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Processes
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Application of Artificial Intelligence in Industrial Process Modelling and Optimization...
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Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition)

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 3495

Special Issue Editors

Prof. Dr. Sheng Du

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: process control; intelligent control; intelligent optimization; computational intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Li Jin

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: power system stability analysis and control; time-delay system; robust theory and application
Special Issues, Collections and Topics in MDPI journals
Dr. Pan Yu

E-Mail Website
Guest Editor
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Interests: robot control; multi-agent cooperative control; high-precision control of electromechanical systems; active disturbance rejection control; advanced robust control; control theory and application
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Liu

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: theory of active defense for information-physical systems; privacy-preserving system state estimation and control; robot intelligent control; telematics security and control

Special Issue Information

Dear Colleagues,

This Special Issue is the second volume of “Application of Artificial Intelligence in Industrial Process Modelling and Optimization” (https://www.mdpi.com/journal/processes/special_issues/0OX7N1I66C).

The integration of artificial intelligence (AI) into industrial process modelling and optimization has proven to be revolutionary. AI can automatically learn the characteristics of industrial processes, improve modelling accuracy, and avoid relying on a large amount of prior knowledge. It can additionally optimize the control strategy of industrial processes and improve their stability and performance and automatically adapt to complex and ever-changing environments. Most importantly, by intelligently analysing industrial process data, AI can enable intelligent monitoring and diagnosis, rapidly detecting and solving problems and thereby improving production efficiency and safety. This Special Issue aims to explore the application of AI approaches in industrial process modelling and optimization. Its focus is on advancing research that harnesses the power of AI to enhance efficiency, safety, and sustainability across various industrial processes.

Scope and Objectives:

This Special Issue primarily aims to foster more research and progress in the application of AI for industrial process modelling and optimization. Its scope encompasses a wide range of industries, including, for example, manufacturing, process engineering, automation, and robotics.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • AI-based modelling techniques;
  • Data-driven modelling techniques;
  • The modelling of complex industrial processes;
  • The integration of AI algorithms;
  • Adaptive control systems;
  • Human–machine collaboration systems;
  • Optimization strategies;
  • Intelligent optimization in industrial processes;
  • Data-driven decision support systems;
  • Applications of AI in cyber–physical systems;
  • AI-based process monitoring and fault diagnosis;
  • AI-driven cyber–physical systems.

Prof. Dr. Sheng Du
Prof. Dr. Li Jin
Dr. Pan Yu
Dr. Hao Liu
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

  • industrial process modeling
  • intelligent optimization
  • artificial intelligence
  • decision support systems
  • cyber–physical systems

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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

