1.
Every delivery is automatically recorded and monitored, and the waste is analyzed for quality characteristics (including particle size and dust content), material composition, and contaminants, with real-time notifications.
By tracking vehicle license plates, we ensure end-to-end documentation and traceability.
2.
Our monitoring system continuously tracks the incoming and outgoing material flow in the silo in real time. Using a continuous state estimation, it determines, among other things, the volume, mass, contaminants, and material homogeneity in the storage silo.
Based on this assessment of the bunker’s condition, the system generates specific recommendations for targeted material mixing. This improves homogenization within the bunker and supports stable and more efficient feeding of the hoppers for thermal recycling.
3.
Using sensors on the hopper, our AI predicts both calorific value and emission trends even before combustion begins.
Early warnings of spikes or drops in calorific value, as well as short-term HCl forecasts, enable proactive plant control and the targeted use of flue gas cleaning agents. This leads to sustainable improvements in process stability, efficiency, and emission compliance.
The WasteAnt system consists of two key components: the on-site hardware and our AI-based software. The sensor box captures relevant image, motion, and temperature data directly at the facility, while the Edgebox processes this data locally and prepares it for analysis.
Building on this, our AI software evaluates the collected information, identifies contaminants, hotspots, and material properties, and presents the results clearly in the dashboard. The result is an intelligent integrated system that combines sensor technology, computing power, and AI analysis.
Our performance, measurable in real time, accuracy, and industrial applications
Accuracy in contaminant detection
Real-time Contaminant & Temperature Detection
MJ/kg Standard deviation in the calorific value prediction
Facility operators already trust us
We develop AI-based solutions for analyzing and evaluating unstructured waste streams. Our goal is to create transparency, optimize processes, and transform waste into a valuable resource.
The research project “Differentiation of Biogenic and Fossil CO2” and the collaborative project “Optimized Control of Waste-to-Energy Plants” are funded by the BAB and the Senator for Economic Affairs, Ports, and Transformation.
