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Innovative Approaches to Modeling, Optimization, Control, and Monitoring in Industrial...
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Innovative Approaches to Modeling, Optimization, Control, and Monitoring in Industrial Processes

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

Deadline for manuscript submissions: 20 November 2025 | Viewed by 3130

Special Issue Editors

Prof. Dr. Yuanqiang Zhou

E-Mail Website
Guest Editor
Department of Control Science and Engineering, Tongji University, Shanghai 200092, China
Interests: optimal control; adaptive control; predictive control, learning control, optimization, and their industrial applications
Dr. Le Yao

E-Mail Website
Guest Editor
School of Mathematics, Hangzhou Normal University, Hangzhou 311121, China
Interests: data driven soft sensing; fault detection & diagnosis; multimodal machine learning; industrial AI
Dr. Xiaoyu Jiang

E-Mail Website
Guest Editor
Hangzhou International Innovation Institute, Beihang University, Beijing 100191, China
Interests: artificial intelligence; industrial big data; process monitoring; fault diagnosis; soft sensing; data model security
Dr. Zheren Zhu

E-Mail Website
Guest Editor
School of Mathematics, Hangzhou Normal University, Hangzhou, China
Interests: safety control; fault diagnosis

Special Issue Information

Dear Colleagues,

Innovative modeling, optimization, control, and monitoring methods are essential for modern industrial processes, enhancing efficiency and sustainability in a competitive landscape. Advanced modeling techniques enable a detailed understanding of complex industrial systems, while optimization methods refine and enhance process performance. In particular, cutting-edge control strategies ensure system stability, adaptability, and safety, while real-time monitoring technologies provide actionable insights for improved decision-making and operational reliability and safety. Together, these methods help industries boost productivity, reduce waste, save costs, and comply with strict environmental and quality standards.

Furthermore, the era of Big Data and the rise of machine learning approaches has further transformed modeling and optimization in industrial processes. By analyzing large volumes of operational data, these methods reveal hidden patterns, offering a deeper understanding of system dynamics. Integrating innovative modeling with optimization and control frameworks is crucial for addressing challenges like process uncertainty and nonlinearity. Advanced monitoring techniques, enhanced by digital tools, facilitate predictive maintenance, reduce downtime, and improve safety. However, a significant gap remains between theoretical frameworks and practical applications. Bridging this gap is essential for advancing the field and ensuring that innovative solutions meet the challenges faced by modern industries.

This Special Issue, ‘Innovative Approaches to Modeling, Optimization, Control, and Monitoring in Industrial Processes’, aims to highlight original research contributions focused on practical applications. Topics include the following:

  1. The development of novel modeling techniques for complex industrial processes, including chemical, energy, and manufacturing systems.
  2. Advanced optimization methods for process improvement, scheduling, and resource allocation.
  3. State-of-the-art control strategies for nonlinear, high-dimensional, or uncertain systems.
  4. Safety control theories and applications for industrial processes.
  5. Innovative monitoring technologies for real-time analysis, fault detection, and predictive maintenance.
  6. Security and robustness of data-driven models in process monitoring systems.
  7. The integration of modeling, optimization, and control for sustainable energy-efficient processes.
  8. Case studies showcasing the applications of innovative methodologies to real-world industrial challenges.

Prof. Dr. Yuanqiang Zhou
Dr. Le Yao
Dr. Xiaoyu Jiang
Dr. Zheren Zhu
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

  • process modeling
  • process optimization
  • process control
  • process monitoring
  • process system engineering
  • machine learning
  • big data

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Published Papers (6 papers)

