Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques
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 temp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/17/3235 |
| _version_ | 1798024523083153408 |
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| author | José Díaz Francisco Javier Fernández Inés Suárez |
| author_facet | José Díaz Francisco Javier Fernández Inés Suárez |
| author_sort | José Díaz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:03:54Z |
| format | Article |
| id | doaj.art-29b9b96e39be49b2982c6a84d87892b9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:03:54Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-29b9b96e39be49b2982c6a84d87892b92022-12-22T04:10:22ZengMDPI AGEnergies1996-10732019-08-011217323510.3390/en12173235en12173235Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting TechniquesJosé Díaz0Francisco Javier Fernández1Inés Suárez2Polytechnic School of Engineering, University of Oviedo, 33204 Gijón, SpainPolytechnic School of Engineering, University of Oviedo, 33204 Gijón, SpainPolytechnic School of Engineering, University of Oviedo, 33204 Gijón, SpainIn 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.https://www.mdpi.com/1996-1073/12/17/3235steelmakingBOF charge modelinfrared thermometrydata-driven modelingtime series forecastingARIMA |
| spellingShingle | José Díaz Francisco Javier Fernández Inés Suárez Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques Energies steelmaking BOF charge model infrared thermometry data-driven modeling time series forecasting ARIMA |
| title | Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques |
| title_full | Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques |
| title_fullStr | Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques |
| title_full_unstemmed | Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques |
| title_short | Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques |
| title_sort | hot metal temperature prediction at basic lined oxygen furnace bof converter using ir thermometry and forecasting techniques |
| topic | steelmaking BOF charge model infrared thermometry data-driven modeling time series forecasting ARIMA |
| url | https://www.mdpi.com/1996-1073/12/17/3235 |
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