4.1 Selection and Evaluation of Relevant Research Approaches
73
detailed evaluation of the machine’s energy consumption and the ability to analyze
process alternatives, time and energy studies are carried out to interpret the energy
levels of single machines and their components in the terms of technical and
process-based improvement of the resources [De+2007; De+2008]. Devoldere
et al. prove in several case studies that the main potential for energy savings can
be found in the fields of non-productive operation phases and present results for
savings in the laser cutting case of 12% and for bending presses of up to 60%.
Dietmair and Verl follow a machine control-based approach to determine
the consumption forecasting of milling machines [DV2009]. The basic method
for their study is the detailed breakdown of machine processes into individual
operating states, which are represented as mathematical models and considered
with different modeling accuracies. By additive linking, those single mathematical
models are composed to form the production consumption profile. By combining
the models with real operational data, Dietmair and Verl calculate reliable forecast values regarding the energy insensitivity of production machines. Thereby
they can identify critical machine components, make improvements regarding the
machine control or select process alternatives for inefficient machines.
Wang et al. present an approach for energy reduction based on an integrated optimization model for batch production load scheduling in energy-intensive
enterprises (EIE) without violating existing constraints of the production processes [Wa+2012]. The reduction of energy use during peak hours, the time-shifting
of energy use in off-peak times of the electricity tariff as well as reduction
of the energy demand through optimal load scheduling are considered in the
optimization approach. The formulation of the integrated optimization problem
includes batch production loads and power generation scheduling, as the approach
is addressing EIEs with self-generation power plants. After analyzing the production load curves and decomposing them into base loads and batch production
loads, the operating parameters for rescheduling the batch production are formulated. Subsequently, the power generation scheduling problem is formulated,
followed by the creation of the integrated optimization model containing integer
variables as well as nonlinear constraints. In order to be able to convert the optimization problem into a mixed integer linear programming model, linearization
techniques are introduced. Wang et al. conduct a case study in an iron and steel
plant, proving the effectiveness of their approach.
Chen et al. analyze the effective control of machine startup and shutdown
schedules to optimize the energy consumption considering given productivity
requirements for serial production lines with Bernoulli machines and finite buffers
[Ch+2013]. Besides using productivity performance measurements, an additional
energy performance system is introduced. To calculate the performance measures
73
detailed evaluation of the machine’s energy consumption and the ability to analyze
process alternatives, time and energy studies are carried out to interpret the energy
levels of single machines and their components in the terms of technical and
process-based improvement of the resources [De+2007; De+2008]. Devoldere
et al. prove in several case studies that the main potential for energy savings can
be found in the fields of non-productive operation phases and present results for
savings in the laser cutting case of 12% and for bending presses of up to 60%.
Dietmair and Verl follow a machine control-based approach to determine
the consumption forecasting of milling machines [DV2009]. The basic method
for their study is the detailed breakdown of machine processes into individual
operating states, which are represented as mathematical models and considered
with different modeling accuracies. By additive linking, those single mathematical
models are composed to form the production consumption profile. By combining
the models with real operational data, Dietmair and Verl calculate reliable forecast values regarding the energy insensitivity of production machines. Thereby
they can identify critical machine components, make improvements regarding the
machine control or select process alternatives for inefficient machines.
Wang et al. present an approach for energy reduction based on an integrated optimization model for batch production load scheduling in energy-intensive
enterprises (EIE) without violating existing constraints of the production processes [Wa+2012]. The reduction of energy use during peak hours, the time-shifting
of energy use in off-peak times of the electricity tariff as well as reduction
of the energy demand through optimal load scheduling are considered in the
optimization approach. The formulation of the integrated optimization problem
includes batch production loads and power generation scheduling, as the approach
is addressing EIEs with self-generation power plants. After analyzing the production load curves and decomposing them into base loads and batch production
loads, the operating parameters for rescheduling the batch production are formulated. Subsequently, the power generation scheduling problem is formulated,
followed by the creation of the integrated optimization model containing integer
variables as well as nonlinear constraints. In order to be able to convert the optimization problem into a mixed integer linear programming model, linearization
techniques are introduced. Wang et al. conduct a case study in an iron and steel
plant, proving the effectiveness of their approach.
Chen et al. analyze the effective control of machine startup and shutdown
schedules to optimize the energy consumption considering given productivity
requirements for serial production lines with Bernoulli machines and finite buffers
[Ch+2013]. Besides using productivity performance measurements, an additional
energy performance system is introduced. To calculate the performance measures