Dominik Leverenz

Profile

My profile

I am an environmental engineer working in the field of circular bioeconomy. Through my research, I have learned that smart data collection and intelligent monitoring systems represent powerful tools for reducing food waste and mitigating environmental impact.

My research focuses on developing scalable, data-driven solutions with real-world applications. For example, the design of innovative food waste tracking systems that enable food services to optimize resource management and enhance data driven decision-making.

Beyond waste reduction, I explore innovative pathways to upcycle unavoidable organic waste streams into alternative bio-based products or valuable resources such as biofuels or proteins, ensuring no resources go to waste. Through a systems-thinking approach and technical innovation, I strive to contribute in creating resilient, sustainable food systems.

Research Vision

To achieve a truly sustainable and resilient future, we must decouple resource consumption and environmental impact from economic growth. Emerging digital technologies—including big data, machine learning, and artificial intelligence (AI)—can accelerate this transition by optimizing waste management and resource recovery.

My research contributes to this transformation by developing smart, interdisciplinary solutions that bridge science and industry, driving the shift toward a circular bioeconomy, through:

Smart Resource Management

• Developing intelligent human-technology-interaction systems for real-time decision-making such as smart food waste tracking systems
• Synchronization of material and information streams along supply chains to synthesize hard and soft data for digital product passports

Digitalization and Automatization

• AI-powered image recognition (e.g., optical and thermal imaging) for monitoring heterogeneous waste and resource streams
• Smart sensor technologies enabling machine-to-machine (M2M) communication for applications like waste sorting, contaminant detection, process monitoring, and fill-level measurement

Big Data and Artificial Intelligence

• Data mining techniques to analyze specific waste challenges and enhance AI-driven system modeling
• Process simulation and predictive modeling, including intelligent forecasting systems and life cycle assessment (LCA)