Abstract: An important part of increasing the energy and resource efficiency in companies is the reduction of energy consumption of production plants. In order to achieve this, suitable energy management concepts have to be developed. Energy management concepts involve collecting all required information and making decisions based on the evaluated data. This paper focuses on the approach of shutting down individual plant components in unproductive phases. Because manually shutting down and starting up plants is risky and time-consuming, plants are often left in a state in which they consume a lot of energy, despite not producing any parts, due to both scheduled and unexpected stops. For this reason, adequate energy management concepts are needed that automatically shut down unneeded plant components and restart them in time for the next productive phase. Multiple dependencies between plant components in the context of production and process flow lead to a massive increase in complexity. Subsequently, such concepts are rarely programmed in the control software. In this paper, we provide an approach that implements the energy management concepts as a superordinate entity at the process control level, which enables a holistic plant overview. Using flexible algorithms, the system should be able to make autonomous decisions about the ideal energetic state of the individual plant components. In order to minimize the effort of adding new plants, the developed algorithms should self-adapt to the respective plant configuration autonomously. In addition to machine learning algorithms, the functional analysis of production plants and knowledge concerning the structure of the plants gained from engineering tools are used.
Abstract: Assembly lines consist of chained or unchained stations, yet usually only single stations are regarded individually for process and quality analytics. Since the quality of the final product depends on interactions of process parameters along the assembly flow, it is insufficient to analyze process parameters of each station separately. Therefore, data of every single assembly station along the assembly line has to be collected and stored. To explore such a big amount of multidimensional data and their correlations, different techniques are established. In this paper, assembly flows and their respective data are visualized using a parallel coordinates plot (PCP). Here, this technique visualizes process parameter combinations along the whole assembly chain. The contribution of this paper is to prove that the presented approach enables a fast detection of stations with malicious impacts on the product quality, when it comes to complex assembly lines. The goal is to help users to detect global problems in those lines, not only single station problems. Furthermore, the relevance of various processes to the quality (good or defective) of the final good shall be revealed.
Abstract: In order to satisfy upcoming needs for detailed traceability of products, components and manufacturing conditions, identification of every part is required. As for bulk goods applied marks are generally not appropriable due to variable costs, aggregation of individual parts manufactured under similar conditions to batches is carried out and these batches are identified and linked to information for tracking and tracing. Currently there is no standard for dividing bulk goods into batches. Hence, there are varying, company-specific approaches regarding rules for batch segregation and batch number definition. Thereby, transparency for subsequent stages of the supply chain is almost non-existent.In this paper we developed a method for batch segregation and number definition considering quality-related impacts. By identifying relevant influences and associated characteristics affecting events, basic parameters regarding segregation of batches can be found. As, especially in the automotive industry, FMEA and similar tools already are required, deriving this information is possible at low effort. Based on the factors identified, a meaningful batch ID may be generated including information on changes in particular parameters by encoding each parameter into the ID. While the overall objective should be outright informational transparency, sharing manufacturing data is not realistic in the short term. Therefore, our approach increases information sharing between members of the supply chain whilst protecting manufacturing know-how.The proposed systematic for batch IDs is supposed to enable subsequent participants in the supply chain to identify failure and scrap causes by communicating meaningful ID including relevant parameters. Hence, data analysis should be able to track down issues to changes in the manufacturing process at the supplier much faster and at lower effort than before. Additionally this may be a first step to pro-actively identify expectable quality issues due to information given in the ID and knowledge about correlation between different components and conditions with automated algorithms or some sort of artificial intelligence.
Abstract: The European and International energy policy targets energy efficiency improvement and CO2 reduction. Therefore, renewables are key players within our future energy supply system. In order to ensure security of energy supply and to avoid load peaks, highly interconnected power grids, also known as smart grids, have to deal with irregularities of phenomena such as sunlight, wind, tides and intelligently control these dynamic changes. Such a smart grid is denoted as a complex system. That is characterized by a large quantity of heterogeneous actors, collaborating within a dynamic and highly interconnected network. This results in big challenges in the steps planning, realizing and expanding such a system. These steps need to be based on a standardized description process to cope with the aforementioned challenges. In this paper, we combine the Smart Grid Architecture Model (SGAM) with the Use Case Methodology for describing processes in complex systems. Our new UC-SGAM Methodology exploits the benefits of both approaches and can also be applied to the Reference Architecture Model Industry 4.0 (RAMI4.0). The developed methodology is examined on two overload scenarios of the smart grid. It strongly enhances comparability and simplifies the visualization of complex use cases.
