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<p>Iron ore sintering, a critical process in blast furnace operations, directly impacts sinter quality, productivity, and fuel consumption. However, fuel control in sintering is largely dependent on operator experience, rendering responding quantitatively to real-time fluctuations in process conditions, challenging. This often results in suboptimal energy use, reduced productivity, and increased greenhouse gas emissions. This study presents the development and validation of a data-driven heat optimization program for iron ore sintering. The model incorporates 50 cases of Lab-scale sinter pot test data and calculates the theoretical optimal reaction heat (determined to be 111.4~123&#xa0;kcal/kg-blending ore) required for optimal sinter formation. Experimental results showed that when the actual heat input deviated from the optimal range, sinter productivity decreased by up to 12&#xa0;pct compared to the theoretical maximum. In contrast, within the optimal range, productivity reached up to 98.5&#xa0;pct of the theoretical maximum productivity. Key variables such as fuel ratio, ore blend composition, ambient temperature, and exhaust gas temperature were integrated into a heat balance framework to estimate effective reaction heat. Compared to traditional experience-based fuel control, this model offers a quantitative and predictive approach to sintering fuel management. The proposed program enhances energy efficiency and contributes to environmentally sustainable sintering by reducing CO<sub>2</sub> emissions.</p> Graphical Abstract <p></p>

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Calorific Optimization for Improving Productivity and Energy Efficiency in Iron Ore Sintering

  • 期刊论文

  • 发表日期：

    2025年7月10日

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  • Taejun Park,
  • Minseong Kim,
  • Yusik Seo,
  • Jongmin Oh,
  • Seungyeon Won,
  • Huigwon Chae

摘要

Iron ore sintering, a critical process in blast furnace operations, directly impacts sinter quality, productivity, and fuel consumption. However, fuel control in sintering is largely dependent on operator experience, rendering responding quantitatively to real-time fluctuations in process conditions, challenging. This often results in suboptimal energy use, reduced productivity, and increased greenhouse gas emissions. This study presents the development and validation of a data-driven heat optimization program for iron ore sintering. The model incorporates 50 cases of Lab-scale sinter pot test data and calculates the theoretical optimal reaction heat (determined to be 111.4~123 kcal/kg-blending ore) required for optimal sinter formation. Experimental results showed that when the actual heat input deviated from the optimal range, sinter productivity decreased by up to 12 pct compared to the theoretical maximum. In contrast, within the optimal range, productivity reached up to 98.5 pct of the theoretical maximum productivity. Key variables such as fuel ratio, ore blend composition, ambient temperature, and exhaust gas temperature were integrated into a heat balance framework to estimate effective reaction heat. Compared to traditional experience-based fuel control, this model offers a quantitative and predictive approach to sintering fuel management. The proposed program enhances energy efficiency and contributes to environmentally sustainable sintering by reducing CO2 emissions.

Graphical Abstract