Project description
Pioneering hydrogen reduction technology for steelmaking
Residue from manufacturing processes presents a significant challenge to the steel industry. When recycling this residue the state-of-the-art Waelz process only focuses on recovering zinc, resulting in the loss of other valuable metals, especially iron. In addition, there is a significant carbon footprint, further exacerbating environmental concerns. To address these challenges, the EU-funded Dust2Value project will introduce an innovative hydrogen reduction technology that efficiently recovers all of the zinc while simultaneously producing an iron-rich material with a high metal content that can be used as a substitute for scrap. This process enhances circularity in steelmaking while slashing greenhouse gas emissions, paving the way for a sustainable future.
Objective
The Dust2Value project aims to transform the steelmaking residue recycling process by introducing an innovative hydrogen reduction technology that efficiently recovers valuable metals, such as zinc and iron, from steelmaking residue streams. This environmentally-friendly technology supports the circular economy, reduces greenhouse gas emissions, and contributes to a sustainable future for the steel industry.
The Dust2value process utilizes green hydrogen to reduce zinc oxide and iron oxide present in the residue, converting them into gaseous zinc that evaporates, re-oxidizes with water vapor to fine-dispersed ZnO particles which leave the furnace via the off-gas system and are recovered in bag house filters. Additionally, a secondary DRI is produced. The novel design of the Dust2Value process recovers heat and hydrogen generated during the re-oxidation of gaseous zinc to fine-dispersed ZnO particles, optimizing its energy efficiency. The project will design, construct and optimize a prototype rotary kiln that enables optimized heat transfer and gas-solid interactions, ensuring effective metal recovery.
A key aspect of the Dust2Value project is the integration of digitalization and machine learning techniques for process modelling and optimization. The project will leverage machine learning algorithms trained on kinetic data from thermogravimetry to create an accurate and comprehensive process model. This model will be used for the dimensioning of the prototype and will be further developed into a digital twin, providing a real-time representation of the physical process. The digital twin will enable continuous monitoring, analysis, and optimization of the process, ensuring optimal performance, and facilitating the rapid implementation of improvements. This advanced approach to process modelling will significantly enhance the Dust2Value process optimisation, driving innovation in the field of steelmaking residue recycling.
Fields of science
- engineering and technologyenvironmental engineeringwaste managementwaste treatment processesrecycling
- natural scienceschemical sciencesinorganic chemistrytransition metals
- social scienceseconomics and businesseconomicssustainable economy
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
8700 Leoben
Austria