requires the production of 10 m of a U-channel profile,
there is the need for the production of a total
128,000 m of profile, as shown in Fig. 20. There was
a calculation of the time and the total energy for the
production of profiles by using benchmarking data and
the second optimum solution (EEF_V2). There is 1 %
reduction in the time required and a 32.64 % reduction
in the total energy consumption, which yields to a
saving of 124 MWh over project time. The financial
impact, based on the industrial cost rates of energy [39],
incurs the saving of about 7,500 euros for the overall
production of profiles.
8 Conclusions
The robust design optimization methodology of the cold
roll forming process parameters towards energy efficiency
has been proposed, developed, and demonstrated
within the current paper. A methodology utilizing an
analytical model of the energy efficiency indicator and
orthogonal arrays for control and noise factors for robust
design was developed for the cold roll forming
process. Analytical model results for the roll forming
mill motor consumption have been experimentally verified
for energy consumption within [24]. Response to
the robust design optimization was the energy efficiency
indicator, as calculated through the analytical energy
efficiency model. Specific quality constraints were set
and checked through the finite elements model (FEM).
Such an FEM was constructed using the optimum set of
levels of the control factors of the cold roll forming
process parameters, while the feasibility of the optimum
energy efficient solution was checked.
A major innovation of the current study was the introduction
to an energy efficiency analytical model of the cold
roll forming process calculation within a robust design
optimization through orthogonal arrays. A robust design
simulation comprised a total of 144 runs (16×9 runs).
Using only FEM was rather impractical, as an FEM run
was computationally expensive and took about 1.5 days
to provide a solution. Utilizing a computationally efficient
but not cost expensive analytical model of energy
requires the production of 10 m of a U-channel profile,there is the need for the production of a total128,000 m of profile, as shown in Fig. 20. There wasa calculation of the time and the total energy for theproduction of profiles by using benchmarking data andthe second optimum solution (EEF_V2). There is 1 %reduction in the time required and a 32.64 % reductionin the total energy consumption, which yields to asaving of 124 MWh over project time. The financialimpact, based on the industrial cost rates of energy [39],incurs the saving of about 7,500 euros for the overallproduction of profiles.8 ConclusionsThe robust design optimization methodology of the coldroll forming process parameters towards energy efficiencyhas been proposed, developed, and demonstratedwithin the current paper. A methodology utilizing ananalytical model of the energy efficiency indicator andorthogonal arrays for control and noise factors for robustdesign was developed for the cold roll formingprocess. Analytical model results for the roll formingmill motor consumption have been experimentally verifiedfor energy consumption within [24]. Response tothe robust design optimization was the energy efficiencyindicator, as calculated through the analytical energyefficiency model. Specific quality constraints were setand checked through the finite elements model (FEM).Such an FEM was constructed using the optimum set oflevels of the control factors of the cold roll forming
process parameters, while the feasibility of the optimum
energy efficient solution was checked.
A major innovation of the current study was the introduction
to an energy efficiency analytical model of the cold
roll forming process calculation within a robust design
optimization through orthogonal arrays. A robust design
simulation comprised a total of 144 runs (16×9 runs).
Using only FEM was rather impractical, as an FEM run
was computationally expensive and took about 1.5 days
to provide a solution. Utilizing a computationally efficient
but not cost expensive analytical model of energy
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