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4 State of the Art
Weinert presents a methodology focusing on the time-based structuring of
the energy inputs depending on the operating state of the machine [We2010].
Combining the time spend in a certain operating state and the associated specific
power consumption profile of a resource, he defines an own classification system.
He assumes that similar resources have comparable power consumption profiles
and develops a mathematical functional description of the real consumption profiles. By combining the energy profiles to a sequence, the description of a process
chain in an appropriate level of detail is reached. The grouping of individually
modeled process chains leads to the description of the total energy consumption
of a production. Weinert validates the concept in a case study in the field of a
mechanical production comparing the forecast quality of his approach to reference
measurements. He simulates the production processes using Visual Components
3DCreate and develops a prototype software to model the energy profiles. By
introducing several interfaces, he enables the exchange of process data with the
simulation software and thus creates a tool to support the modeling of energy use
in production systems.
Berglund et al. present an approach to improve the production sustainability by measuring and evaluating the concerted effect of process energy from
machine operations and the facility energy from building services [Be+2011].
They invent a state-based DES model incorporating processes, process energy,
and facility knowledge. Integrating real production data, process data, and facility
energy data, Berglund et al. validate their approach in an engine block production. They prove that their approach has the potential to reduce manufacturing
energy consumption even though the model generation is highly complex.
Wolff, Kulus, and Dreher present attempts to include energy aspects in
the material flow simulation [WKD2012]. Assuming that it is possible to model
energy consumption as a constant or time-dependent status, they introduce a classification system for the machine states. To ensure that the functionality can be
removed at any time for a model, the principle of modularity was adopted for
elements of the energy simulation. Thus, the end user is able to switch the energy
calculation modules on and off according to his requirements. Taking into account
negative effects on the system performance, the energy simulation and the material
flow simulation are run in parallel. To realize the import of the energy consumption simulation, three modules are required: a module for parametrization and
import, a calculation module as well as a model for statistics and visualization.
Conducting pilot studies in the automotive industry, the energy calculation module
was validated. For further documentation purposes as well as the presentation
and deeper analysis of the results, the researchers provide additional options for
4 State of the Art
Weinert presents a methodology focusing on the time-based structuring of
the energy inputs depending on the operating state of the machine [We2010].
Combining the time spend in a certain operating state and the associated specific
power consumption profile of a resource, he defines an own classification system.
He assumes that similar resources have comparable power consumption profiles
and develops a mathematical functional description of the real consumption profiles. By combining the energy profiles to a sequence, the description of a process
chain in an appropriate level of detail is reached. The grouping of individually
modeled process chains leads to the description of the total energy consumption
of a production. Weinert validates the concept in a case study in the field of a
mechanical production comparing the forecast quality of his approach to reference
measurements. He simulates the production processes using Visual Components
3DCreate and develops a prototype software to model the energy profiles. By
introducing several interfaces, he enables the exchange of process data with the
simulation software and thus creates a tool to support the modeling of energy use
in production systems.
Berglund et al. present an approach to improve the production sustainability by measuring and evaluating the concerted effect of process energy from
machine operations and the facility energy from building services [Be+2011].
They invent a state-based DES model incorporating processes, process energy,
and facility knowledge. Integrating real production data, process data, and facility
energy data, Berglund et al. validate their approach in an engine block production. They prove that their approach has the potential to reduce manufacturing
energy consumption even though the model generation is highly complex.
Wolff, Kulus, and Dreher present attempts to include energy aspects in
the material flow simulation [WKD2012]. Assuming that it is possible to model
energy consumption as a constant or time-dependent status, they introduce a classification system for the machine states. To ensure that the functionality can be
removed at any time for a model, the principle of modularity was adopted for
elements of the energy simulation. Thus, the end user is able to switch the energy
calculation modules on and off according to his requirements. Taking into account
negative effects on the system performance, the energy simulation and the material
flow simulation are run in parallel. To realize the import of the energy consumption simulation, three modules are required: a module for parametrization and
import, a calculation module as well as a model for statistics and visualization.
Conducting pilot studies in the automotive industry, the energy calculation module
was validated. For further documentation purposes as well as the presentation
and deeper analysis of the results, the researchers provide additional options for