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

Format:

Book

Author/Creator:

Dusheng., Editor.

Contributor:

Wan, Xiongbo, Editor.

Jin, Li, Editor.

Huang, Zishen., Editor.

Dusheng.

Wan, Xiongbo

Jin, Li

Huang, Zixin

Language:

English

Physical Description:

1 online resource

Place of Publication:

MDPI - Multidisciplinary Digital Publishing Institute 2025

Language Note:

English

Summary:

This is to explore the multifaceted aspects of 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. It explores innovative approaches that leverage data-driven intelligence to model and optimize energy use in industrial processes. The integration of advanced technologies such as machine learning, artificial intelligence and data analytics will play a pivotal role in achieving energy efficiency, reducing environmental impacts and ensuring the sustainability of industrial operations. Research areas include hybrid intelligent modeling techniques, intelligent optimization strategies, case studies and applications, and interdisciplinary approaches. 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. By sharing their practice and insights in the development and application of these new technologies, the authors of the articles in this reprint have demonstrated the value of hybrid intelligent modeling technology and optimization strategy for industrial energy consumption processes, providing readers with valuable ideological inspiration in the field.