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

• Published in: Applied energy Vol. 337; p. 120843
• Main Authors: Ma, Shuaiyin, Huang, Yuming, Liu, Yang, Kong, Xianguang, Yin, Lei, Chen, Gaige
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2023
• Subjects: Adversarial interpretative structural model (AISM); Edge-cloud cooperation; Energy-intensive manufacturing industries (EIMIs); Environmental, social and governance (ESG); Kernel principal component analysis (KPCA); 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
• DOI: 10.1016/j.apenergy.2023.120843

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.