Projects
There are 5 results.
AI-driven decontamination technologies for repurpose/recycle to meet food-contact regulations using light (Light-AIClean)
The project aims to develop a chemometrics-assisted decontamination (DC) process for waste plastic with AI-driven quality control and a renewable energy-based DC technology. By utilizing visible light and a reusable catalytic system, it seeks to replace resource-intensive methods like hot water washing and gamma irradiation. AI techniques, including neural networks and reinforcement learning, will optimize efficiency, reducing resource use. The process will be tested in a photoreactor and automated DC setup, benefiting the recycling industry, AI developers, and environmental sustainability while promoting circular economy principles.
DeB-AT – Detection and separation of portable batteries from mixed waste using sensor technology and artificial intelligence
The DeB-AT project plans the development of a laboratory or pilot plant demonstrator for the targeted separation of batteries from mixed waste streams. The concept of the demonstrator follows the methodological elaboration of the necessary requirements of optical sensors and the separation technology for AI-supported detection of the general population of batteries.
DigiTech4CE - Digital key technologies for circular production
DigitTech4CE analysed industrial cycles in discrete, digitalised production; their participants, advantages/disadvantages and framework conditions, as well as the key digital technologies required. Fields of action were developed, according to the needs of Austrian industry. Recommendations for action serve the development of sustainable, Austrian production that builds and expands competitiveness through circular innovations.
KI4COMP - AI-based prediction of moisture distribution in composites
The project aims to develop an AI model for predicting moisture distribution and the mechanical properties of composite materials under various environmental conditions. By using integrated sensors and machine learning, more precise and easily accessible predictions can be achieved. This facilitates material development, reduces testing efforts, and promotes sustainable innovations through the increased use of natural fibers.
OPENing Re-Use – Optimal planning decisions in the re-use sector
In an operational context, companies in the circular economy are faced daily with the question of whether a used product should be repaired, remanufactured, refurbished or recycled. The decision on what to do with used products is fraught with a great deal of uncertainty and must be made on a case-by-case basis - product-individually and depending on factors such as brand, condition, age, demand or recycling possibilities. As part of the "OPENing Re-Use" project, a business planning tool is being developed to support companies in their re-use planning, thereby increasing the efficiency of re-use processes and making re-use activities even more competitive with the purchase of new products.