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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data-driven modeling
• industrial energy consumption processes
• machine learning
• optimization
• hybrid intelligent