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.

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.

A static mass and energy balance model combined with a MgO saturation slag model is developed for electric arc furnaces. The model parameters including distribution ratios and dust factors are calibrated for a specific furnace using experimental data. Afterward, the model is applied to study the effect of charging different amounts of hot briquetted iron (HBI) on energy consumption, charged slag former amount, and slag composition. The following results were obtained per each 1% increase of HBI additions: (i) a 0.16 Nm³/t decrease in the amount of injected oxygen for metal oxidation, (ii) a 1.29 kWh/t increase in the electricity consumption, and (iii) a 34 kg increase in the amount of the slag.

The current nature of steel design and production is a response to meet increasingly demanding applications but without much consideration of end-of-life scenarios. The scrap handling infrastructure, particularly the characterization and sorting, is unable to match the complexity of scrapped products. This is manifested in problems of intermixing and contamination in the scrap flows, especially for obsolete scrap. Also, the segmentation of scrap classes in standards with respect to chemical compositions is based on tolerance ranges. Thus, variation in scrap composition exists even within the same scrap type. This study applies the concept of expected value of perfect information (EPVI) to the context of steel recycling. More specifically, it sets out to examine the difference between having partial and full information on scrap composition by using a raw material optimization software. Three different scenarios with different constraints were used to appraise this difference in terms of production and excess costs. With access to perfect information, production costs decreased by 8–10%, and excess costs became negligible. Overall, comparing the respective results gave meaningful insights on the value of reestablishing the compositional information of scrap at the end of its use phase. Furthermore, the results provided relevant findings and contribute to the ongoing discussions on the seemingly disparate prioritization of economic and environmental incentives with respect to the recycling of steel.

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 production-scale 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.

A statistical model is developed in order to simulate the melt composition in electric arc furnaces (EAFs) with respect to uncertainties in (1) scrap composition, (2) scrap weighing and (3) element distribution factors. The tramp element Cu and alloying element Cr are taken into account. The model enables simulations of a charge program as well as backwards estimations of the element concentrations and their variance in scrap. In the backwards calculation, the maximum likelihood method is solved by considering three cases corresponding to the involved uncertainties. It is shown that the model can estimate standard deviations for elements so that the real values lie within the estimated 95% confidence interval. Moreover, the results of the model application in each target product show that the estimated scrap composition results in a melt composition, which is in good agreement with the measured one. The model can be applied to increase our understanding of scrap chemical composition and lower the charged material cost and carbon footprint of the products.