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Processes
Special Issues
Process Control and Optimization in the Era of Industry 5.0
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Process Control and Optimization in the Era of Industry 5.0

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

Deadline for manuscript submissions: 25 September 2026 | Viewed by 3921

Special Issue Editors

Prof. Dr. Aimin An

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730300, China
Interests: artificial intelligence control technology; industrial process advanced control and optimization control technology
Prof. Dr. Shuyou Yu

E-Mail Website
Guest Editor
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Interests: reinforcement learning; model predictive control; human–machine augmentation; human–machine cooperative game theory
Special Issues, Collections and Topics in MDPI journals
Dr. Langwen Zhang

E-Mail Website
Guest Editor
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
Interests: model predictive control; process control; optimization; learning based model predictive control
Prof. Dr. Zhonggai Zhao

E-Mail Website
Guest Editor
IoT School, Jiangnan University, Wuxi City 214122, China
Interests: process monitoring; product design; data-driven model

Special Issue Information

Dear Colleagues,

We invite you to submit papers to this Special Issue of Processes on “Process Control and Optimization in the Era of Industry 5.0”. Ensuring production stability, reducing production costs, and energy consumption of industrial processes have become increasingly critical as industries adopt more automated, digital, and complex processes in the era of Industry 5.0. The integration of advanced control and optimization technologies offers new opportunities to enhance the production stability of industrial operations while improving efficiency and resource utilization and reducing production costs and energy consumption.

The rapid progress in fields such as artificial intelligence (AI), machine learning, and the Industrial Internet of Things (IIoT) is transforming industrial process modeling and control systems. These advancements allow for more intelligent control and predictive control techniques, enabling accurately identifying the dynamics of the process, optimizing control strategies, and ensuring production stability. With the ability to deal with vast operational data, modern industrial systems can be more robust and adaptable, leading to improved operational stability and reduced production costs and energy consumption.

Given these developments, this Special Issue aims to highlight the latest trends, research, applications, and challenges in advanced process control and optimization for industrial processes in the era of Industry 5.0. We invite both theoretical and application-oriented contributions, as well as review articles that explore innovations in these areas. All submissions will undergo peer review and be evaluated for their originality, relevance, and contribution to the field.

Potential topics of interest include, but are not limited to, the following:

  • Advanced modeling and control methods for the process;
  • Application of AI, machine learning, and data analytics in process modeling;
  • IIoT-based data-driven modeling for processes;
  • Collaborative and distributed control in industrial processes;
  • Parameter estimation and system identification based on machine learning;
  • Performance evaluation for industrial control systems;
  • Nonlinear and coupled process control using model predictive control techniques;
  • Digital twin applications for process simulation;
  • Industrial intelligent control and intelligent optimization.

Prof. Dr. Aimin An
Prof. Dr. Shuyou Yu
Dr. Langwen Zhang
Prof. Dr. Zhonggai Zhao
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 250 words) can be sent to the Editorial Office for assessment.

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 semimonthly 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

  • process data analysis and modeling
  • intelligent information perception and data processing
  • data-driven modeling
  • parameter estimation and system identification
  • system simulation and dynamic simulation
  • digital twin of industrial process equipment
  • machine learning algorithms and applications
  • industrial intelligent control and intelligent optimization
  • data-driven model-based predictive control
  • advanced control theory and methods
  • data drive and control in the process industry
  • servo-drive control
  • engineering equipment modeling and intelligent control
  • smart factories in the era of Industry 5.0
  • optimization and production scheduling
  • quality prediction of industrial process products

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

Published Papers (4 papers)

