Energy-cyber-physical system enabled management for energy-intensive manufacturing industries

Ma, Shuaiyin, Zhang, Yingfeng, Lv, Jingxiang, Yang, Haidong and Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 2019. Energy-cyber-physical system enabled management for energy-intensive manufacturing industries. Journal of Cleaner Production 226 , pp. 892-903. 10.1016/j.jclepro.2019.04.134

Full text not available from this repository.

Abstract

Cleaner production is a green production way to minimize emissions and waste as well as maximize product output. Cleaner production has proven itself as an effective way to reduce energy consumption and improve material utilization during the whole manufacturing process. However, the implementation of cleaner production strategy is facing barriers due to the lack of applying advanced technologies such as cloud manufacturing, Internet of Things, and cyber-physical system. Based on these advanced technologies, this paper presents an architecture of energy-cyber-physical system enabled management for energy-intensive manufacturing industries to promote the implementation of cleaner production strategy. An energy-cyber-physical system enabled green manufacturing model for the future smart factory is proposed. Then the qualitative and quantitative synergetic models based on energy-cyber-physical system are developed for cleaner manufacturing. Finally, an application of a partner company is presented to demonstrate the proposed framework and synergistic models. The results show that energy consumption can be greatly reduced using synergistic evaluation models. Further managerial implications are summarized to increase the level of synergy of energy-cyber-physical system, which can achieve cleaner production strategy by making energy-efficient decisions from different departments.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0959-6526
Date of Acceptance:	11 April 2019
Last Modified:	04 Nov 2022 12:12
URI:	https://orca.cardiff.ac.uk/id/eprint/122256

Citation Data

Cited 96 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item