Data-Driven Modeling, Control and Optimization of Complex Industrial Processes
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".
Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 34137
Special Issue Editors
Interests: artificial intelligent system; signal processing; fuzzy control
Special Issues, Collections and Topics in MDPI journals
Interests: modeling, control and optimization of complex industrial processes; data mining, machine learning, pattern recognition, big data analytics and the application of artificial intelligence in different fields, including intelligent manufacturing, intelligent energy, intelligent civil aviation, etc.
Special Issue Information
Dear Colleagues,
The last decade has seen a radical step-change in the scale and complexity of engineering systems in industrial processes such as manufacturing technologies, equipment used, and production processes in the petrochemical industry, iron and steel metallurgy, the light industry, and other industries. Complexity arises from a number of factors, such as strong nonlinearities, multi-variable coupling, and variations in operation conditions, together with unknown model structures and parameters. So, it is hard to establish mathematical models using the first principle techniques and to further control and optimize them using traditional theory. Moreover, the rapid development and application of information and communication technologies make it possible to collect massive data for industrial processes. Such data contain comprehensive knowledge and information about the operation and control of industrial processes. The question of how to deal with complex industrial systems using industrial data has attracted an increasing amount of interest. As the core technologies, the development of new modeling, control, and optimization techniques for large-scale and complex industrial process based on industrial data has become a multidiscipline theme that brings together the modern control theory, computer modeling, intelligent optimization, powerful data real-time processing, and networking technology.
The main focus of this Special Issue is new theories and their applications in data-based modeling, control, and optimization for complex industrial processes, especially in industry applications. Topics include, but not are limited to:
- Advanced data-driven simulation and modeling methods for complex industrial systems and processes;
- Data-driven control theory, approaches, and applications;
- Data-driven fault diagnosis, health maintenance, and performance evaluation;
- Data-driven modeling, optimization, scheduling, decision making, and simulation;
- Intelligent transport systems and electric vehicles;
- Statistical learning, machine learning, data mining, and practical applications in the automation field.
Prof. Dr. Rey-Chue Hwang
Prof. Dr. Huixin Tian
Guest Editors
Manuscript Submission Information
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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 and simulation
- control theory and application
- industrial optimization
- fault diagnosis
- complex industrial processes
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