Energy Consumption Characterization based on a Self-analysis Tool: A Case Study in Yarn Manufacturing
| Autor: | Piero De Sabbata, Gessica Ciaccio, Giuseppe Nigliaccio, Angelo Frascella, Samuele Branchetti, C. Petrovich |
|---|---|
| Přispěvatelé: | Petrovich, C., Ciaccio, G., De Sabbata, P., Frascella, A., Nigliaccio, G., Branchetti, S. |
| Rok vydání: | 2019 |
| Předmět: |
Textile industry
Energy Efficiency Computer science 020209 energy Benchmarking Energy Consumption Energy Efficiency Sustainable Economy Textile Industry Yarn Manufacture Energy Consumption 02 engineering and technology Sustainable Economy Manufacturing 0202 electrical engineering electronic engineering information engineering Production (economics) 0505 law Yarn Manufacture Consumption (economics) business.industry 05 social sciences Yarn Benchmarking Energy consumption Manufacturing engineering Textile Industry visual_art 050501 criminology visual_art.visual_art_medium business Efficient energy use |
| Zdroj: | SMARTGREENS |
| Popis: | Even if energy efficiency represents a crucial issue for the sustainability of the manufacturing industry, the companies need to be encouraged in investing their resources for this goal. One of the means to facilitate this effort is the comparison of the energy performances with similar factories. Nevertheless, since the enterprises are very heterogeneous, these performance values have, even within a specified manufacturing sector, a high variability and therefore risk not to be representative. The dispersion of these data has to be decisively decreased. This goal is pursued here by means of an energy consumption characterization model based on: 1. a self-analysis software tool collecting energy consumption data in a simple and homogeneous way; 2. the clustering of the factories; 3. the separation of the auxiliary energy uses from the production process energy consumption. The method is here applied to textile industry with a focus on the electrical consumption in yarn factories. The outcomes show a correlation with some production variables, such as the raw materials, and allow to reduce the relative errors of the energy performances of different factories from about 80% to about 25-40%. In this way, energy reference indicators can be built in an acceptable and representative way. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|