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Research

21 pages, 2105 KB  
Article
Sustainable Design of Phosphonate Anti-Scale Additives for Oilfield Flow Assurance via 2D-QSAR-KNN and Global Inverse-QSAR Descriptor Profiling
by Ouafa Belkacem, Lokmane Abdelouahed, Kamel Aizi, Maamar Laidi, Abdelhafid Touil and Salah Hanini
Processes 2026, 14(6), 906; https://doi.org/10.3390/pr14060906 - 12 Mar 2026
Viewed by 508
Abstract
Mineral scale deposition remains a major flow-assurance constraint in oil and gas operations, especially in water-flooding and produced-water reinjection, where mixing between incompatible brines promotes super-saturation and precipitation of poorly soluble salts. This work introduces a novel extension of traditional methods used for [...] Read more.
Mineral scale deposition remains a major flow-assurance constraint in oil and gas operations, especially in water-flooding and produced-water reinjection, where mixing between incompatible brines promotes super-saturation and precipitation of poorly soluble salts. This work introduces a novel extension of traditional methods used for modeling chemical inhibition and the predictive evaluation of oilfield scale-inhibitor molecules. A systematically optimized Two-Dimensional Quantitative Structure–Activity Relationship Model based on the k-Nearest Neighbors algorithm 2D-QSAR-KNN model was developed to quantitatively link molecular constitution of phosphonate inhibitors, brine chemistry, and operating factors with inhibition efficiency IE %. The optimized model achieved strong accuracy and generalization R2train = 0.9182, R2test = 0.9306, and R2global = 0.9208 with low prediction errors RMSEtrain = 4.7888%, RMSEtest = 4.5485%, and RMSEglobal = 4.7421%. Median absolute errors remained minimal for the train set = 0.80%, and test set = 1.63%, and model stability was confirmed by high correlation with experimental IE % r = 0.94 and R2train/R2test ≈ 0.99, showing no sign of overfitting. Additionally, an inverse-2D-QSAR framework was applied to identify the optimal molecular descriptor profile expected to maximize inhibitory performance within normalized bounds, providing rational rules for next-generation inhibitor design. The findings highlight the practical value of QSAR-inspired AI modeling to accelerate molecule screening and dosage exploration prior to laboratory validation, supporting more cost-effective, interpretable, and environmentally aware sulfate-scale inhibition strategies under high-salinity reservoir conditions. Full article
(This article belongs to the Special Issue Process Control and Optimization in the Era of Industry 5.0)
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30 pages, 1581 KB  
Article
A Human–AI Collaborative Framework for Additive Manufacturing Modeling and Decision-Making
by Alexios Papacharalampopoulos, Panagis Foteinopoulos, Olga Maria Karagianni and Panagiotis Stavropoulos
Processes 2025, 13(12), 3877; https://doi.org/10.3390/pr13123877 - 1 Dec 2025
Cited by 2 | Viewed by 1380
Abstract
Even though Additive Manufacturing (AM) has become a critical enabler of manufacturing in various industries, its full potential in terms of process quality and productivity has not been achieved yet. The recent developments in Artificial Intelligence (AI) can help toward this goal, especially [...] Read more.
Even though Additive Manufacturing (AM) has become a critical enabler of manufacturing in various industries, its full potential in terms of process quality and productivity has not been achieved yet. The recent developments in Artificial Intelligence (AI) can help toward this goal, especially through Human–AI Collaboration (HAIC). However, existing approaches are focused on certain aspects of the problem, without comprehensively tackling the issue. This study proposes a holistic and AM-specific HAIC framework that combines the different components of human expertise, explainable AI, simulation-based forecasting, and variable-based process control into an integrated decision-making structure. The key findings include the identification of the most important variables that should be utilized, including their classification through the input of experts in terms of importance (utilizing the presented M-S metric), controllability, and the most suitable agent (human, AI, both) to effectively control each variable. Finally, the concept of the framework for effective HAIC in AM is analyzed, including the operational sequence of sensing, AI analysis, human evaluation, decision implementation, and feedback loops. Two complementary case studies are presented; the first provides a conceptual example, and the second one develops a quantitative scenario that allows the comparison of three decision pathways—AI-only, Human-only, and HAIC. Full article
(This article belongs to the Special Issue Process Control and Optimization in the Era of Industry 5.0)
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24 pages, 1479 KB  
