74
4 State of the Art
of serial lines, Markovian analysis and a recursive procedure built on aggregation are used. Chen et al. validate their approach in an automotive paint shop
line, proving that scheduling the startup and shutdown times of machines lead to
significant improvements in energy efficiency.
Since 2009, the research group Ecomation is focusing on approaches regarding the control of energy consumption in production facilities and the increase of
energy efficiency by using automation techniques [EV2014]. As a part of this research team, Eberspaecher and Verl present a status-based approach focusing on
the energy-optimal use of unproductive times in manufacturing. Combining two
optimization algorithms, the Dijkstra-algorithm and the A*-algorithm, the most
energy-efficient production state for the machine can be found. In order to be
able to apply the optimization theory on machine tools, the energy consumption
model had to be defined as a graph and was implemented in C# on the control’s operating system. Eberspaecher and Verl prove during a prototypical
implementation that the developed consumption graph-based energy optimization
approach allows for a switching of energy saving modes during unproductive
times to spend them in an energy-optimal state [Eb+2014, p. 48].
Swat presents an approach for the design of energy efficient processes in serial
productions which enables the production planner to predict the energy requirements of production processes already in the early planning stages of a production
[Sw2015]. By building a business-related energy database, Swat creates the possibility to determine the total energy demand for all combinations of production
equipment and machine parameters. Thus, the production planner has the option
of selecting alternative process parameters and components to optimize the energy
demand. Swat validates his methodology based on the processes of electrochemical machining and honing. The deviation between the predicted and the measured
values for the energy consumption of the machine tools was only 4% [Sw2015,
p. 95].
Baumann et al. develop an integrated multi-criteria optimization and scheduling platform for energy consumption reductions in the glass tempering industry
[Ba+2016]. The platform consists of three components, a thermodynamic process
model of all energy-critical steps in glass manufacturing designed using Modelica 4 , a scheduling model to determine energy-efficient loading sequences for the
furnace using multi-criteria search techniques from discrete optimization and a
4 The development of Modelica was initiated by Hilding Elmqvist in 1996. „The basic
idea behind Modelica was to create a modeling language that could express the behavior
of models from a wide range of engineering domains without limiting those models to a
particular commercial tool […] Modelica is both, a modeling language and a model exchange
specification“ [Ti2001, p. 4].
4 State of the Art
of serial lines, Markovian analysis and a recursive procedure built on aggregation are used. Chen et al. validate their approach in an automotive paint shop
line, proving that scheduling the startup and shutdown times of machines lead to
significant improvements in energy efficiency.
Since 2009, the research group Ecomation is focusing on approaches regarding the control of energy consumption in production facilities and the increase of
energy efficiency by using automation techniques [EV2014]. As a part of this research team, Eberspaecher and Verl present a status-based approach focusing on
the energy-optimal use of unproductive times in manufacturing. Combining two
optimization algorithms, the Dijkstra-algorithm and the A*-algorithm, the most
energy-efficient production state for the machine can be found. In order to be
able to apply the optimization theory on machine tools, the energy consumption
model had to be defined as a graph and was implemented in C# on the control’s operating system. Eberspaecher and Verl prove during a prototypical
implementation that the developed consumption graph-based energy optimization
approach allows for a switching of energy saving modes during unproductive
times to spend them in an energy-optimal state [Eb+2014, p. 48].
Swat presents an approach for the design of energy efficient processes in serial
productions which enables the production planner to predict the energy requirements of production processes already in the early planning stages of a production
[Sw2015]. By building a business-related energy database, Swat creates the possibility to determine the total energy demand for all combinations of production
equipment and machine parameters. Thus, the production planner has the option
of selecting alternative process parameters and components to optimize the energy
demand. Swat validates his methodology based on the processes of electrochemical machining and honing. The deviation between the predicted and the measured
values for the energy consumption of the machine tools was only 4% [Sw2015,
p. 95].
Baumann et al. develop an integrated multi-criteria optimization and scheduling platform for energy consumption reductions in the glass tempering industry
[Ba+2016]. The platform consists of three components, a thermodynamic process
model of all energy-critical steps in glass manufacturing designed using Modelica 4 , a scheduling model to determine energy-efficient loading sequences for the
furnace using multi-criteria search techniques from discrete optimization and a
4 The development of Modelica was initiated by Hilding Elmqvist in 1996. „The basic
idea behind Modelica was to create a modeling language that could express the behavior
of models from a wide range of engineering domains without limiting those models to a
particular commercial tool […] Modelica is both, a modeling language and a model exchange
specification“ [Ti2001, p. 4].