 21.31, respectively, indicating their relative estimation performance. The essential input parameters used in the models can be selected by sensitivity analysis. The RMSE for each model was calculated again after a sequential input selection process was used to remove insignificant input parameters. The RMSE of the LSSVM was then 13.21, which is better than the previous RMSE with all 16 parameters. The results show that LSSVM model using 13 input parameters can be utilized to calculate the required values for oxygen volume and coolant needed to optimally adjust the steel target temperature.

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Hybrid Model of Molten Steel Temperature Prediction Based on Ladle Heat Status and Artificial Neural Network

Cited by 49 publications

References 5 publications

Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques

Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques

Heat Transfer in Steel Ladles: Models and Applications

Comparative Study of Estimation Methods of the Endpoint Temperature in Basic Oxygen Furnace Steelmaking Process with Selection of Input Parameters

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