Flue dust waste coming from a copper (Cu) smelting company has been valorized using a newly developed metallurgical process with the aim of recovering a dust concentrated in valuable metals, such as lead (Pb) and zinc (Zn), and studying the feasibility of replacing anthracite with biochar as reducing agent. Metallurgical trials using different mixtures of reducing agents have been performed in a furnace at 1400 °C. This furnace employs a high-power thermal plasma (HPTP) system as energy source. Using copper as the base metal, pellets containing a mixture of the waste, different reducing agents, and binder were introduced into the furnace for their dissolution. Recovery yields in the range of 45–85 wt.% for Pb and Zn were obtained in the flue dusts. All the trials proved the effectiveness of the developed process to enrich the flue dusts in valuable metals, including those only using biochar as reducing agent. Further trials will be carried out in order to better control the parameters involved in the metallurgical process developed and improve its efficiency.

The use of optimization algorithms to adjust the numerical models with experimental values has been applied in other fields, but the efforts done in metal casting sector are much more limited. The advances in this area may contribute to get metal casting adjusted models in less time improving the confidence in their predictions and contributing to reduce tests at laboratory scale. This work compares the performance of four algorithms (compass search, NEWUOA, genetic algorithm (GA) and particle swarm optimization (PSO)) in the adjustment of the metal casting simulation models. The case study used in the comparison is the multiscale simulation of the hypereutectic ductile iron (SGI) casting solidification. The model fitting criteria is the value of the tensile strength. Four different situations have been studied: model fitting based in 2, 3, 6 and 10 variables. Compass search and PSO have succeeded in reaching the error target in the four cases studied, while NEWUOA and GA have failed in some cases. In the case of the deterministic algorithms, compass search and NEWUOA, the use of a multiple random initial guess has been clearly beneficious.

In the context of the development of new lightweight materials, Al-alloyed cast irons have a great potential for reducing the weight of the different part of the vehicles in the transport industry. The correlation of the amount of Al and its effect in the microstructure of cast irons is not completely well established as it is affected by many factors such as chemical composition, cooling rate, etc. In this work, four novel lightweight cast irons were developed with different amounts of Al (from 0 wt. % to 15 wt. %). The alloys were manufactured by an easily scalable and affordable gravity casting process in an induction furnace, and casted in a resin-bonded sand mold. The microstructural evolution as a function of increasing Al content by different microstructural characterization techniques was studied. The hardness of the cast irons was measured by the Vickers indentation test and correlated with the previously characterized microstructures. In general, the microstructural evolution shows that the perlite content decrease with the increment of wt. % of Al. The opposite occurs with the ferrite content. In the case of graphite, a slight increment occurs with 2 wt. % of Al, but a great decrease occurs until 15 wt. % of Al. The addition of Al promotes the stabilization of ferrite in the studied alloys. The hardness obtained varied from 235 HV and 363 HV in function of the Al content. The addition of Al increases the hardness of the studied cast irons, but not gradually. The alloy with the highest hardness is the alloy containing 7 wt. % Al, which is correlated with the formation of kappa-carbides and finer perlite.