The primary energy consumption and greenhouse gas emissions from nickel smelting products have been assessed through case studies using a process model based on mass and energy balance. The required primary energy for producing nickel metal, nickel oxide, ferronickel, and nickel pig iron is 174 GJ/t alloy (174 GJ/t contained Ni), 369 GJ/t alloy (485 GJ/t contained Ni), 110 GJ/t alloy (309 GJ/t contained Ni), and 60 GJ/t alloy (598 GJ/t contained Ni), respectively. Furthermore, the associated GHG emissions are 14 tCO2-eq/t alloy (14 tCO2-eq/t contained Ni), 30 t CO2-eq/t alloy (40 t CO2-eq/t contained Ni), 6 t CO2-eq/t alloy (18 t CO2-eq/t contained Ni), and 7 t CO2-eq/t alloy (69 t CO2-eq/t contained Ni). A possible carbon emission reduction can be observed by comparing ore type, ore grade, and electricity source, as well as allocation strategy. The suggested process model overcomes the limitation of a conventional life cycle assessment study which considers the process as a ‘black box’ and allows for an identification of further possibilities to implement sustainable nickel production.

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Molybdenum is mainly used as an alloy material in the iron and steel industry and typically in the form of ferromolybdenum (FeMo). The current study aims to evaluate the energy consumption and greenhouse gas emissions (GHG) of four ferromolybdenum production cases using inventory inputs from a process model based on mass and energy conservations. The total energy required for producing 1 tonne of FeMo can vary between 29.1 GJ/t FeMo and 188.6 GJ/t FeMo. Furthermore, the corresponding GHG emissions differ from 3.16 tCO 2-eq/t FeMo to 14.79 tCO 2-eq/t FeMo. The main variances are from the mining and beneficiation stages. The differences in these stages come from the beneficiation degree (ore grade) and the mine type (i.e., co-product from copper mining). Furthermore, the mine type has a larger impact on the total energy consumption and GHG emissions than the beneficiation degree. More specifically, FeMo produced as co-product from copper mining has a lower environmental impact measured as the energy consumption and GHG emission among all the four cases. The inventory, consumed energy or associated GHG emission is independent on the initial ore grade and mine type in the downstream production stages such as roasting and smelting. Also, transport has the least impact on the energy consumption and GHG emission among all production stages.

The pressure on the steel industry to reduce its carbon footprint has led to discussions to replace coke as the main reductant for iron ore and turn to natural gas, bio-syngas or hydrogen. Such a major transition from the blast furnace-basic oxygen furnace route, to the direct reduction-electric arc furnace route, for steel production would drastically increase the demand for both suitable iron ore pellets and high-quality scrap. The value for an EAF plant to reduce the SiO2 content in DRI by 2 percentage points and the dirt content of scrap by 0.3 percentage points Si was estimated by using the optimization and calculation tool RAWMATMIX®. Three plant types were studied: (i) an integrated plant using internal scrap, (ii) a plant using equal amounts of scrap and DRI and (iii) a plant using a smaller fraction of DRI in relation to the scrap amount. Also, the slag volume for each plant type was studied. Finally, the cost for upgrading was estimated based on using mainly heuristic values. A conservative estimation of the benefit of decreasing the silica content in DRI from 4 to 2% is 20 USD/t DRI or 15 USD/t DR pellets and a conservative figure for the benefit of decreasing the dirt in scrap by 0.3 percentage points Si is 9 USD/t scrap. An estimate on the costs for the necessary ore beneficiation is 2.5 USD/t pellet concentrate and for a scrap upgrade, it is 1-2 USD/t scrap.

In this work, a time-dependent thermodynamic AOD process model, TimeAOD3™, is proposed to be used as a prediction model at practice to reach the desired nitrogen level to lower the cost and carbon footprint. The model is developed from an existing model and integrated with Thermo-Calc®. The modelling results suggest a fairly good prediction when compared with productionscale heats. The relative error of dissolved nitrogen content during and after decarburisation is −0.05 < RE < 1.36 and −0.23 < RE < 0.29, respectively. Besides, most modelled dissolved N% are higher than the measurement during decarburisation. This is probably owing to the formation of high turbulence flow and CO gas bubble which can flush the dissolved nitrogen out of the bath at practice. Moreover, dissolved C%, Cr% in bath and Cr 2 O 3 %, CaO%, MgO% in slag, as well as bath temperature are also compared and studied.

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