Metall. Res. Technol.
Volume 116, Number 4, 2019
|Number of page(s)||16|
|Published online||28 June 2019|
Advances in thermal level measurement techniques using mathematical models, statistical models and decision support systems in blast furnace
Tata Steel Limited,
2 Department of Metallurgy and Materials Engineering (Materials Technology), National Institute of Technology, Jamshedpur, 831014, India
* e-mail: firstname.lastname@example.org
Accepted: 22 March 2019
The estimation of thermal level in blast furnace is of utmost importance, because the processes occurring inside the blast furnace are complex in nature and any drift in thermal level could lead to abnormal furnace state. The present review is made to understand the methods for estimating thermal level in blast furnace, and the drift in estimation of the thermal level. The thermal level estimation is divided into 3 categories, viz. mathematical models, statistical models and decision support systems. The mathematical models are based on the first principle of thermodynamics and give an estimate of the thermal level in blast furnace. On the other hand, the statistical models are mainly the data-based approach that uses the historical data to predict the instability in blast furnace. Lastly, the decision support systems are the prescriptive models that give the recommendations for making the necessary corrections in the process parameters to avoid occurrence of abnormality in blast furnace. Further, the drifts in estimation of thermal level by these techniques are identified and recommendations are made to improve the accuracy of thermal level estimation. The recommendations to control thermal level in blast furnace are provided which when applied in the industrial blast furnace, can avoid the occurrence of catastrophic condition.
Key words: blast furnace / hot metal temperature / thermal level / prediction models / mathematical models / data-driven models / expert systems
© EDP Sciences, 2019
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