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

Dear Colleagues,

In the era of Industry 4.0 and the ever-growing emphasis on sustainable practices, the efficient management of industrial energy consumption has become a critical concern. This Special Issue aims to explore 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.

The main objective of this Special Issue is to promote research and innovation in the field of hybrid intelligent modeling and optimization for industrial energy consumption processes, especially in the fields of steel metallurgy, chemical engineering, geological drilling, marine exploration, textile, pharmaceutical, and other large-scale industries.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

1. Hybrid Intelligent Modeling Techniques:
   Exploration of advanced machine learning algorithms for modeling energy consumption patterns.
   Integration of sensor data and IoT technologies for real-time data collection and analysis.
   Development of predictive models for forecasting energy demand and consumption trends.

2. Intelligent Optimization Strategies:
   Application of optimization algorithms to enhance energy efficiency in industrial processes.
   Utilization of decision support systems for intelligent and adaptive energy management.
   Integration of intelligent control systems for the dynamic optimization of energy consumption.

3. Case Studies and Applications:
   Real-world case studies demonstrating the successful implementation of data-driven intelligent models in industrial set-tings.
   Application of intelligent optimization strategies in diverse industrial sectors to showcase versatility and effectiveness.
   Assessment of economic, environmental, and operational benefits achieved through optimized energy consumption.

4. Interdisciplinary Approaches:
   Cross-disciplinary studies that explore the synergy between data-driven intelligence and renewable energy sources.

Prof. Dr. Sheng Du
Prof. Dr. Xiongbo Wan
Prof. Dr. Li Jin
Dr. Zixin Huang
Guest Editors

Manuscript Submission Information

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