Influence of Different Carbon Content on Reduction of Zinc Oxide via Metal Bath

Electric arc furnace dust (EAFD) is an important secondary resource for the zinc industry. The most common process for its recycling is the pyro-metallurgical treatment in the Waelz process. However, this process focuses on the recycling of the zinc, whereas the recovery of other metals from the EAFD—such as iron and other alloying elements—is neglected. An up-to-date version of reprocessing can involve multi-metal recycling by means of a metal bath containing carbon. The use of a liquid iron alloy requires a higher processing temperature, which enables the reduction and melting of iron oxides as well as other compounds occurring in the dust. Furthermore, the Zn yield is higher and the reduction kinetics are faster than in the Waelz process. This paper is only focused on the zinc reduction in such a metal bath. In order to determine the influence of the carbon content in the molten metal on the reduction rate, experiments were carried out on the reduction behavior of zinc oxide using a synthetic slag. This slag, with a basicity B2 = 1, was applied to an iron bath with varying carbon contents. (0.85%, 2.16%, 2.89%, and 4.15%) The decrease in the zinc oxide concentration was monitored, along with the reaction rates calculated from these data. It was found that the reaction rate increases with rising carbon content in the melt.

Feasibility study of a method for identification and classification of magnesium and aluminum with ME-XRT

The identification of magnesium and aluminum in scrap metal recycling has always been a difficult point. In this paper, a material identification method of multi-energy X-ray transmission (ME-XRT) based on photon counting detector (PCD) and machine learning algorithm was proposed and used to identify and classify magnesium and aluminum. This method includes three main steps: using PCD to obtain X-ray attenuation images of five energy bins, feature extraction, and the machine learning classification. The performance of several machine learning models was compared for the fine-grained classification task. The prediction results demonstrate that the best achieved recognition rates of aluminum and magnesium are 96.43% and 98.81%, respectively.

Metal Extraction and Recovery from Mobile Phone PCBs by a Combination of Bioleaching and Precipitation Processes

Bearing in mind the metal rich composition of printed circuit boards (PCBs), this material represents a secondary source of valuable metals and offers an entrepreneurial opportunity in the metal sales market. Based on the ability of microorganisms to regenerate and produce the chemical oxidants that are responsible for metal leaching, bioleaching has become an efficient and affordable alternative to conventional metal recycling technologies, although further research is still necessary before industrial implementation. This study focuses on the recovery of metals contained in mobile phone PCBs through a combined process. Two different PCB pre-treatments were evaluated: grinding the whole piece and removing the epoxy cover from the piece without grinding. The benefit of A. ferrooxidans activity on the metal solubilization rate was analyzed. Additional chemical leaching assays were also conducted for comparison purposes and the reagents ferric iron (Fe3+) and sulfuric acid (H2SO4) were selected for these experiments. The copper extraction results obtained in Fe3+ experiments with and without bacteria (A. ferrooxidans) were similar after 260 h of operation, indicating the need for alternative strategies to ensure a controlled and continuous metal biodissolution rate. The contribution of H2SO4 to the leaching processes for copper and nickel was almost negligible during the first 50 h, and more significant thereafter. The recovered metals were precipitated from a synthetic solution simulating a real ferric leaching by adding sodium hydroxide (NaOH) and sodium sulfide (Na2S). The combination of both precipitants allowed an effective removal of metals from the leachate.

Development of a Fast Modeling Approach for the Prediction of Scrap Preheating in Continuously Charged Metallurgical Recycling Processes

Improving the overall energy efficiency of processes is necessary to reduce costs, lower the specific energy consumption and thereby reduce the direct or indirect emission of gases that contribute to climate change. In many metallurgical processes, a large amount of energy is lost with the off-gas. In metallurgical recycling processes, off-gas often can be used to preheat the to-be-recycled metal scrap, leading to significantly higher energy efficiency. However, the application of preheating has the disadvantage that it often requires more precise planning in the design and better control of the process. In this paper, a simplified look at a continuously charged scrap preheating aggregate for the widely used electric arc furnace (EAF) in the steel processing industry is used as illustration. Continuous scrap charging in EAF-type furnaces in general has much higher demands on process control and general process knowledge, which is why they are found only very rarely. General issues and basic modeling approaches to mitigate such issues allowing a better process control will be described. In particular, a fast, one-dimensional modeling approach for the determination of the temperature distribution inside a constantly moving scrap bulk, with hot air (or exhaust gases) flowing through it, will be described. Possible modeling applications, assumptions, possible enhancements and limitations are shown. The first results indicate that this approach can be used as a solid basis for the modeling of scrap bulks with thermally thin parts, consisting of materials with similar thermodynamic material properties. Therefore, as a basis, this approach may help improve the design and control of future or existing preheating devices in metal recycling processes.