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<p>The ironmaking blast furnace inevitably operates with varying thermal level due to variation in raw material quality and process parameters, which signifies variation in hot metal temperature (HMT), one of the key indicators of the furnace performance. The variation in HMT can be dampened if thermal level is forecasted in advance to sustain the furnace in stable operation, which in turn also reduces the fuel rate. In the present work, a model has been developed using an analytical expression which is based on mass and enthalpy balance to calculate specific fuel rate and direct reduction rate of the furnace using real time blast furnace data. The model is encapsulated with data engineering methodology which provides good quality data as an input to the model for better prediction accuracy. The model predicts thermal level 2&#xa0;h ahead that correlates with HMT with regression coefficient ~ 0.838. Further, a user interface is developed to track the predicted thermal index on real time basis, thereby recommendations are provided on controllable parameters, if any drift in thermal level is encountered. The model is operational with an accuracy of ~ 90%, enabling efficient and stable furnace operation and its usage has led to reduction in standard deviation of HMT.</p> Graphical Abstract <p></p>

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Improving Fuel Efficiency Through Carbon-Direct Rate Reduction for Sustainable Blast Furnace Operation

  • 期刊论文

  • 发表日期：

    2025年7月8日

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  • Durgesh Shukla,
  • Pratyush Ranjan Samantaray,
  • Ashish Agrawal,
  • Basant Kumar Singh,
  • Sagnik Chowdhury,
  • Kamma Ramakrishna Rao,
  • Jose Martin Korath,
  • Padmapal

摘要

The ironmaking blast furnace inevitably operates with varying thermal level due to variation in raw material quality and process parameters, which signifies variation in hot metal temperature (HMT), one of the key indicators of the furnace performance. The variation in HMT can be dampened if thermal level is forecasted in advance to sustain the furnace in stable operation, which in turn also reduces the fuel rate. In the present work, a model has been developed using an analytical expression which is based on mass and enthalpy balance to calculate specific fuel rate and direct reduction rate of the furnace using real time blast furnace data. The model is encapsulated with data engineering methodology which provides good quality data as an input to the model for better prediction accuracy. The model predicts thermal level 2 h ahead that correlates with HMT with regression coefficient ~ 0.838. Further, a user interface is developed to track the predicted thermal index on real time basis, thereby recommendations are provided on controllable parameters, if any drift in thermal level is encountered. The model is operational with an accuracy of ~ 90%, enabling efficient and stable furnace operation and its usage has led to reduction in standard deviation of HMT.

Graphical Abstract