Article
Designs of Bayesian EWMA Variability Control Charts in the Presence of Measurement Error
by Ming-Che Lu and Su-Fen Yang
Processes 2025, 13(10), 3371; https://doi.org/10.3390/pr13103371 - 21 Oct 2025
Viewed by 682
Abstract
Statistical process control may lead to false detection results in the presence of measurement error, so it is necessary to deal with the effect of measurement error. The Bayesian exponentially weighted moving average (EWMA) variability control chart, first proposed by Lin et al., [...] Read more.
Statistical process control may lead to false detection results in the presence of measurement error, so it is necessary to deal with the effect of measurement error. The Bayesian exponentially weighted moving average (EWMA) variability control chart, first proposed by Lin et al., is a distribution-free control chart, and it can effectively monitor process variance even if the process skewness varies with time. This paper investigates the influence of measurement error on the Bayesian EWMA variability control chart, and it proposes two designs for the Bayesian EWMA variability control chart in the presence of measurement error. One is to modify the control limits based on the biased error-prone monitoring statistics, called the error-embedded control chart. The other is to design the control limits based on the error-corrected monitoring statistics, called the error-corrected control chart. Simulation results prove that both of the proposed control charts are reliable and have good detection performance in the presence of measurement error. Moreover, the average run lengths of the proposed control charts are exactly the same, indicating that both of them are equivalent control charts. Comparison results show that the existing control chart in Lin et al. is not in-control robust and fails to detect a downward shift in process variance when measurement error is present. Thus, using the error-embedded control chart or the error-corrected control chart to monitor processes with measurement errors is reliable and effective. Moreover, the proposed control charts, where π11 = 1 and π10 = 0, can be applied to monitor processes without measurement errors since their detection performance is equal to that of the existing control chart in Lin et al. Finally, we demonstrate the application of the error-embedded control chart and the error-corrected control chart to analyze the data from the service time system of a bank branch and the data from a semiconductor manufacturing process, showing that the proposed control charts can indeed be applied to data with measurement errors. Full article
(This article belongs to the Special Issue Process Control and Optimization in the Era of Industry 5.0)
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17 pages, 3710 KB  
Article
A New Incipient Faults Diagnosis Method Combining SAE and AdaBoost Algorithm for Vehicle Power Supply with Imbalanced Datasets
by Yinlong Han, Aimin An, Wei Li and Haiying Dong
Processes 2025, 13(10), 3343; https://doi.org/10.3390/pr13103343 - 18 Oct 2025
Viewed by 644
Abstract
For the incipient faults of vehicle power supply under imbalanced datasets, the traditional shallow network has the problems of limited feature extraction ability and the insufficient generalization ability of a single network model. In this paper, an AdaBoost-SAE deep ensemble diagnosis method, which [...] Read more.
For the incipient faults of vehicle power supply under imbalanced datasets, the traditional shallow network has the problems of limited feature extraction ability and the insufficient generalization ability of a single network model. In this paper, an AdaBoost-SAE deep ensemble diagnosis method, which combines the Stacked Auto-Encoder (SAE) deep network and Adaptive Boosting (AdaBoost) algorithm, is proposed. First, SAE is used as a weak classifier to learn and extract incipient fault features from the monitoring date of vehicle power supply. Secondly, in the iterative training process of the model, the classification performance of a single SAE is improved step-by-step by constantly adjusting the weights of the misclassified samples in the training set. Finally, the multiple weak classifiers are combined into strong classifiers by linear weighting to achieve accurate identification of incipient faults under imbalanced datasets. The test results demonstrate that the proposed method can mine deeper features of incipient faults and effectively improve the adverse effects of sample imbalance. Compared with traditional fault diagnosis models and a single SAE, the accuracy of the incipient fault diagnosis can reach 96.6%. Furthermore, the F1-scores of the various working conditions also increased significantly. Full article
(This article belongs to the Special Issue Process Control and Optimization in the Era of Industry 5.0)
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