Optimization and Scale-Up of a Bioleaching Process for Metallurgical Dusts

The concept of the circular economy is becoming increasingly important, especially in the context of the growing scarcity of resources and the increase in environmental pollution. This also applies to the steel industry, which is one of the most energy- and raw material-intensive sectors. Steel production generates by-products and waste products that cannot be recycled due to their metal content. The high iron content of cast house dust (approx. 65%) makes this by-product an interesting alternative iron raw material. Although the entire cast house dust is returned to the internal steel production cycle, the high zinc content makes it difficult to recover other materials such as blast furnace dust, so that some of these dusts have to be landfilled.
The currently established processes for recovering metals from steel mill dusts can be divided into physical, hydrometallurgical and pyrometallurgical processes. In recent years, however, alternative processes such as bioleaching for the removal of heavy metals like zinc have gained interest, as they are more environmentally friendly and less energy-intensive than the established processes. The microorganisms commonly used for bioleaching are acidophilic bacteria such as Acidithiobacillus ferrooxidans or Acidithiobacillus thioooxidans.
The aim of this work was to optimize the bioleaching process for the substrate cast house dust and to develop a suitable scale-up in order to build a pilot plant of 1,000 L and thus get closer to production scale. For this purpose, two 3-liter stirred tank systems were set up with the necessary sensors and the pH, redox and temperature values were continuously recorded. In an initial optimization, the most suitable bacterial culture was selected. In further experiments, various parameters such as the composition of the medium, the aeration rate or the amount of inoculum were varied in order to increase the zinc leaching efficiency. Based on the data collected, calculations were made for a suitable design and empirical values from systems, which are already in operation, were taken into account in order to plan the best possible system.