Industrial Process Operation State Sensing and Performance Optimization
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".
Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 23912
Special Issue Editors
Interests: process control; intelligent control; intelligent optimization; computational intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: power system stability analysis and control; time-delay system; ro-bust theory and appli-cation
Special Issues, Collections and Topics in MDPI journals
2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
3. Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
Interests: artificial intelligence; robust control of time-delay systems
Special Issues, Collections and Topics in MDPI journals
Interests: underdrive system control; intelligent control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid development of large-scale industries, the operational safety, energy consumption, and efficient management of industrial processes have received widespread attention. This Special Issue aims to explore industrial process operation state sensing and performance optimization. The integration of advanced technologies, such as machine learning, artificial intelligence, and data analytics, will provide important support for soft sensing, process monitoring, fault diagnosis, energy consumption optimization, and performance improvement.
Scope and Objectives:
The primary objective of this Special Issue is to promote research and advancement in the field of operation state sensing and performance optimization for industrial processes, especially in the fields of steel metallurgy, chemical engineering, geological drilling, marine exploration, textiles, pharmaceuticals, 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:
- Soft sensing techniques.
- Hybrid intelligent modeling techniques.
- Data-driven modeling techniques.
- Operation state sensing.
- Process monitoring.
- Fault diagnosis.
- Energy consumption optimization.
- Performance improvement.
- Performance assessment.
Prof. Dr. Sheng Du
Prof. Dr. Li Jin
Prof. Dr. Xiongbo Wan
Guest Editors
Dr. Zixin Huang
Guest Editor Assistant
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. Processes is an international peer-reviewed open access monthly 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 2400 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.
Keywords
- data-driven modeling
- industrial processes
- machine learning
- operation state sensing
- performance improvement
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