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Processes
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Hybrid Artificial Intelligence for Smart Process Control
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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 1674

Special Issue Editors

Prof. Dr. Ján Piteľ

E-Mail Website
Guest Editor
Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies with a Seat in Presov, Technical University of Kosice, 080 01 Presov, Slovakia
Interests: monitoring and control of machines; mechatronic systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haruki Ueno

E-Mail Website
Guest Editor
Principles of Informatics Research Division, National Institute of Informatics, Tokyo 101-8430, Japan
Interests: artificial intelligence; robotics; R&D strategy

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

18 pages, 1809 KB  
Article
Transformer Fault Diagnosis Method Based on Improved Particle Swarm Optimization and XGBoost in Power System
by Yuanhao Zheng, Chaoping Rao, Fei Wang and Hongbo Zou
Processes 2025, 13(10), 3321; https://doi.org/10.3390/pr13103321 - 16 Oct 2025
Abstract
Fault prediction and diagnosis are critical for enhancing the maintenance and reliability of power system equipment, reducing operational costs, and preventing potential failures. In power transformers, periodic oil sampling and gas ratio analysis provide valuable insights for predictive maintenance and life-cycle assessment. Machine [...] Read more.
Fault prediction and diagnosis are critical for enhancing the maintenance and reliability of power system equipment, reducing operational costs, and preventing potential failures. In power transformers, periodic oil sampling and gas ratio analysis provide valuable insights for predictive maintenance and life-cycle assessment. Machine learning methods, such as XGBoost, have proven to deliver more accurate results, especially when historical data is limited. However, the performance of XGBoost is highly dependent on the optimization of its hyperparameters. To address this, this paper proposes an improved Particle Swarm Optimization (IPSO) method to optimize the hyperparameters of XGBoost for transformer fault diagnosis. The PSO algorithm is enhanced by introducing topology optimization, adaptively adjusting the acceleration factor, dividing the swarm into master–slave particle groups to strengthen search capability, and dynamically adjusting inertia weights using a linear adaptive strategy. IPSO is applied to optimize key hyperparameters of the XGBoost model, improving both its diagnostic accuracy and generalization ability. Experimental results confirm the effectiveness of the proposed model in enhancing fault prediction and diagnosis in power systems. Full article
(This article belongs to the Special Issue Hybrid Artificial Intelligence for Smart Process Control)
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24 pages, 1804 KB  
Article
Proactive Defense Approach for Cyber–Physical Fusion-Based Power Distribution Systems in the Context of Attacks Targeting Link Information Systems Within Smart Substations
by Yuan Wang, Xingang He, Zhi Cheng, Bowen Wang, Jing Che and Hongbo Zou
Processes 2025, 13(10), 3269; https://doi.org/10.3390/pr13103269 - 14 Oct 2025
Viewed by 67
Abstract
The cyber–physical integrated power distribution system is poised to become the predominant trend in the development of future power systems. Although the highly intelligent panoramic link information system in substations facilitates the efficient, cost-effective, and secure operation of the power system, it is [...] Read more.
The cyber–physical integrated power distribution system is poised to become the predominant trend in the development of future power systems. Although the highly intelligent panoramic link information system in substations facilitates the efficient, cost-effective, and secure operation of the power system, it is also exposed to dual threats from both internal and external factors. Under intentional cyber information attacks, the operational data and equipment response capabilities of the panoramic link information system within smart substations can be illicitly manipulated, thereby disrupting dispatcher response decision-making and resulting in substantial losses. To tackle this challenge, this paper delves into the research on automatic verification and active defense mechanisms for the cyber–physical power distribution system under panoramic link attacks in smart substations. Initially, to mitigate internal risks stemming from the uncertainty of new energy output information, this paper utilizes a CGAN-IK-means model to generate representative scenarios. For scenarios involving external intentional cyber information attacks, this paper devises a fixed–flexible adjustment resource response strategy, making up for the shortfall in equipment response capabilities under information attacks through flexibility resource regulation. The proposed strategy is assessed based on two metrics, voltage level and load shedding volume, and computational efficiency is optimized through an enhanced firefly algorithm. Ultimately, the efficacy and viability of the proposed method are verified and demonstrated using a modified IEEE standard test system. Full article
(This article belongs to the Special Issue Hybrid Artificial Intelligence for Smart Process Control)
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19 pages, 3724 KB  
Article
Mechanical Fault Diagnosis of High-Voltage Disconnectors via Multi-Domain Energy Features of Vibration Signals in Power Systems
by Shijian Zhu, Peilong Chen, Xin Li, Qichen Deng and Feiyue Yan
Processes 2025, 13(10), 3254; https://doi.org/10.3390/pr13103254 - 13 Oct 2025
Viewed by 119
Abstract
To accurately diagnose the potential faults such as jamming and incomplete opening and closing of high-voltage disconnectors during long-term operation, this paper proposes a fault diagnosis method based on the fusion of time-frequency domain energy features of the body-side vibration signal. This method [...] Read more.
