Multi-level energy efficiency evaluation for die casting workshop based on fog-cloud computing

• Published in: Energy (Oxford) 2021-07, Vol.226, p.120397, Article 120397
• Main Authors: Cao, Huajun, Chen, Erheng, Yi, Hao, Li, Hongcheng, Zhu, Linquan, Wen, Xuanhao
• Format: Article
• Language: English
• Subjects: Casting; Casting machines; Cloud computing; Die casting; Die casting workshop; Energy conservation; Energy consumption; Energy efficiency; Energy efficiency evaluation; Energy utilization; Evaluation; Fog; Fog computing; Manufacturing industry; Parameter identification; Performance evaluation; Real time; Sustainable production; Workshops
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.120397

Die casting is a complex process performed in harsh working environments. Driven by cost and environmental pressure, die casting, as one of the most energy-intensive manufacturing processes, has received increasing attention on enhancing energy efficiency toward greener and more sustainable manufacturing. Energy efficiency evaluation is a starting point for energy audits and analysis of energy-saving scenarios, while complex production conditions in the die casting workshop (e.g. product changeover, technology improvements, and degradation of equipment performance) require even higher real-time and dynamic performance of energy efficiency evaluation. To this end, this paper proposes a multi-level energy efficiency evaluation framework based on fog-cloud computing. Accordingly, real-time parameter identification models and dynamic energy efficiency evaluation method are proposed. An industrial case study of die casting workshop has demonstrated the feasibility and effectiveness of the proposed approach. The results reported that the overall equipment effectiveness and energy utilization ratio of die casting units increased by 3% and 7%, respectively, and energy consumption per kilogram of the die casting workshop was reduced by 7.9%, showing its great potential in identifying energy efficiency improvement opportunities. •A fog-cloud computing based energy efficiency evaluation framework is proposed.•Fog computing based real-time parameter identification models are established.•Fog-cloud computing oriented multi-level dynamic evaluation model is proposed.•Energy can be reduced by 7.9% via multi-level energy efficiency evaluation approach.•Fog-cloud based approach can evaluate energy efficiency dynamically and in real time.