energy efficiency

Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO₂ footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint.

Facility managers for industrial properties are faced with the challenge of optimizing the energy efficiency of their facilities in the face of ever-increasing energy demand and rising energy costs. Digital processes that enable the comprehensive monitoring, analysis and control of energy demand offer an effective way to reduce costs, increase energy efficiency and make optimal use of resources. Based on IoT technologies and open-source software, a cost-effective, wireless and flexible retrofit solution for real-time energy data collection has been developed.

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)

Production planning and control (PPC) software schedules the availability and optimal use of human and operation resources. The system must allocate the operation steps and operation resources to the machines in the right order under the control of qualified personnel. The scheduling takes place according to customer-specific objectives such as adherence to delivery dates. These objectives compete against the cost-specific objectives such as high workload of the machines. For the cost specific objectives, the topic of resource and energy efficiency become the focus of attention.

The Green Factory Bavaria is a research project, in which a platform at several locations in Bavaria is developed, in order to increase the resource efficiency in manufacturing companies. The platform shall serve as research-, demonstration- and training purposes. In Augsburg a process chain was developed, which consists of an additive manufacturing step, a cleaning and a packaging step. The research foci of those areas as well as the training concept are going to be presented in this article.