Mineral Wool Waste – From on-site Analysis to Recycling
© Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft der Montanuniversität Leoben (12/2024)
Mineral wool waste has become increasingly challenging to manage within the Austrian waste management system. At present, the only available option for mineral wool waste of unknown origin is landfilling, as there are no existing recycling options.

Limits and Challenges of the Calculation and Verification of the Recycling Efficiency of Lithium-ion Batteries posed by the new European Battery Regulation
© Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft der Montanuniversität Leoben (12/2024)
The new European Battery Regulation, introduced as part of the EU's Green Deal, presents significant challenges and changes in recycling lithium-ion batteries (LIB). This regulation not only raises the general recycling efficiency quotas from 50 % to 65 % by 2027 and 70% by 2030 but also sets specific recycling efficiency requirements for cobalt (Co), copper (Cu), lithium (Li), and nickel (Ni) at the elemental level.

Possible ways of utilising metal by-products from thermal phosphorus recovery
© Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft der Montanuniversität Leoben (12/2024)
The EU-funded demonstration project FlashPhos intends to show the feasibility of a thermochemical process to recover white phosphorus (P4) from sewage sludge.

The Role of Circular Economy in Industry 5.0
© Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft der Montanuniversität Leoben (12/2024)
The sustainability transformation of industry is one of the greatest challenges facing the European Union. One of the key levers in the transformation is the shift from a linear product life cycle to a circular one.

Viable recycling approaches to electrolyser stacks
© Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft der Montanuniversität Leoben (12/2024)
To ensure the sustainable use of the world's limited resources, it is essential to integrate principles of circularity into every new technology or product development. The ReCycle project deals with the reduction of environmental impact and related recycling capabilities (of hydrogen technologies) and aims to apply these circularity principles to technologies within the hydrogen value chain.

Metal recovery over the product life cycle
© Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft der Montanuniversität Leoben (12/2024)
The voestalpine Group's High Performance Metals Division is globally known for producing high-quality steels using well defined combinations of alloy elements and sophisticated production techniques. What makes these steels special is the set of exceptional properties such as resistance to corrosion and heat, high purity, and extreme durability that makes our products suitable for diverse applications, from aviation to construction of turbine blades and high pressure die casting tools.

The Borealis Borcycle™ M Demo Plant – Borcycle™ M as Best Practice
© Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft der Montanuniversität Leoben (12/2024)
Borcycleâ„¢ M is the mechanical recycling platform of Borealis, setting new standards for what is possible in mechanical recycling, enabling previously unattainable applications from cosmetics packaging through to mobility.

The heat is on! - From the material characterisation of spent refractory bricks to sensor training (practical examples from Project ReSoURCE)
© Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft der Montanuniversität Leoben (12/2024)
Refractory products are essential for high-temperature industrial processes, from steel and cement production to waste incineration and many more. Harsh environmental conditions in these applications result in the frequent renewal of refractory lining.

Implementation and evaluation of a real-time capable approach to sensor-based sorting using CNNs
© Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft der Montanuniversität Leoben (12/2024)
In state-of-the-art optical sorting, engineered image processing algorithms are commonly used to identify materials. However, for complex material textures and high- ensity material streams, these approaches are often unable to maintain the desired sorting quality. Convolutional neural networks (CNNs) have been proven to outperform such approaches in their ability to classify objects in images.

Comparative Analysis of Transfer and Continual Learning for Vision-Based Particle Classification in Plastics Sorting for Recycling
© Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft der Montanuniversität Leoben (12/2024)
In this study, we evaluate the effectiveness of transfer and continual learning techniques for vision-based trash particle detection and classification in plastics recycling. This task poses unique challenges for vision-based methods due to the great variety of particles in recycling material flows, their variability over time, and the lack of real recycling industrial datasets available for research.

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