Topics

Measurements for energy efficiency and transition to renewable energy sources

The energy transition poses major challenges for the industry. We develop concepts for a sustainable energy supply for industrial companies with the aim of converting to 100% renewable energy in the near future. For this purpose, we develop transition paths, taking into account state-of-the-art technologies, costs and other operational framework conditions.

Projects: Leopold, 5DIndustrialTwin, IFE

Publications:

• Optimizing the utilization of excess heat for district heating in a chipboard production plant

Sector coupling and circular economy

In the future, energy systems must become more efficient and resilient. To achieve this, it is essential to optimally couple and interconnect energy flows from different sources and with different energy carriers. Coupled energy systems also make it possible to integrate renewable energy sources to a greater extent, reduce energy losses and at the same time increase supply stability through redundancies.

An important concept in this context is the Industrial Energy Hub. Here, the focus is on the joint consideration of energy supply and production at the site. In the future, the concept of circular economy will also gain in importance. We are working on a holistic view of the entire industrial process including energy supply, as this is the only way to achieve our sustainability goals.

Projects: EDCS, Sinfonies, I4RD

Publications:

• Assessing the potential of combined production and energy management in Industrial Energy Hubs – Analysis of a chipboard production plant
• Combined optimization for retrofitting of heat recovery and thermal energy supply in industrial systems

Energy 4.0 and process improvement

Internet and communication technologies have permeated and revolutionized our entire lives. These technologies also open up many new possibilities for industrial applications. We are working to fully exploit the potential of these technologies in industrial energy systems and processes. In this context, the buzzword Energy 4.0 has been coined for energy systems. Our research focuses on the development of concepts for networked, flexible and intelligent energy systems of the latest generation.

Projects: 5DIndustrialTwin, DigiWind, DigiTEx, DigiKurztakt

Publications:

• Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
• Improving Control of Energy Systems With Reinforcement Learning: Application to a Reversible Pump Turbine
• Increasing the Flexibility of Hydropower with Reinforcement Learning on a Digital Twin Platform
• Enabling Reinforcement Learning for Flexible Energy Systems Through Transfer Learning on a Digital Twin Platform

Methods

Modeling with analytical and data-driven approaches

The foundation for most digital methods is an accurate, fast model of the underlying process. We combine our expert knowledge with classic and novel modeling methods, such as machine learning and data-driven approaches, to develop the optimal model for each use case.

Publications:

• Grey Box Modeling of a Packed-Bed Regenerator Using Recurrent Neural Networks
• Mechanistic Grey-Box Modeling of a Packed-Bed Regenerator for Industrial Applications
• MILP model for a packed bed sensible thermal energy storage

Optimization with mathematical methods

Determining the optimal configuration for the power system of a given industrial operation or calculating the optimal operating trajectory under the given circumstances is a major challenge due to the rapidly increasing complexity of these problems. We develop specialized formulations and solution approaches for design and operational optimization of industrial processes based on mathematical programming.

Publications:

• Combined optimization for retrofitting of heat recovery and thermal energy supply in industrial systems
• Optimizing the utilization of excess heat for district heating in a chipboard production plant
• Assessing the potential of combined production and energy management in Industrial Energy Hubs – Analysis of a chipboard production plant

Innovative automation approaches

Optimal control of plants and real-time monitoring with integrated error detection and diagnosis have the potential to make industrial processes significantly more efficient. Thereby, the digital twin approach opens up completely new possibilities. In collaboration with the Faculty of Computer Science, we are, for example, creating a specialized digital twin platform. For this platform, we are developing innovative services that create the actual added value of this technology.

Publications:

• Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
• Framework for Automated Data-Driven Model Adaption for the Application in Industrial Energy Systems
• Improving Control of Energy Systems With Reinforcement Learning: Application to a Reversible Pump Turbine
• Increasing the Flexibility of Hydropower with Reinforcement Learning on a Digital Twin Platform
• Enabling Reinforcement Learning for Flexible Energy Systems Through Transfer Learning on a Digital Twin Platform