Modeling, Optimization and Control of Industrial Processes
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "C2: Dynamical Systems".
Deadline for manuscript submissions: 15 January 2026 | Viewed by 13893
Special Issue Editor
Interests: process control, system modelling, system identification; fuzzy system; industrial automation; distributed control systems; networked control systems
Special Issue Information
Dear Colleagues,
With the remarkable advances in mathematics and information technologies, smart manufacturing has become a central strategy in the transformation of industrial production. The goals of smart manufacturing are to enable production with higher quality, lower costs, lower environmental impact, and carbon neutrality. In this development process, some technologies such as digital twin, industrial internet of things, cyber-physical systems, big data, 5G, etc., will become the main technical framework in the process industry. Process industries refer to basic raw materials industries, including oil, gas, steel, building materials, petrochemicals, chemical and plastic production, food and drink processing, water and wastewater treatment plants and energy production.
This Special Issue will focus on the latest research papers on how to develop and implement new modeling, optimization, and control methods in the process industry to take advantage of the new technologies mentioned above.
Topics include, but are not limited to, the following: building mathematical models of complex dynamic systems and related simulation methods; developing theory with the formulation of new control systems; developing theory and control systems with artificial intelligence using neural networks, fuzzy systems, and machine learning; simulating control systems for dynamic objects on computers and in real time on test beds; and developing digital twins. Contributions of both theoretical and practical research are welcome.
Dr. Marjan Golob
Guest Editor
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Keywords
- process control
- biotechnology
- mathematical modeling and identification
- numerical analysis and simulation
- data-driven modeling and control
- fuzzy systems and control
- machine learning
- digital twins
- process industry
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