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29 pages, 7233 KB  
Article
Exposing Vulnerabilities: Physical Adversarial Attacks on AI-Based Fault Diagnosis Models in Industrial Air-Cooling Systems
by Stavros Bezyrgiannidis, Ioannis Polymeropoulos, Eleni Vrochidou and George A. Papakostas
Processes 2025, 13(9), 2920; https://doi.org/10.3390/pr13092920 - 12 Sep 2025
Viewed by 383
Abstract
Although neural network-based methods have significantly advanced the field of machine fault diagnosis, they remain vulnerable to physical adversarial attacks. This work investigates such attacks in the physical context of a real production line. Attacks simulate failures or irregularities arising from the maintenance [...] Read more.
Although neural network-based methods have significantly advanced the field of machine fault diagnosis, they remain vulnerable to physical adversarial attacks. This work investigates such attacks in the physical context of a real production line. Attacks simulate failures or irregularities arising from the maintenance or production department during the production process, a scenario commonly encountered in industrial environments. The experiments are conducted using data from vibration signals and operational parameters of a motor installed in an industrial air-cooling system used for staple fiber production. In this context, we propose the Mean Confusion Impact Index (MCII), a novel and simple robustness metric that measures the average misclassification confidence of models under adversarial physical attacks. By performing a series of hardware-level interventions, this work aims to demonstrate that even minor physical disturbances can lead to a significant reduction in the model’s diagnostic accuracy. Additionally, a hybrid defense approach is proposed, which leverages deep feature representations extracted from the original classification model and integrates them with lightweight classifiers retrained on adversarial labeled data. Research findings underscore an important limitation in existing industrial artificial intelligence (AI)-based monitoring systems and introduce a practical, scalable framework for improving the physical resilience of machine fault diagnosis in real-world environments. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))
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19 pages, 3880 KB  
Article
Optimal Scheduling of a Multi-Energy Hub with Integrated Demand Response Programs
by Rana H. A. Zubo, Patrick S. Onen, Iqbal M Mujtaba, Geev Mokryani and Raed Abd-Alhameed
Processes 2025, 13(9), 2879; https://doi.org/10.3390/pr13092879 - 9 Sep 2025
Viewed by 362
Abstract
This paper presents an optimal scheduling framework for a multi-energy hub (EH) that integrates electricity, natural gas, wind energy, energy storage systems, and demand response (DR) programs. The EH incorporates key system components including transformers, converters, boilers, combined heat and power (CHP) units, [...] Read more.
This paper presents an optimal scheduling framework for a multi-energy hub (EH) that integrates electricity, natural gas, wind energy, energy storage systems, and demand response (DR) programs. The EH incorporates key system components including transformers, converters, boilers, combined heat and power (CHP) units, and both thermal and electrical energy storage. A novel aspect of this work is the joint coordination of multi-carrier energy flows with DR flexibility, enabling consumers to actively shift or reduce loads in response to pricing signals while leveraging storage and renewable resources. The optimisation problem is formulated as a mixed-integer linear programming (MILP) model and solved using the CPLEX solver in GAMS. To evaluate system performance, five case studies are investigated under varying natural gas price conditions and hub configurations, including scenarios with and without DR and CHP. Results demonstrate that DR participation significantly reduces total operating costs (up to 6%), enhances renewable utilisation, and decreases peak demand (by around 6%), leading to a flatter demand curve and improved system reliability. The findings highlight the potential of integrated EHs with DR as a cost-effective and flexible solution for future low-carbon energy systems. Furthermore, the study provides insights into practical deployment challenges, including storage efficiency, communication infrastructure, and real-time scheduling requirements, paving the way for hardware-in-the-loop and pilot-scale validations. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))
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17 pages, 4994 KB  
Article
Enhancing the Reliability and Durability of Micro-Sensors Using the Taguchi Method
by Chi-Yuan Lee, Jiann-Shing Shieh, Guan-Quan Huang, Chen-Kai Liu, Najsm Cox and Chia-Hao Chou
Processes 2025, 13(9), 2852; https://doi.org/10.3390/pr13092852 - 5 Sep 2025
Viewed by 329
Abstract
This study presents the development and optimization of a flexible integrated three-in-one micro-sensor using Micro-Electro-Mechanical Systems (MEMS) technology. To enhance its reliability and performance, the Taguchi Method was employed to analyze and optimize key fabrication parameters, including the electrode area, electrode thickness, and [...] Read more.
This study presents the development and optimization of a flexible integrated three-in-one micro-sensor using Micro-Electro-Mechanical Systems (MEMS) technology. To enhance its reliability and performance, the Taguchi Method was employed to analyze and optimize key fabrication parameters, including the electrode area, electrode thickness, and protective layer thickness. An L4 orthogonal array design enabled efficient experimentation with minimal runs. Experimental results demonstrate that optimized parameter combinations significantly improve sensor linearity, sensitivity, and reproducibility. Comparative analysis with commercial sensors shows the superior reliability of the self-fabricated sensor, particularly in airflow velocity detection. The findings validate the use of the Taguchi Method for robust MEMS sensor design and highlight its potential for industrial heating, ventilation, and air conditioning (HVAC) applications. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))
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29 pages, 4727 KB  
Article
A Low-Code Visual Framework for Deep Learning-Based Remaining Useful Life Prediction
by Yuhan Lin, Jianhua Chen, Sijuan Chen, Yunfei Nie, Ming Wang, Bing Zhang, Ming Yang and Jipu Wang
Processes 2025, 13(8), 2366; https://doi.org/10.3390/pr13082366 - 25 Jul 2025
Viewed by 489
Abstract
In the context of intelligent manufacturing, deep learning-based remaining useful life (RUL) prediction has become a research hotspot in the field of Prognostics and Health Management (PHM). The traditional approaches often require strong programming skills and repeated model building, posing a high entry [...] Read more.
In the context of intelligent manufacturing, deep learning-based remaining useful life (RUL) prediction has become a research hotspot in the field of Prognostics and Health Management (PHM). The traditional approaches often require strong programming skills and repeated model building, posing a high entry barrier. To address this, in this study, we propose and implement a visualization tool that supports multiple model selections and result visualization and eliminates the need for complex coding and mathematical derivations, helping users to efficiently conduct RUL prediction with lower technical requirements. This study introduces and summarizes various novel neural network models for DL-based RUL prediction. The models are validated using the NASA and HNEI datasets, and among the validated models, the LSTM model best met the requirements for remaining useful life (RUL) prediction. In order to achieve the low-code usage of deep learning for RUL prediction, the following tasks were performed: (1) multiple models were developed using the Python (3.9.18) language and were implemented on the PyTorch (1.12.1) framework, providing users with the freedom to choose their desired model; (2) a user-friendly and low-code RUL prediction interface was built using Streamlit, enabling users to easily make predictions; (3) the visualization of prediction results was implemented using Matplotlib (3.8.2), allowing users to better understand and analyze the results. In addition, the tool offers functionalities such as automatic hyperparameter tuning to optimize the performance of the prediction model and reduce the complexity of operations. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))
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Review

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46 pages, 10548 KB  
Review
A Review of Hybrid LSTM Models in Smart Cities
by Bum-Jun Kim and Il-Woo Nam
Processes 2025, 13(7), 2298; https://doi.org/10.3390/pr13072298 - 18 Jul 2025
Cited by 1 | Viewed by 1530
Abstract
Rapid global urbanization poses complex challenges that demand advanced data-driven forecasting solutions for smart cities. Traditional statistical and standalone Long Short-Term Memory (LSTM) models often struggle to capture non-linear dynamics and long-term dependencies in urban time-series data. This review critically examines hybrid LSTM [...] Read more.
Rapid global urbanization poses complex challenges that demand advanced data-driven forecasting solutions for smart cities. Traditional statistical and standalone Long Short-Term Memory (LSTM) models often struggle to capture non-linear dynamics and long-term dependencies in urban time-series data. This review critically examines hybrid LSTM models that integrate LSTM with complementary algorithms, including CNN, GRU, ARIMA, and SVM. These hybrid architectures aim to enhance prediction accuracy, integrate diverse data sources, and improve computational efficiency. This study systematically reviews principles, trends, and real-world applications, quantitatively evaluating hybrid LSTM models using performance metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2), while identifying key study limitations. The case studies considered include traffic management, environmental monitoring, energy forecasting, public health, infrastructure assessment, and urban waste management. For example, hybrid models have achieved substantial accuracy improvements in traffic congestion forecasting, reducing their mean absolute error by up to 29%. Despite the inherent challenges related to structural complexity, interpretability, and data requirements, ongoing research on attention mechanisms, model compression, and explainable AI has significantly mitigated these limitations. Thus, hybrid LSTM models have emerged as vital analytical tools capable of robust spatiotemporal prediction, effectively supporting sustainable urban development and data-driven decision-making in evolving smart city environments. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))
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