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Processes
Special Issues
Artificial Intelligence and Smart Systems in Process Engineering Applications
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Artificial Intelligence and Smart Systems in Process Engineering Applications

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 1180

Special Issue Editors

Prof. Dr. Hasan Amca
*
E-Mail Website
Guest Editor
Faculty of Engineering, Eastern Mediterranean University, Mersin, Turkey
Interests: smart mobile/cellular communication systems; machine learning; artificial intelligence applications such as in industry and businesses; millimeter-wave communications; optical communications; oFdM; internationalization of higher education
* Affiliated with EMU but retired
Prof. Dr. Huseyin Ademgil

E-Mail Website
Guest Editor
Electrical Engineering Department, Europeam University of Lefke, Lefke, Turkey
Interests: photonics; crystal fiber-based biosensors; machine learning methods; generative adversarial neural networks; photonic crystal fiber-based surface plasmon resonance sensors

Special Issue Information

Dear Colleagues,

This Special Issue compiles the valuable research results of scientists who have contributed to the 5th International Conference on Artificial Intelligence, Smart Technologies and Engineering Applications (INVENT-2025), 25–27 April 2025, Bologna, Italy (https://inventconference.com/). The Special Issue presented here covers the latest developments in Artificial Intelligence, smart/intelligent systems, and engineering applications in terms of both technological and social/human challenges and will provide a broad overview of their progress and future prospects. Since research in the field is constantly evolving, it aims to provide an excellent integration of research and collective results to solve many of today's problems and share future developments and results.

This Special Issue brings together the results of research on Artificial Intelligence, Machine Learning, Pattern Recognition, Smart Energy Systems, Automation and Optimization, Robotics, IoT, Sensors, Smart Telecommunication Networks and Systems, Biomedical Systems and Engineering Innovations, Data Science and Analytics, Cybersecurity, Smart Cities, Human–Computer Interactions, User Experience Design, Blockchain Applications in Industry, Sustainable Production with Smart Technologies, VR, AR, ER, Data Processing with Sensors, Evolutionary Computations (Genetic Algorithms, Evolution Strategies, Particle Swarm Optimization, etc.), Intelligent Processing of Electromagnetic and Acoustic Waves, and GAI in Education, Law, and similar topics. A platform is provided here where the latest developments, research results, projects, and industrial experiences in the fields can be shared by researchers working in both academia and industry. Authors are invited to contribute to this Special Issue by submitting articles showcasing their work in the relevant fields.

Prof. Dr. Hasan Amca
Prof. Dr. Huseyin Ademgil
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
  • smart technologies
  • machine learning
  • engineering applications
  • intelligent telecommunication systems
  • smart energy systems
  • signal processing and detection
  • data science and analytics
  • robotics and automation
  • cybersecurity

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

Published Papers (2 papers)

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22 pages, 3190 KiB  
Article
A Hybrid Fault Early-Warning Method Based on Improved Bees Algorithm-Optimized Categorical Boosting and Kernel Density Estimation
by Kuirong Liu, Guanlin Wang, Dajun Mao and Junqing Huang
Processes 2025, 13(5), 1460; https://doi.org/10.3390/pr13051460 - 10 May 2025
Viewed by 274
Abstract
In the context of intelligent manufacturing, equipment fault early-warning technology has become a critical support for ensuring the continuity and safety of industrial production. However, with the increasing complexity of modern industrial equipment structures and the growing coupling of operational states, traditional fault [...] Read more.
In the context of intelligent manufacturing, equipment fault early-warning technology has become a critical support for ensuring the continuity and safety of industrial production. However, with the increasing complexity of modern industrial equipment structures and the growing coupling of operational states, traditional fault warning models face significant challenges in feature recognition accuracy and adaptability. To address these issues, this study proposes a hybrid fault early-warning framework that integrates an improved bees algorithm (IBA) with a categorical boosting (CatBoost) model and kernel density estimation (KDE). The proposed framework first develops the IBA by integrating Latin Hypercube Sampling, a multi-perturbation neighborhood search strategy, and a dynamic scout bee adjustment strategy, which effectively overcomes the conventional bees algorithm (BA)’s tendency to fall into local optima. The IBA is then employed to achieve global optimization of CatBoost’s key hyperparameters. The optimized CatBoost model is subsequently used to predict equipment operational data. Finally, the KDE method is applied to the prediction residuals to determine fault thresholds. An empirical study on a deflection fault in the valve position sensor connecting rod of the mineral oil system in a gas compressor station shows that the proposed method can issue early-warning signals two hours in advance and outperforms existing advanced algorithms in key indicators such as root mean square error (RMSE), coefficient of determination (R2) and mean absolute percentage error (MAPE). Furthermore, ablation experiments verify the effectiveness of the strategies in IBA and their contribution to CatBoost hyperparameter optimization. The proposed method significantly improves the accuracy and reliability of fault prediction in complex industrial environments. Full article
(This article belongs to the Special Issue Artificial Intelligence and Smart Systems in Process Engineering Applications)
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20 pages, 5665 KiB  
Article
Applied Internet of Things to Analyze Vibration, Workpiece Roughness, and Tool Wear: Case Study of Successive Milling
by Chin-Shan Chen and Pin-Yu Pan
Processes 2025, 13(4), 978; https://doi.org/10.3390/pr13040978 - 25 Mar 2025
Viewed by 609
Abstract
Along with technology development and market change, automated production should be made easier and more intelligent to promote production efficiency and product quality as well as reduce labor and production costs. The introduction of the Internet of Things (IoT) is an important issue [...] Read more.
Along with technology development and market change, automated production should be made easier and more intelligent to promote production efficiency and product quality as well as reduce labor and production costs. The introduction of the Internet of Things (IoT) is an important issue in automated processing. This study aims to apply the Industrial Internet of Things (IIoT) to automated processing systems for real-time monitoring of the condition of production lines and analyze the causal relationship between vibration, surface roughness, tool wear, and take successive milling of medium carbon steel workpieces as a case study. First, automated processing hardware equipment is set up, and software and hardware are required for installing IIoT; then, the IoT App is designed. Second, successive automated processing experiments are preceded. The Taguchi method is utilized in the processing process to find optimized cutting parameters to be the parameter setting values for successive cutting. Three accelerometers are used to detect vibration changes in the cutting process; meanwhile, IIoT is introduced to monitor the condition of the production line. Finally, Using big data analytics acquired in the experiments to verify the processing quality under optimized cutting parameters could make a 4.516% improvement and obtain the vibration value for the best tool change during successive processing as well as to realize the obtainment of current processing information through IIoT. The system would deliver tool change or processing abnormality alerts to users for real-time condition exclusion. To achieve the goal of remote monitoring and intelligent automatic processing. Full article
(This article belongs to the Special Issue Artificial Intelligence and Smart Systems in Process Engineering Applications)
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