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

Sheng Du, Li Jin (), Zixin Huang and Xiongbo Wan
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Sheng Du: School of Automation, China University of Geosciences, Wuhan 430074, China
Li Jin: School of Automation, China University of Geosciences, 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

Energies, 2025, vol. 18, issue 8, 1-6

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.

Keywords: hybrid intelligent modeling; industrial processes; optimization strategy; artificial intelligence; energy conservation and consumption reduction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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