Dust generated at an electric arc furnace during steel production industry is still not a solved problem. Electric arc furnace dust (EAF) is a hazardous solid waste. Sintering of well-prepared briquetted mixtures in a shaft furnace is one of possible methods of EAFD utilisation. Simultaneously some metal oxides from exhaust gases can be separated. In this way, various metals are obtained, particularly zinc is recovered. As a result, zinc-free briquettes are received with high iron content which can be used in the steelmaking process. The purpose of the research was selecting the appropriate chemical composition of briquettes of the required strength and coke content necessary for the reduction of zinc oxide in a shaft furnace. Based on the results of the research the composition of the briquettes was selected. The best binder hydrated lime and sugar molasses and the range of proper moisture of mixture to receive briquettes of high mechanical strength were also chosen and tested. Additionally, in order to determine the thermal stability for the selected mixtures for briquetting thermal analysis was performed. A technological line of briquetting was developed to apply in a steelworks.

Additive manufacturing (AM) technologies have been gaining popularity in recent years due to patent releases – and in effect – better accessibility of the technology. One of the most popular AM technologies is fused deposition modeling (FDM), which is used to manufacture products out of thermoplastic polymers in a layer-by-layer manner. Due to the specificity of the method, parts manufactured in this manner tend to have non-isotropic properties. One of the factors influencing the part’s mechanical behavior and quality is the thermoplastic material’s bonding mechanism correlated with the processing temperature, as well as thermal shrinkage during processing. In this research, the authors verified the suitability of finite element method (FEM) analysis for determining PET-G thermal evolution during the process, by creating a layer transient heat transfer model, and comparing the obtained modelling results with ones registered during a real-time process recorded with a FLIR T1020 thermal imaging camera. Our model is a valuable resource for providing thermal conditions in existing numerical models that connect heat transfer, mesostructure and AM product strength, especially when experimental data is lacking. The FE model presented reached a maximum sample-specific error of 11.3%, while the arithmetic mean percentage error for all samples and layer heights is equal to 4.3%, which the authors consider satisfactory. Model-to-experiment error is partially caused by glass transition of the material, which can be observed on the experimental cooling rate curve after processing the temperature signal.