Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries

Energy-intensive manufacturing industries are characterised by high pollution and heavy energy consumption, severely challenging the ecological environment. Fortunately, environmental, social, and governance (ESG) can promote energy-intensive manufacturing enterprises to achieve smart and sustainabl...

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Vydáno v:Applied energy Ročník 337; s. 120843
Hlavní autoři: Ma, Shuaiyin, Huang, Yuming, Liu, Yang, Kong, Xianguang, Yin, Lei, Chen, Gaige
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.05.2023
Témata:
Adversarial interpretative structural model (AISM)
data collection
economic impact
Edge-cloud cooperation
energy
Energy-intensive manufacturing industries (EIMIs)
Environmental, social and governance (ESG)
governance
greenhouse gases
industry
Kernel principal component analysis (KPCA)
pollution
pollution control
principal component analysis
Technique for order preference by similarity to ideal solution (TOPSIS)
PANISD
SPFM
Kernel principal component analysis (KPCA)
PSEPHP
GHG
Energy-intensive manufacturing industries (EIMIs)
Environmental, social and governance (ESG)
EIIs
IoT
GDP
ESG
AISM
EIMIs
KPCA
MNMP
ISM
Edge-cloud cooperation
Technique for order preference by similarity to ideal solution (TOPSIS)
Adversarial interpretative structural model (AISM)
PPCPNF
MRCMCP
PANGCD
PANRS
SPNM
TOPSIS
Environmental social and governance (ESG)
ideal solution (TOPSIS)
Technique for order preference by similarity to
Energy-intensive manufacturing industries
Adversarial interpretative structural model
ISSN:0306-2619, 1872-9118, 1872-9118
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Popis
Shrnutí:Energy-intensive manufacturing industries are characterised by high pollution and heavy energy consumption, severely challenging the ecological environment. Fortunately, environmental, social, and governance (ESG) can promote energy-intensive manufacturing enterprises to achieve smart and sustainable production. In Industry 4.0, various advanced technologies are used to achieve smart manufacturing, but the sustainability of production is often ignored without considering ESG performance. This study proposes a strategy of edge-cloud cooperation-driven smart and sustainable production to realise data collection, preprocessing, storage and analysis. In detail, kernel principal component analysis (KPCA) is used to decrease the interference of abnormal data in the evaluation results. Subsequently, an improved technique for order preference by similarity to ideal solution (TOPSIS) based on the adversarial interpretative structural model (AISM) is proposed to evaluate the production efficiency of the manufacturing workshop and make the analysis results more intuitive. Then, the architecture and models are verified using real production data from a partner company. Finally, sustainable analysis is discussed from the perspective of energy consumption, economic impact, greenhouse gas emissions and pollution prevention. •An edge-cloud cooperation-driven sustainable manufacturing strategy is proposed.•A KPCA algorithm is used for data processing.•An improved TOPSIS-AISM algorithm is established for data mining.•The effectiveness of the models and the management implications are discussed.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0306-2619
1872-9118
1872-9118
DOI:10.1016/j.apenergy.2023.120843