Hybrid Artificial Intelligence for Smart Process Control
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 25
Special Issue Editors
Interests: monitoring and control of machines; mechatronic systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancements in artificial intelligence (AI) are transforming smart process control, enabling adaptive, autonomous, and reliable industrial operations. This Special Issue aims to explore the cutting-edge research and practical implementations of hybrid AI systems in smart process control. It seeks contributions that highlight how knowledge-based AI (symbolic reasoning and expert systems), deep learning-based AI (data-driven machine learning), and agent-based AI (autonomous systems) can be effectively combined to address challenges in industrial automation, predictive maintenance, fault diagnosis, and autonomous systems.
We invite researchers, practitioners, and industry experts to contribute original research articles, reviews, and case studies that address the following themes:
- Hybrid AI in Process Control: Exploring integrated frameworks combining symbolic, neural, and agent-based AI for intelligent, robust, and adaptive process control systems.
- Predictive Maintenance using Hybrid AI: Combining sensor data analytics and expert knowledge to forecast equipment failures and optimize maintenance scheduling in industrial systems.
- Edge AI and Cloud System for Real-Time Process Control: Implementing lightweight hybrid AI models on edge devices for fast, on-site decision-making in industrial automation environments.
- AI-Driven Control Loop Optimization: Optimizing feedback and feedforward control loops with hybrid AI for enhanced system responsiveness and operational stability.
- Sustainability and Environmental Impact: Analyzing the contributions of hybrid AI in promoting sustainable practices and reducing the environmental footprint of industrial processes.
This Special Issue seeks to provide a comprehensive overview of the current state of the art in hybrid AI within smart process control, fostering discussions on future trends and challenges. We encourage submissions that not only advance academic knowledge but also offer practical insights and solutions for industry implementation.
Prof. Dr. Ján Piteľ
Prof. Dr. Haruki Ueno
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. 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
- Artificial Intelligence (AI)
- Machine Learning (ML)
- smart process control
- industrial automation
- data-driven process control
- predictive analytics
- deep learning
- real-time data processing
- control systems
- Internet of Things (IoT)
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