Research Areas
P2O-Group Structure Overview
Research areas of the Process-to-Order Group include following topics:
Smart Architectures (SmArt)
Heterogeneous and distributed devices and services are one of the core components of intelligent cyber-physical production systems. Such systems mainly depend on technologies that provide a solution for the integration and connectivity of diverse and heterogeneous sources. Therefore, the development of architectural patterns and concepts is crucial for the performance of intelligent manufacturing systems.
The key principles of the Industrie 4.0 concept include networking, flexibility, modularity and reliability of production systems. As a result, processor orchestration plays an important role in modular automation. One of the team's key research areas is therefore the provision of modular and flexible solution architectures for automation problems.
Artificial intelligence (AI) and machine learning (ML) represent a promising solution to many problems faced by small and medium enterprises today. However, there are still many open technical questions regarding the application of AI/ML applications in industry. Therefore, another focus of the team is to facilitate the integration of AI/ML-based solutions into industrial applications.
More about Smart Architectures (SmArt)
Modular Plant Operations (ModOps)
The modularization of the process industry enables not only the integration of process equipment assemblies (PEA) across manufacturers, but also a variety of starting points for engineering processes. In the Modular Plant Operations group, we focus on how efficient processes can be used for flexible, adaptable and resilient operation of modular plants.
The core topic is MTP, the associated service-oriented automation of process modules and the orchestration of process plants.
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Hybrid Modeling, Simulation and Optimization (HybridMSO)
The Hybrid Modeling, Simulation, and Optimization (HybridMSO) group
researches the exciting convergence of knowledge-based and data-driven
modeling approaches. Their work is centered on accelerating process
development and improving online optimization, particularly through the
creation of digital twins for conventional and modular process plants using
hybrid modeling. Importantly, their approach significantly reduces the data
requirements for model development compared to purely data-driven
methods. They specialize in capturing complex and hard-to-model phenomena
through data-driven techniques and contribute to the development of design
of experiments methodologies for process and model enhancement.
More about Hybrid Modeling, Simulation and Optimization (HybridMSO)
Model Exchange and System Simulation (MXSS)
The Model Exchange & System Simulation (MXSS) group explores concepts for modeling and simulating distributed, large-scale, modular systems. In this context, we investigate how large-scale system simulations can be modeled and simulated reliably and resource-efficiently. For the vendor-independent exchange of simulation models for digital twins, methods for quality assurance of simulation models and interface description are considered.
More about Model Exchange and System Simulation (MXSS)
Publications
An overview of our research results can be found here: Publications
For further questions about research, feel free to contact us:
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Process-to-Order Lab
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