This article is based on specialized analyses by Dr. Babak Al-Tahae, senior advisor in the field of geology and geometallurgy, and examines the role of geometallurgy in optimizing energy consumption, reducing carbon footprint, and facilitating the steel industry’s transition to low-carbon production.
Introduction
In the first part of this article, the role of geometallurgy as a link between geology, mineralogy, processing, and steelmaking was discussed. It was also shown that the quality of iron ore feed, mineral type, liberation degree, hardness, porosity, gangue composition, and reducibility can all affect the performance of the steel production chain and energy consumption.
In the second part, the main focus is on the role of geometallurgy in optimizing energy consumption, reducing CO₂ emissions, applying artificial intelligence in geometallurgical modeling, and examining global experiences in the path to producing green steel.
۱. Geometallurgy and Energy Optimization in the Steel Chain
The steel industry is one of the most energy-intensive industries in the world, and a significant portion of its operating costs and greenhouse gas emissions are directly related to energy consumption. In the transition to green steel, reducing energy intensity has become one of the most important strategic goals for mining and steel companies.
Despite significant investments in technologies such as hydrogen reduction, renewable energies, and electric arc furnaces, a significant portion of energy reduction opportunities still lie in the stage of mineral feed management.
Geometallurgy provides the possibility of predicting and managing the total energy consumed in the entire chain, from mining to steel production, based on the inherent characteristics of the ore.
۱.۱ Energy Consumption in the Steel Chain
Energy consumption in the steel chain occurs in various stages. Approximately, the share of different stages in energy consumption is as follows:
| Stage | Approximate share of energy consumption |
|---|---|
| Mining | ۳ to ۸ percent |
| Crushing and grinding | ۲۵ to ۴۵ percent |
| Processing and concentration | ۵ to ۱۰ percent |
| Pelletizing | ۱۵ to ۲۵ percent |
| Direct reduction | ۲۰ to ۳۵ percent |
| Steel production | ۱۰ to ۲۰ percent |
These figures indicate that a significant portion of energy is consumed before the feed enters the steel production unit. Therefore, geometallurgical opportunities are mainly located in the upstream stages.
۱.۲ Reducing Energy in Mining and Crushing
The first effect of geometallurgy on energy consumption appears in the extraction stage. Characteristics such as uniaxial compressive strength, point load index, porosity, weathering, and rock hardness affect the energy required for drilling, blasting, loading, and transportation.
In the crushing and grinding stage, parameters such as the following are important:
- Bond Work Index
- Fracture index
- Rock mechanical strength
- Abrasion index
- Liberation degree of iron-bearing minerals
Crushing is one of the largest energy consumers in iron ore processing plants. If the plant feed consists of a mixture of soft and hard rocks, energy consumption increases, plant capacity decreases, and operational fluctuations intensify.
A geometallurgical model can identify the ranges of soft, medium, and hard rocks. As a result, the feed program can be designed to reduce specific energy consumption.
۱.۳ Preventing Over-Grinding
One of the most important sources of energy waste in processing plants is over-grinding or over-crushing.
Information from systems such as QEMSCAN, MLA, and Automated Mineralogy provides the possibility of determining the optimal liberation size for each mineral domain. If the liberation size is determined correctly, unnecessary grinding can be prevented, and energy consumption can be reduced.
In simple terms, the goal of geometallurgy is not to crush all feed to a fixed size, but to crush each type of ore only to the size required for effective liberation of iron-bearing minerals.
۱.۴ Energy Optimization in Pelletizing
Pelletizing is one of the largest thermal energy consumers in the iron ore chain. Energy consumption in this stage is heavily influenced by the mineralogy of the feed.
Magnetite concentrates undergo exothermic oxidation during pellet firing, which can reduce fuel consumption and CO₂ emissions. In contrast, hematite concentrates lack this advantage and require more fuel. Goethite concentrates also consume more energy due to dehydration.
Geometallurgical models can predict the ratio of magnetite to hematite, goethite percentage, moisture content, and surface area. Based on this information, optimal feed for pelletizing can be designed, and natural gas consumption can be reduced.
۱.۵ Energy Optimization in Direct Reduction
In direct reduction units, energy is consumed for heating pellets, producing reducing gas, and performing reduction reactions. The most important geometallurgical variable in this stage is reducibility.
Pellets with higher reducibility:
- Have a shorter residence time in the reactor
- Consume less natural gas or hydrogen
- Create higher energy efficiency
- Have lower carbon emissions
In hydrogen-based green steel technologies, the importance of geometallurgy increases, as hydrogen penetration into the pellet structure depends on porosity, pore size, and internal microstructure.
۱.۶ Geometallurgical Energy Intensity Index
To quantitatively evaluate the effect of geometallurgy on energy consumption, an index called the Geometallurgical Energy Intensity or GEI can be defined.
This index shows the amount of energy consumed to convert one ton of ore into steel product throughout the entire chain.
In an advanced model, each mineral block can have a GEI value. Therefore, the mineral deposit can be classified not only based on grade but also on energy intensity.
This approach enables mine planning based on energy consumption, a topic that will be of great importance in the future low-carbon economy.
۲. The Role of Geometallurgy in Reducing CO₂ Emissions and Producing Green Steel
Reducing CO₂ emissions has become the most important driver of technological transformation in the steel industry. Green steel means not only replacing fossil fuels with renewable energy but also reducing emissions throughout the entire value chain.
Geometallurgy can be one of the most effective tools for reducing CO₂ emissions, as many factors determining the carbon footprint of steel have their roots in the geological and mineralogical characteristics of the mineral deposit.
