Potenzial: Energy Efficiency

This study examines the contribution of natural building materials, in particular loam and wood, to the sustainable development of logistics infrastructure, assessing ecological, economic, and technical dimensions across the entire life cycle. Potentials, restrictions, and supportive framework conditions are identified based on literature analyses and expert interviews. Wood proves to be technically mature and ecologically advantageous, especially in high rack construction, while loam offers high potential for energy- and resource-efficient construction. The study concludes with recommendations for research, policy, and practice to establish circular construction methods in logistics.

Electric trucks (e-trucks) play an important role in reducing CO₂ emissions especially on short distances in pre and post-carriage in combined road-rail freight transport (CT). Using the example of a CT terminal, this article highlights the logistical and energy challenges involved in using e-trucks to establish suitable charging infrastructures and ensuring a reliable power supply.

Digital twins connect physical and digital systems, furthering efficiency, enabling predictive maintenance, and allowing the production of more customized products. Despite these advantages, challenges such as high costs, data synchronization, and security risks hinder widespread adoption. This article explores the potential of digital twins and examines key barriers to integration and implementation, also considering some industrial applications including additive manufacturing as a relevant use case.

The supply of production processes and buildings with thermal energy represents a significant share of the total energy demand of an industrial site. The use of industrial waste heat offers a way to reduce the external purchase of final energy. Due to the lack of transparency and the complexity of such measures, their potential often remains untapped. In the research project ETA im Bestand a user-oriented software solution was prototypically implemented. The software solution enables the development and evaluation of industrial energy concepts. Approaches from the research area of operations research and dynamic simulation are applied.

Energy efficiency is becoming increasingly important in all sectors of the manufacturing industry. Companies are currently feeling the pressure of exorbitant energy prices very clearly, as well as the additional challenge of becoming CO2-neutral by 2045. With technologies from the field of machine learning (ML), innovative solutions can be developed that enable energy-efficient product manufacturing. In this way, ML-supported process control can make a decisive contribution to increasing the sustainability and competitiveness of a company. Decisive for ML-supported process control are the process- and raw material- dependent parameters, which are significantly responsible for the quality of the final product. The subject of this paper is a procedure for analyzing the complex relationships between the relevant influencing parameters for increasing energy efficiency in the manufacturing industry. (Only in German)