Energy Efficiency Indicators for Textile Industry Based on a Self-analysis Tool
Samuele Branchetti, Carlo Petrovich, Gessica Ciaccio, Piero De Sabbata, Angelo Frascella, Giuseppe Nigliaccio
Articolo Conferenza internazionale con referaggio
Energy efficiency in the industry sector represents a crucial issue for the sustainable development, but manufacturing companies are not still implementing, on a mass scale, energy saving actions. One of the most important barriers is that many companies are scarcely aware about their consumptions and need reference values to compare their energy performances with similar factories. Nevertheless, since the enterprises are very heterogeneous and the production chains is often fragmented, these values have a high variability. The dispersion of these data has to be decisively decreased, but keeping generality to be representative. This goal is pursued here for the textile sector analysing datasets regarding 140 European factories. The datasets were retrieved by means of a self-analysis software tool, collecting energy consumption data in a simple and homogeneous way. The analysis of the data was performed using energy efficiency indicators and by clustering the factories. The method is here applied to textile industry and the outcomes show a correlation with some production variables, such as the raw materials, the kind of process and the price of the final products. The approach based on a regression analysis between energy consumptions and production has allowed to reduce the relative errors of the energy performances of different categories of factories from more than 100% to about 2540% in many cases. In this way, energy efficiency indicators can be adopted as acceptable and representative references.
Branchetti S., Petrovich C., Ciaccio G., De Sabbata P., Frascella A., Nigliaccio G. (2021) Energy Efficiency Indicators for Textile Industry Based on a Self-analysis Tool. In: Helfert M., Klein C., Donnellan B., Gusikhin O. (eds) Smart Cities, Green Technologies and Intelligent Transport Systems. SMARTGREENS 2019, VEHITS 2019. Communications in Computer and Information Science, vol 1217. Springer, Cham. https://doi.org/10.1007/978-3-030-68028-2_1, ISSN: 1865-0929 , 1865-0937; ISBN: 978-3-030-68027-5 , 978-3-030-68028-2, DOI: 10.1007/978-3-030-68028-2_1