۲.۱ Main Sources of Carbon Emissions in the Steel Chain
CO₂ emissions in the steel chain are created from several main sources:
- Fuel consumption by machinery in mining
- Electricity consumption in crushing and grinding
- Energy consumption in processing and filtration
- Natural gas consumption in pelletizing
- Decomposition of carbonates during heating
- Natural gas or hydrogen consumption in direct reduction
- Electricity and auxiliary material consumption in steel production
From a geometallurgical perspective, the carbon footprint of steel begins with the geology of the deposit. Two ores with the same grade can have completely different carbon footprints due to differences in hardness, mineralogy, reducibility, and gangue composition.
۲.۲ The Role of Mineralogy in CO₂ Emissions
Magnetite, due to its exothermic oxidation reaction in the pelletizing kiln, can reduce fuel consumption and CO₂ emissions.
Goethite, due to its structural water, requires more energy for dehydration and can increase fuel consumption.
Carbonates such as calcite and dolomite directly release CO₂ during heating. Therefore, the presence of carbonates not only increases energy consumption but is also a direct source of carbon emissions.
۲.۳ Reducing Carbon Emissions in Crushing, Pelletizing, and Reduction
In crushing, identifying softer ranges and preventing unnecessary grinding can reduce electricity consumption and indirect CO₂ emissions.
In pelletizing, selecting feed with suitable mineralogical composition can reduce the need for fuel while maintaining optimal pellet strength.
In direct reduction, the higher the reducibility of the pellet, the less the residence time, gas consumption, and carbon emissions.
۲.۴ Geometallurgical Carbon Index
To quantify the effect of geometallurgy on carbon emissions, an index called the Geometallurgical Carbon Index or GCI can be defined.
This index shows the amount of CO₂ emissions related to each mineral block throughout the entire mine-to-steel chain.
In this framework, each mineral block can be ranked not only based on grade and economic value but also on its carbon footprint.
This approach is in line with the concept of Mine-to-Steel Carbon Footprint, which means calculating the carbon footprint from the moment of mining to the production of final steel.
۳. Application of Artificial Intelligence in Iron Ore Geometallurgy
Modern geometallurgy is a data-driven issue. A large volume of geological, mineralogical, mechanical, and process data must be converted into a predictive model of behavior.
Due to the complexity of relationships between deposit characteristics and process performance, classical statistical methods are not always responsive to industrial needs. In these conditions, artificial intelligence and machine learning can play a crucial role.
۳.۱ Data Architecture in Intelligent Geometallurgy
An integrated data architecture for geometallurgy usually consists of four layers:
- Geological layer
Includes drilling logs, lithology, structures, and geophysical data - Mineralogical layer
Includes QEMSCAN, MLA, mineral percentage, and liberation degree data - Mechanical and process layer
Includes Bond Work Index, fracture index, abrasion index, crushing, grinding, and separation data - Energy and carbon layer
Includes pellet quality index, reducibility index, energy intensity index, and geometallurgical carbon index
۳.۲ Predicting Energy and Carbon with Machine Learning
One of the most important applications of artificial intelligence in geometallurgy is the direct prediction of energy consumption and CO₂ emissions for each mineral block.
Machine learning models can identify complex relationships between variables such as hardness, mineralogy, liberation degree, gangue composition, and reducibility, and predict outputs such as energy consumption, pellet quality, reducibility, and carbon footprint.
As a result, artificial intelligence can become a tool for operational decision-making in green steel production.
۴. Case Studies: Sweden, Australia, and Canada
۴.۱ Sweden and the HYBRIT Project
The HYBRIT project is one of the most advanced green steel projects in the world, developed in collaboration with SSAB, LKAB, and Vattenfall. The goal of this project is to produce steel without using fossil fuels and to replace carbon with green hydrogen in the iron ore reduction process.
In Sweden, high-grade magnetite deposits with low impurities provide suitable feed for pelletizing and hydrogen reduction. This shows that the success of green steel technologies is heavily dependent on the geometallurgical quality of the feed.
۴.۲ Australia
Australia is one of the world’s largest iron ore producers, but many of its deposits consist of medium-grade hematite with higher impurities and altered materials.
In these conditions, geometallurgy can play a decisive role in separating low-energy and high-energy domains, optimizing feed mixing, reducing energy consumption in crushing, and controlling feed quality fluctuations.
۴.۳ Canada
Canada has diverse iron ore deposits, particularly in the Labrador Trough region. The diversity of mineralogy and high variability of deposits have made geometallurgy crucial in managing feed quality.
In Canadian projects, the focus is on producing high-quality pellets, optimizing firing energy, developing direct reduction with low-carbon natural gas and combined hydrogen, and reducing CO₂ emissions.
۵. Conclusion
The transition to green steel is not just a transformation in reduction technology or a change in energy source, but it requires a fundamental redesign of the relationship between geology, mineralogy, processing, and metallurgy.
In the past, the value of an iron ore deposit was defined mainly based on grade, tonnage, extraction cost, and processing recovery. However, in the framework of low-carbon steel, the true value of a deposit should be evaluated based on three simultaneous axes:
- Economic value
- Energy intensity
- Carbon footprint
In this view, a low-grade but low-energy and low-carbon block may have more strategic value than a high-grade but high-energy block.
Geometallurgy can act as the technical infrastructure for the transition to low-carbon steel, as it converts geological and mineralogical data into decision-making indices in the fields of energy, carbon, and product quality.
Therefore, the future of green steel will depend not only on metallurgical technologies but also on a deeper understanding of the geometallurgical behavior of iron ore deposits.