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A big data driven analytical framework for energy-intensive manufacturing industries
Northwestern Polytech Univ, Peoples R China; Northwestern Polytech Univ, Peoples R China.
Northwestern Polytech Univ, Peoples R China.
Guangdong Univ Technol, Peoples R China.
Northwestern Polytech Univ, Peoples R China.
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2018 (English)In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 197, p. 57-72Article in journal (Refereed) Published
Abstract [en]

Energy-intensive industries account for almost 51% of energy consumption in China. A continuous improvement in energy efficiency is important for energy-intensive industries. Cleaner production has proven itself as an effective way to improve energy efficiency and reduce energy consumption. However, there is a lack of manufacturing data due to the difficult implementation of sensors in harsh production environment, such as high temperature, high pressure, high acid, high alkali, and smoky environment which hinders the implementation of the cleaner production strategy. Thanks to the rapid development of the Internet of Things, many data can be sensed and collected in the manufacturing processes. In this paper, a big data driven analytical framework is proposed to reduce the energy consumption and emission for energy-intensive manufacturing industries. Then, two key technologies of the proposed framework, namely energy big data acquisition and energy big data mining, are utilized to implement energy big data analytics. Finally, an application scenario of ball mills in a pulp workshop of a partner company is presented to demonstrate the proposed framework. The results show that the energy consumption and energy costs are reduced by 3% and 4% respectively. These improvements can promote the implementation of cleaner production strategy and contribute to the sustainable development of energy intensive manufacturing industries. (C) 2018 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD , 2018. Vol. 197, p. 57-72
Keywords [en]
Energy-intensive manufacturing industries; Big data analytics; Cleaner production; Data mining
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:liu:diva-150839DOI: 10.1016/j.jclepro.2018.06.170ISI: 000441998400007OAI: oai:DiVA.org:liu-150839DiVA, id: diva2:1245930
Note

Funding Agencies|National Natural Science Foundation of China [51675441, 51475096, U1501248]; Fundamental Research Funds for the Central Universities [3102017jc04001]; Circularis (Circular Economy through Innovating Design) project - Vinnova - Swedens Innovation Agency [2016-03267]; Simon (New Application of Al for Services in Maintenance towards a Circular Economy) project - Vinnova - Swedens Innovation Agency [2017-01649]

Available from: 2018-09-06 Created: 2018-09-06 Last updated: 2018-09-06

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • Other style
More styles
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  • de-DE
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  • Other locale
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Output format
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