Abstract:

Three quarters of all emissions associated with steel production come from the reduction process of iron ore. Reducing the iron ore is a crucial step in the steel manufacturing chain, as it removes the oxygen from the ore before it can be transformed into steel. The gas employed in the reducing process is composed of a mixture of carbon monoxide and hydrogen, which respectively emit carbon dioxide (CO2) and water after the reduction. Therefore, the higher the monoxide carbon content in the reducing gas, the more CO2 emissions are produced. One possible pathway to reduce the carbon footprint of iron ore reduction is to increase the hydrogen content of the reducing gas. Our proposed research project investigates the injection of reducing gas with enriched hydrogen content into our industrial partner’s operations. This partnership provides Polytechnique Montreal’s team a solid empirical basis for large scale research, with each test consuming multiple dozen tons of hydrogen. Our process simulation of the hydrogen injection will render an estimate of how much CO2 emissions were avoided and what are the adjustments required to operate the plant at that new hydrogen regime.

Furthermore, scenarios involving a radical change in the operating regime, such as the replacement of up to 100 % of the reducing gas content will be studied. To achieve these predictions, a customizable interface will be programmed keeping in mind future utilization by the industrial partner. This interface will perform the association into one software of specific databases such as Polytechnique CRCT’s laboratory commercial software, FACTSage, and Python library thermodynamics calculator, Chemapp. By automating the computation of energy and mass balances, the accuracy of the results will be improved compared with the use of a conventional chemical process simulation software. Commercially available softwares are not currently suited for pyrometallurgical processes such as the iron ore reduction operating at temperatures above 900 degrees Celsius. An optimization based on artificial intelligence will be performed to find the right balance between the inputs of the process to minimize CO2 emissions.

This research project will also aim to combine life cycle analysis with the process simulation of a optimized iron ore reduction process. This analysis technique allows to measure the potential environmental impacts of the process, from raw materials extraction to end of life. Current life cycle analysis techniques are often based on average values from multiple known processes, which make them incompatible with technologies in development. Here, it is proposed to integrate the life cycle approach directly into the process design stage. In this way, a multi-criteria optimization can be performed taking into consideration environmental impacts, guiding the decision-making for the design of the new process. The deployment of this new optimized process could then substantially reduce the carbon footprint of steel production, and help Canada lead its industry towards a net-zero pathway.

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Sophia Roy