Abstract: Energy efficiency is a well-known and often implemented energy-related activity with a variety of different characteristics and fields of application. Energy efficient solutions, especially for pneumatic and electric intensive processes, are usually applied to minimise the electric and pneumatic energy consumption in the field of production. Especially in complex machinery it must be avoided that the energy consumption influences the productivity or quality of the product. On the other hand, due to volatile energy supply from renewable sources, there is a growing awareness of energy flexibility, which is mainly implemented on energy-intensive processes such as metal production, foundries, chemical processes and paper industry. An increase of the energy flexibility potential of machines often influences the energy efficiency. In other cases, energy efficiency and energy flexibility actions can be conducted without an interaction. This paper compares the definition and characteristics of energy efficiency as well as energy flexibility actions. An application of energy flexibility as well as efficiency actions can lead to optimised energy consumption behaviour of production machinery.
Abstract: Reducing the use of fossil fuels for energy production is one of the main objectives in 21st century. In order to achieve this target renewable energy resources (like agricultural waste) in biogas plants can be used. An anaerobic bacterial fermentation process digests the substrates into methane and carbon dioxide. The process itself has strong fluctuations in terms of net methane yield due to different amounts and composition of agricultural influents. For increasing the space time yield two main difficulties are encountered. The first one is system-specific and includes stirrer design and reactor geometry. The second affects the biotechnological fermentation process. The following work is focusing on the fermentation process. The determination of critical parameters for the optimization of the anaerobic microbial digestion is investigated. An economic approach for solving these problems is only feasible by using mathematical models and simulation. Consequently two fermentation models are compared by regarding parameter sensitivity and critical operational points. The first one is based on simple Monod-kinetics while the second one is extended with two steps of fermentation and therefore two different microbial consortia and additive inhibition effects. The complex model is able to describe different phenomena in more detail. But its estimability and therefore its validation is difficult without further investigation of the model structure and the reduction of the model complexity. One important result of the investigation is that stable process conditions with simultaneous high yields are depending on a careful adjustment of the loading rate and therefore requiring precise model parameters.
Abstract: Despite numerous research activities, guidelines and regulations, the exploitation of energy and resource efficiency potentials at companies in the field of electric drive technology is still lagging behind. Existing catalogues of measures and research results are mostly too generic or too theoretic for companies, especially for small and medium-sized enterprises (SME). Thus, this paper proposes the development of a user-oriented software system that supports the consideration of sustainability aspects within the product and process development of electric drives. The core component of this concept is a knowledge-based system (KBS), which reveals the wide range of energy and resource efficiency potentials along the whole product life cycle. In particular, correlations that exist between the individual saving potentials are to be mapped. By presenting the general solution concept and current stage of development, this paper provides the basis for future research which should focus on the further elaboration and prototypical implementation of the system proposed here.
Abstract: There are numerous levers and constraints affecting the sustainability of electric motors. While previous work mostly focuses on individual energy and resource efficiency potentials within single phases of the motor’s lifecycle, this paper summarizes sustainability aspects from three different perspectives, namely from the market, product and process view. The first part of this paper analyzes the electric motor market to emphasize the significance of this industry and to outline the importance of state-regulated efficiency classes. The second part provides an overview of the range of electric motor types and their sustainability characteristics. The third part contains an analysis of manufacturing processes in terms of energy and resource efficiency by pointing out appropriate key figures and optimization approaches. In doing so, the connection of the three perspectives market, product and processes offers a holistic view on sustainability aspects of electric motors.
Abstract: Implementing condition monitoring functionality in production machinery often proves to be a difficult task. Device-and process-specific algorithms must be created while inhomogeneous industrial communication networks hinder the integration of control signals and process variables. Further challenges arise from the advance of flexible Cyber-Physical Systems (CPS) and the Industrial Internet of Things (IIoT). They demand a service-oriented condition monitoring architecture, which seamlessly adapts to quickly changing production topologies. Existing condition monitoring systems (CMS) and reference architectures for CMS however do not possess the capabilities to meet the requirements originating from CPS and the IIoT. This paper presents a software module serving as a generic framework to ease the implementation of decentralized condition monitoring functionalities. A decentralized component, the monitoring module, constitutes a part of a holistic condition monitoring architecture managed by a central server deployed on an edge or cloud server. Therefore, the monitoring module offers an interface through which its data processing flow and algorithms are entirely remotely configurable during operation. Algorithms are encapsulated in function blocks, which can easily be setup and interconnected. Scalability is ensured by the use of web technology optimized for efficient data handling and parallel data-processing. The examined use cases show the potential of the developed software module for its deployment in the use case of monitoring turn-mill-centers.