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Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes

Author

Listed:
  • Sheng Du

    (School of Automation, China University of Geosciences, Wuhan 430074, China
    Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
    Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China)

  • Li Jin

    (School of Automation, China University of Geosciences, Wuhan 430074, China
    Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
    Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China)

  • Zixin Huang

    (School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China)

  • Xiongbo Wan

    (School of Automation, China University of Geosciences, Wuhan 430074, China
    Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
    Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China)

Abstract

This editorial discusses recent progress in hybrid intelligent modeling technology and optimization strategy for industrial energy consumption processes. With the increasing emphasis on sustainable practices, efficient management of industrial energy consumption has become a critical concern. This editorial aims to explore innovative approaches that use artificial intelligence to model and optimize energy use in industrial processes. The integration of advanced technologies such as machine learning, artificial intelligence, and data analytics play a pivotal role in achieving energy efficiency, reducing environmental impacts and ensuring the sustainability of industrial operations. These studies collectively contribute to the body of knowledge on hybrid intelligent modeling technology and optimization strategy, offering practical solutions and theoretical frameworks to address energy conservation and consumption reduction.

Suggested Citation

  • Sheng Du & Li Jin & Zixin Huang & Xiongbo Wan, 2025. "Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes," Energies, MDPI, vol. 18(8), pages 1-6, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:1939-:d:1632132
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    References listed on IDEAS

    as
    1. Xiangdong Wang & Zerong Huang & Daxing Zhang & Haoyu Yuan & Bingzi Cai & Hanlin Liu & Chunsheng Wang & Yuan Cao & Xinyao Zhou & Yaolin Dong, 2024. "Dynamic Prediction of Proton-Exchange Membrane Fuel Cell Degradation Based on Gated Recurrent Unit and Grey Wolf Optimization," Energies, MDPI, vol. 17(23), pages 1-13, November.
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