Abstract
Regulative measures and rising energy cost foster the trend towards energy efficient manufacturing. However, companies are facing hurdles such as high effort and little expertise when implementing energy efficiency measures. Moreover, the embodied energy of products cannot be determined accurately in eco-assessments of factories. This paper presents a methodology for the reliable prediction of energy consumption of arbitrary manufacturing processes. It is based on minimal measurements and requires little effort and previous knowledge due to precise guidelines. Consumption models help to allocate energy cost to products, to calculate the product carbon footprint and to derive and validate measures to improve energy efficiency in production. The methodology has been applied in a medium size company with a large number of different products and machines.
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Abbreviations
- a, b :
-
coefficients
- C 0, C 1 :
-
machine-specific coefficients
- E annual :
-
annual energy consumption of process
- e heat :
-
energy demand per cm3 (induction heating)
- e i :
-
specific energy consumption (i th operation mode)
- E i :
-
Energy amount (i th operation mode)
- E part :
-
energy consumption per product
- E per place :
-
Energy per place on conveyor
- E stroke :
-
energy consumption per stroke
- m :
-
No. of different products going through a process
- n heater :
-
No. of active nozzle-heater units
- n m :
-
annual number of product m
- n per cycle :
-
parts per cycle
- n stroke :
-
number of strokes
- P avg :
-
averaged (constant) power level
- P coat, avg :
-
average power level of powder coating line
- P idle :
-
idle power level
- SEC :
-
specific energy consumption
- t annual,run :
-
annual runtime of machine
- t annual,setup :
-
annual setup time of machine
- t cycle :
-
cycle time of the product
- t idle :
-
idle time per product
- t stroke :
-
duration of stroke
- V :
-
volume of machined material
- V part :
-
volume of part
- x :
-
key process parameter
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Schmidt, C., Li, W., Thiede, S. et al. A methodology for customized prediction of energy consumption in manufacturing industries. Int. J. of Precis. Eng. and Manuf.-Green Tech. 2, 163–172 (2015). https://doi.org/10.1007/s40684-015-0021-z
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DOI: https://doi.org/10.1007/s40684-015-0021-z