Energy Efficiency

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)

An increasingly fluctuating energy production and therefore fluctuating energy prices are new challenges for sustainable and cost-effective manufacturing. Due to a growing volatility in the energy supply, industrial consumers are increasingly involved in the balance of the energy system. The goal is to consume energy only when it is cheap and reduce the power consumption during periods of high energy prices. This leads to a conflict between the flexible use of energy and the production output which has to be balanced. While Smart Grids solutions for regional and higher structured energy networks balance supply and demand, solutions for the industrial environment are still missing. In this article, the current research on the development of a decentralized control solution for industrial energy system is presented.

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.

Due to a lack of energy consumption transparency, manufacturing companies are struggling to identify the energetic cost drivers within a factory. Energy transparency can be reached through the aid of suitable measuring technologies. However, the gained transparency must be used intelligently to obtain economic benefits. One possible solution is the use of modern information technologies that are provided by the Industry 4.0. Maximum synergies can only be achieved through a holistic implementation of Industry 4.0, by taking the business model, the operational processes, the technology and the employees into account. A corresponding strategy for the implementation of Industry 4.0 in the context of energy transparency is introduced in this article.

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.

Range and individualisation of diversified logistic services vary substantially. A unique indicator for specific CO2-emission is not a viable option. The author proposes to aggregate individual CO2-efficiency trends of sub-entities by adding all singular tendencies during a reporting period weighted against the overall CO2-emission of the reporting entity or group of entities. The result is a weighted average tendency giving a viable indication of the specific CO2-emission trends of a logistics service provider.