Energy Implications of Switching to Surfactant-Modified Concentrate in Iron Ore Pellet Induration

Abstract:

The article investigates energy consumption during the drying stage of iron ore pellets, a critical process in ensuring energy efficiency in mining and metallurgical production. Particular attention is given to the influence of charge material moisture content and the application of SAS (SAS) on the specific consumption of energy resources, namely electricity and natural gas. Industrial trials were conducted at one of the leading mining and processing enterprises in the Kryvyi Rih region, focusing on the transition from the baseline (in-house) concentrate to raw material from another regional enterprise, pre-treated with non-ionic SAS. It was established that the increased dispersity and hydrophilicity of the new raw material concentrate necessitate additional moistening of the charge, significantly affecting thermal regimes and energy expenditures during drying. Based on collected experimental data, regression models were developed to quantitatively predict the specific consumption of electricity and gas as a function of technological parameters. The primary factors influencing energy consumption were identified as the moisture content of the charge and the daily throughput of the drying unit. An increase in specific electricity consumption by 17.73% and natural gas consumption by 33.25% was recorded, accompanied by a simultaneous reduction in productivity by 9.55%. The findings are relevant for specialists in energy management, electrical engineering, and thermal analysis in metallurgy, particularly in the development of strategies for optimizing energy consumption under industrial conditions.

• Periodical: Key Engineering Materials (Volume 1043)
• Pages: 21-32
• DOI: https://doi.org/10.4028/p-fi3lOq
• Online since: February 2026
• Authors: Volodymyr V. Kukhar, Yevhen Chuprinov, Igor Navolniev*, Elena Belan
• Keywords: Concentrate Fineness, Energy Consumption, Feed Mix, Induration Machine, Iron Ore Pellets, Moisture Content, Productivity, Regression Modeling, Surfactants

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