To accurately diagnose the potential faults such as jamming and incomplete opening and closing of high-voltage disconnectors during long-term operation, this paper proposes a fault diagnosis method based on the fusion of time-frequency domain energy features of the body-side vibration signal. This method extracts short-term energy in the time domain and the marginal spectral energy of the sub-signals processed by variational mode decomposition (VMD) as features in the frequency domain, and constructs a feature set that can effectively represent different states through feature fusion. This enables the distinction between six states, namely normal closing, normal opening, closing jam, opening jam, closing not in place, and opening not in place. On this basis, the particle swarm optimization (PSO) algorithm is adopted to optimize the hyperparameters of the support vector machine (SVM), and the fault diagnosis model is obtained. The fault simulation experiment was conducted on the ZF12B type disconnector, and the experimental results show that the recognition accuracy of the proposed method reaches 98.33%, which is superior to the compared method, verifying the effectiveness and superiority of the proposed method. Full article
(This article belongs to the Special Issue Hybrid Artificial Intelligence for Smart Process Control)
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18 pages, 2649 KB  
Article
Bi-Level Optimization Method for Frequency Regulation Performance of Industrial Extraction Heating Units Under Deep Peak Shaving Conditions
by Libin Wen, Hong Hu, Jinji Xi and Li Xiong
Processes 2025, 13(10), 3111; https://doi.org/10.3390/pr13103111 - 28 Sep 2025
Viewed by 266
Abstract
This paper proposes a multi-objective collaborative optimization method based on a two-layer optimization framework to address the problem of difficult coordinated optimization of multi-parameter coupling in the frequency regulation performance of heating units under deep peak shaving conditions. The upper-level optimization of this [...] Read more.
This paper proposes a multi-objective collaborative optimization method based on a two-layer optimization framework to address the problem of difficult coordinated optimization of multi-parameter coupling in the frequency regulation performance of heating units under deep peak shaving conditions. The upper-level optimization of this method focuses on the dynamic performance of primary frequency modulation and improves the fast response capability through multi-objective optimization of overshoot and adjustment time. Lower-level optimization is based on the optimal control parameter set output by the upper level, with comprehensive power deviation as the indicator, focusing on suppressing the deviation of frequency modulation power and the steady-state deviation of heating power. Propose a comprehensive quantitative index for frequency modulation performance and characterize the optimization effect of frequency modulation performance. Introducing a dynamic perturbation factor mechanism to generate an improved HO algorithm for dual-layer optimization solutions, preventing it from getting stuck in local optima and solving the problem of global search capability imbalance. The effectiveness of the method was verified based on actual unit calculations, and the obtained control parameter set met the objectives of optimal primary frequency regulation dynamic performance and optimal comprehensive power deviation performance, significantly improving the frequency regulation performance of heating units under deep peak shaving. After optimization, the overshoot performance score of the unit increased by 16.9%, the regulation time performance score increased by 25.1%, the frequency modulation power deviation score increased by 14.2%, the heating power deviation score increased by 17.7%, and the total frequency modulation performance score increased from 75.26 to 95.95, with a comprehensive optimization range of 27.5%. Full article
(This article belongs to the Special Issue Hybrid Artificial Intelligence for Smart Process Control)
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18 pages, 10978 KB  
Article
A Lightweight Infrared and Visible Light Multimodal Fusion Method for Object Detection in Power Inspection
by Linghao Zhang, Junwei Kuang, Yufei Teng, Siyu Xiang, Lin Li and Yingjie Zhou
Processes 2025, 13(9), 2720; https://doi.org/10.3390/pr13092720 - 26 Aug 2025
Viewed by 844
Abstract
Visible and infrared thermal imaging are crucial techniques for detecting structural and temperature anomalies in electrical power system equipment. To meet the demand for multimodal infrared/visible light monitoring of target devices, this paper introduces CBAM-YOLOv4, an improved lightweight object detection model, which leverages [...] Read more.
Visible and infrared thermal imaging are crucial techniques for detecting structural and temperature anomalies in electrical power system equipment. To meet the demand for multimodal infrared/visible light monitoring of target devices, this paper introduces CBAM-YOLOv4, an improved lightweight object detection model, which leverages a novel synergistic integration of the Convolutional Block Attention Module (CBAM) with YOLOv4. The model employs MobileNet-v3 as the backbone to reduce parameter count, applies depthwise separable convolution to decrease computational complexity, and incorporates the CBAM module to enhance the extraction of critical optical features under complex backgrounds. Furthermore, a feature-level fusion strategy is adopted to integrate visible and infrared image information effectively. Validation on public datasets demonstrates that the proposed model achieves an 18.05 frames per second increase in detection speed over the baseline, a 1.61% improvement in mean average precision (mAP), and a 2 MB reduction in model size, substantially improving both detection accuracy and efficiency through this optimized integration in anomaly inspection of electrical equipment. Validation on a representative edge device, the NVIDIA Jetson Nano, confirms the model’s practical applicability. After INT8 quantization, the model achieves a real-time inference speed of 40.8 FPS with a high mAP of 80.91%, while consuming only 5.2 W of power. Compared to the standard YOLOv4, our model demonstrates a significant improvement in both processing efficiency and detection accuracy, offering a uniquely balanced and deployable solution for mobile inspection platforms. Full article
(This article belongs to the Special Issue Hybrid Artificial Intelligence for Smart Process Control)
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