Sebastian Schrems

Architecture for Multilevel Monitoring and Control of Energy Consumption

Guaranteeing energy efficient operation of production systems through a-priori optimization is an extremely complex task. We therefore propose an alternative approach where controls can decide locally if an energy reduction is possible and set components to energy-optimal states. In this paper we present results from the research group ECOMATION describing the information flow that generates energy control loops on different levels based on model information and an appropriate communication and control infrastructure. The communication mechanisms and control structures in production systems and machines are presented which allow using models automatically and coherently on all required levels of detail and abstraction.

Modular Modeling of Energy Consumption for Monitoring and Control

A prerequisite to achieve energy efficiency in production through optimization is the possibility to use simulation models to predict the consequences settings, decisions and actions have on the energy consumption and thus on the lifecycle cost of production machines. But today’s production systems are dominated by agile structures and rapid changes of general conditions so that predictive energy consumption models need to be adaptable. Therefore in the ECOMATION project the modular modeling approach for energy consumption prediction presented here is being developed, which facilitates re-use of energy consumption models and thus minimizes the cost of finding the optimal operational profiles.

Simulation of the Energy Consumption of Machine Tools for a Specific Production Task

This paper describes an approach to simulate the energy consumption of machine tools for a specific production task by modelling the energetic interactions of the machine tools’ components and implementing the models in a simulation environment. By connecting a hardware machine control system with the simulation model, NC-code information is utilized and machine tool specific characteristics of the simulation model behaviour are achieved. Exemplarily, a specific machine tool is modelled and the energy consumption of a production process is simulated. The impact of the implementation of an improvement measure as well as the payback period of the investment is reviewed.

Methodology for an Energy and Resource Efficient Process Chain Design

In consequence of rising costs for raw materials and energy, as well as the customers’ increasing awareness of environmental protection the question of a sustainable production is becoming more and more important. A considerable part of the energy and resource demand in manufacturing is determined during the production planning process. Thus the chosen production technique and the consequential process design have a big influence on the consumption values. This paper presents a methodology to assess different manufacturing technologies, analyzing and estimating their energy and resource consumption and finally choosing the process chain that fits the best to the company’s requirements.

Automated Approach to Exchange Energy Information

Optimizing the product, the supply chain, the production process chain and steps as well as the structure and components of manufacturing systems can all contribute to resource and energy efficient production. It has to be kept in mind, though, that these strategies yield an “average optimization”. The operation in an energy optimal state is not considered.

Energy-Efficient Machine Tools through Simulation in the Design Process

Machine tool manufacturers are increasingly challenged by their customers to quantify the energy consumption of their product during the design process. Therefore the availability of instruments to consider the energy demand already in the early phases of the machine tool conception is essential. This article describes an approach to simulate the energy consumption of machine tools by modeling the energetic interactions of the machine tools’ components. Machine Tool manufacturers can thereby identify the optimization potentials already early in the design process. Exemplarily, components of a machine tool’s coolant system will be modeled and the systems energy consumption will be evaluated.

Simulation-Based Assessment of the Energy Consumption of Manufacturing Processes

The industrial sector offers a promising potential to reduce its immense need for energy. Many strategies to improve energy efficiency focus on the modernization of existing production systems. However, the most important potential lies in early stages of the production planning. To be able to introduce energy consumption as a decision factor in production planning, a methodology for a generic description of production machines is developed. This is methodology builds the basis for energy simulation models which support the determination of consumption values of alternative processes by the representation of whole production machines.

Ressourcenorientierte Bewertung alternativer Prozessketten

Kurzfassung Das Ziel einer nachhaltigen, besonders energie- und ressourceneffizienten Produktion gewinnt zunehmend an Bedeutung. Bestehende Fertigungen lassen sich dabei nur bis zu einem gewissen Grad optimieren. Bei der Neuplanung von Fertigungsprozessketten sind die Stellhebel ungleich größer, da dort die grundlegenden Entscheidungen zur eingesetzten Technologie und einem damit einhergehenden Energie- und Ressourcenverbrauch getroffen werden. Derzeit ist es nur möglich, bestehende Fertigungsketten mittels aufwendiger Datenerfassung und Bilanzierung zu bewerten. Bis heute mangelt es an einer Vorgehensweise, die es mit noch teils unsicheren Daten während der frühen Planungsphasen ermöglicht, bei Vorliegen alternativer Prozessketten die effizienteste Fertigungskette prospektiv bestimmen zu können. Dieser Artikel beschreibt eine Methodik für eine effiziente Datenerhebung und Bewertung sowie für deren Umsetzung in einem Software-Tool zur Implementierung in den Produktionsplanungsprozess. Betrachtet werden dabei die größten Herausforderungen zur Datenerfassung und der frühzeitigen Abschätzung der zu erwartenden Verbrauchskennwerte beliebiger Prozessketten.