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An Internet of Things-enabled model-based approach to improving the energy efficiency of aluminum die casting processes

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  • Liu, Weipeng
  • Peng, Tao
  • Tang, Renzhong
  • Umeda, Yasushi
  • Hu, Luoke

Abstract

The demand for aluminum products is expected to continually increase. Die casting is an important technology for processing aluminum products. It is energy-intensive and its melting and holding sub-processes consume large amounts of energy, but in low energy efficiency. Therefore, improving their energy efficiency can significantly reduce energy costs and environmental impact. Based on an in-depth field survey of die casting factories, two obstacles hindering the melting and holding energy efficiency improvement were identified: 1) the determination of optimal furnace operation parameters in the production planning stage, and 2) the timely adjustment of furnace operation parameters when an incident occurs in the production stage. An Internet of Things-enabled model-based approach, including a parameter optimization model and energy-aware incident control strategy, was proposed to address these two issues. The proposed approach was validated in a die casting factory. Optimizing the furnace melting rate and maximum holding height saved 5%–9% cost, product stock was reduced by approximately 3.6% with the online adjustment of the furnace melt-stoppage time, and holding energy consumption was reduced by approximately 2% with the online control of the furnace standby mode. It was revealed that the practical value of the proposed approach was significant for industrial applications.

Suggested Citation

  • Liu, Weipeng & Peng, Tao & Tang, Renzhong & Umeda, Yasushi & Hu, Luoke, 2020. "An Internet of Things-enabled model-based approach to improving the energy efficiency of aluminum die casting processes," Energy, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:energy:v:202:y:2020:i:c:s0360544220308239
    DOI: 10.1016/j.energy.2020.117716
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    References listed on IDEAS

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    1. Li, Tianxiang & Hassan, Mohamed & Kuwana, Kazunori & Saito, Kozo & King, Paul, 2006. "Performance of secondary aluminum melting: Thermodynamic analysis and plant-site experiments," Energy, Elsevier, vol. 31(12), pages 1769-1779.
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    Citations

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    Cited by:

    1. Liu, Weipeng & Peng, Tao & Kishita, Yusuke & Umeda, Yasushi & Tang, Renzhong & Tang, Wangchujun & Hu, Luoke, 2021. "Critical life cycle inventory for aluminum die casting: A lightweight-vehicle manufacturing enabling technology," Applied Energy, Elsevier, vol. 304(C).
    2. Minh Nguyen Dat & Kien Duong Trung & Phap Vu Minh & Chau Dinh Van & Quynh T. Tran & Trung Nguyen Ngoc, 2023. "Assessment of Energy Efficiency Using an Energy Monitoring System: A Case Study of a Major Energy-Consuming Enterprise in Vietnam," Energies, MDPI, vol. 16(13), pages 1-15, July.
    3. Zheng, Jun & Qi, Tiening & Hu, Xinyu & Pan, Qi & Zhang, Zhiyi & Guan, Aizhi & Ling, Wei & Peng, Tao & Wu, Jian & Wang, Wei, 2024. "Energy consumption assessment and economic analysis of a novel sustainable electro-machining auxiliary system," Applied Energy, Elsevier, vol. 357(C).
    4. Liu, Weipeng & Zhao, Chunhui & Peng, Tao & Zhang, Zhongwei & Wan, Anping, 2023. "Simulation-assisted multi-process integrated optimization for greentelligent aluminum casting," Applied Energy, Elsevier, vol. 336(C).
    5. Cao, Huajun & Chen, Erheng & Yi, Hao & Li, Hongcheng & Zhu, Linquan & Wen, Xuanhao, 2021. "Multi-level energy efficiency evaluation for die casting workshop based on fog-cloud computing," Energy, Elsevier, vol. 226(C).
    6. Ma, Shuaiyin & Zhang, Yingfeng & Lv, Jingxiang & Ge, Yuntian & Yang, Haidong & Li, Lin, 2020. "Big data driven predictive production planning for energy-intensive manufacturing industries," Energy, Elsevier, vol. 211(C).
    7. Zhu, Minglei & Huang, Haiyan & Ma, Weiwen, 2023. "Transformation of natural resource use: Moving towards sustainability through ICT-based improvements in green total factor energy efficiency," Resources Policy, Elsevier, vol. 80(C).

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