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<p>This study presents a predictive modeling approach to estimate energy consumption and greenhouse gas (GHG) emissions in ferronickel (FeNi) production using Rotary Kiln–Electric Furnace (RKEF) technology. The model integrates a physics-based framework with multiple linear regression, incorporating key process variables such as calcine charging temperature, energy mix, ore grade, and nickel recovery in the smelter. Two distinct regression models were developed: one for energy use (including quadratic terms) and one for GHG emissions (linear terms only). Both models demonstrated high predictive accuracy (<i>R</i><sup>2</sup> &gt; 0.98). The model uses operational data to facilitate more precise energy consumption and carbon footprint accounting. It also enables the simulation of process modifications and energy source scenarios, providing insights that inform optimization and procurement strategies. Results highlight that while all studied variables influence environmental performance, the energy mix has the greatest impact due to the electricity intensive nature of the smelting stage. The findings indicate the critical role of renewable energy in achieving low-carbon nickel production and emphasize the essence of supportive policies, including stricter coal regulations and investment incentives for clean energy.</p>

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A Predictive Model for Energy Consumption and Greenhouse Gas Emissions in FeNi Production Via Rotary Kiln–Electric Furnace Process Using Physics-Based Parametric Regression Approach

  • 期刊论文

  • 发表日期：

    2025年9月15日

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  • Wenjing Wei,
  • Dong-Yuan Sheng

摘要

This study presents a predictive modeling approach to estimate energy consumption and greenhouse gas (GHG) emissions in ferronickel (FeNi) production using Rotary Kiln–Electric Furnace (RKEF) technology. The model integrates a physics-based framework with multiple linear regression, incorporating key process variables such as calcine charging temperature, energy mix, ore grade, and nickel recovery in the smelter. Two distinct regression models were developed: one for energy use (including quadratic terms) and one for GHG emissions (linear terms only). Both models demonstrated high predictive accuracy (R2 > 0.98). The model uses operational data to facilitate more precise energy consumption and carbon footprint accounting. It also enables the simulation of process modifications and energy source scenarios, providing insights that inform optimization and procurement strategies. Results highlight that while all studied variables influence environmental performance, the energy mix has the greatest impact due to the electricity intensive nature of the smelting stage. The findings indicate the critical role of renewable energy in achieving low-carbon nickel production and emphasize the essence of supportive policies, including stricter coal regulations and investment incentives for clean energy.