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.

Statistical modeling, also known as machine learning, has gained increased attention in part due to the Industry 4.0 development. However, a review of the statistical models within the scope of steel processes has not previously been conducted. This paper reviews available statistical models in the literature predicting the Electrical Energy (EE) consumption of the Electric Arc Furnace (EAF). The aim was to structure published data and to bring clarity to the subject in light of challenges and considerations that are imposed by statistical models. These include data complexity and data treatment, model validation and error reporting, choice of input variables, and model transparency with respect to process metallurgy. A majority of the models are never tested on future heats, which essentially renders the models useless in a practical industrial setting. In addition, nonlinear models outperform linear models but lack transparency with regards to which input variables are influencing the EE consumption prediction. Some input variables that heavily influence the EE consumption are rarely used in the models. The scrap composition and additive materials are two such examples. These observed shortcomings have to be correctly addressed in future research applying statistical modeling on steel processes. Lastly, the paper provides three key recommendations for future research applying statistical modeling on steel processes.

The non-linearity of the Electric Arc Furnace (EAF) process and the correlative behavior between the process variables impose challenges that have to be considered if one aims to create a statistical model that is relevant and useful in practice. In this regard, both the statistical modeling framework and the statistical tools used in the modeling pipeline must be selected with the aim of handling these challenges. To achieve this, a non-linear statistical modeling framework known as Artificial Neural Networks (ANN) has been used to predict the Electrical Energy (EE) consumption of an EAF producing stainless steel. The statistical tools Feature Importance (FI), Distance Correlation (dCor) and Kolmogorov-Smirnov (KS) tests are applied to investigate the most influencing input variables as well as reasons behind model performance differences when predicting the EE consumption on future heats. The performance, measured as kWh per heat, of the best model was comparable to the performance of the best model reported in the literature while requiring substantially fewer input variables.

The performance of self‐assessment and the various tools for conducting self‐assessment have been frequently debated in the literature. This paper discusses the complete process of self‐assessment and how organisations use the EFQM excellence model in real‐life situations. The research, reflected in this paper, comprises experiences from nine large organisations. There is no universal method for self‐assessment. On the contrary, findings indicate that several approaches to self‐assessment are successful as long as they fit the organisation, are used continuously, and foster participation. Organisations sometimes overlook the need to establish structured ways of prioritising actions for improvement, creating possibilities for sharing experiences, collecting feedback, and developing work procedures. It is also crucial to understand that self‐assessment has no end in itself as a separate activity. We claim that self‐assessment must be considered from a holistic perspective in order to realise its full potential.