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Research

23 pages, 19257 KB  
Article
A Dual-Norm Support Vector Machine: Integrating L1 and L∞ Slack Penalties for Robust and Sparse Classification
by Xiaoyong Liu, Qingyao Liu, Shunqiang Liu, Genglong Yan, Fabin Zhang, Chengbin Zeng and Xiaoliu Yang
Processes 2025, 13(9), 2858; https://doi.org/10.3390/pr13092858 - 6 Sep 2025
Viewed by 326
Abstract
This paper presents a novel support vector machine (SVM) classification approach that simultaneously accounts for both overall and extreme misclassification errors via a dual-norm regularization strategy. Traditional SVMs minimize the L1-norm of slack variables to control global misclassification, while least squares [...] Read more.
This paper presents a novel support vector machine (SVM) classification approach that simultaneously accounts for both overall and extreme misclassification errors via a dual-norm regularization strategy. Traditional SVMs minimize the L1-norm of slack variables to control global misclassification, while least squares SVM (LSSVM) minimizes the sum of squared errors. In contrast, our method preserves the classical L1-norm penalty to maintain overall classification fidelity and incorporates an additional L∞-norm term to penalize the largest slack variable, thereby constraining the worst-case margin violation. This composite objective yields a more robust and generalizable classifier, particularly effective when occasional large deviations disproportionately affect decision boundaries. The resulting optimization problem minimizes a regularized objective combining the model norm, the sum of slack variables, and the maximum slack variable, with two hyperparameters, C1 and C2, balancing global error against extremal robustness. By formulating the problem under convex constraints, the optimization remains tractable and guarantees a globally optimal solution. Experimental evaluations on benchmark datasets demonstrate that the proposed method achieves comparable or superior classification accuracy while reducing the impact of outliers and maintaining a sparse model structure. These results underscore the advantage of jointly enforcing L1 and L∞ penalties, providing an effective mechanism to balance average performance with worst-case error sensitivity in support vector classification. Full article
(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control, and Monitoring in Industrial Processes)
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19 pages, 1180 KB  
Article
A Novel Terminal Sliding Mode Control with Robust Prescribed-Time Stability
by Chaimae El Mortajine, Mostafa Bouzi and Abdellah Benaddy
Processes 2025, 13(9), 2728; https://doi.org/10.3390/pr13092728 - 26 Aug 2025
Viewed by 384
Abstract
The present paper investigates a new tool for analyzing stability/convergence properties and robustness against matched perturbations of a class of nonlinear systems. We start with a scalar system, where it is shown that the state can be regulated or stabilized to a prescribed [...] Read more.
The present paper investigates a new tool for analyzing stability/convergence properties and robustness against matched perturbations of a class of nonlinear systems. We start with a scalar system, where it is shown that the state can be regulated or stabilized to a prescribed time using time-varying functions. The proof is based on Lyapunov theory. We developed a robust terminal-integral sliding mode controller that guarantees convergence of the system states to a desired equilibrium within a user-defined time, irrespective of initial conditions and under bounded disturbances. The method was extended to a class of second-order nonlinear systems, achieving both fixed-time (prescribed-time) convergence and robustness. Theoretical properties were established via Lyapunov-based analysis, and numerical simulations confirmed the effectiveness of the proposed methods in terms of robustness and convergence. Full article
(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control, and Monitoring in Industrial Processes)
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15 pages, 3001 KB  
Article
Analytical Prediction of Fatigue Life for Roller Bearings Considering Impact Loading
by Yuwei Liu, Haosen Gong, Yufei Li, Zehai Gao and Tong Zhao
Processes 2025, 13(8), 2545; https://doi.org/10.3390/pr13082545 - 12 Aug 2025
Viewed by 335
Abstract
During the actual operating conditions, it is inevitable that rolling bearings will be subjected to impact loading. However, due to the very short duration of impact loading, previous studies have almost ignored the influence of impact loading on fatigue life of roller bearings. [...] Read more.
During the actual operating conditions, it is inevitable that rolling bearings will be subjected to impact loading. However, due to the very short duration of impact loading, previous studies have almost ignored the influence of impact loading on fatigue life of roller bearings. This paper attempts to construct a numerical framework to address the above issues, thereby providing a theoretical basis for predicting fatigue life of roller bearings under frequent impact loading. A quasi-dynamic model of roller bearings is established to capture the instantaneous fluctuation in roller–raceway contact loads due to impact loading. Then, the influence of impact loading on the fatigue life of roller bearings is accurately characterized based on Miner’s rule. The results show that the frequent impact loading causes a significant decrease in the fatigue life of roller bearings, and the extent of fatigue life decrease depends on the bearing speeds and load conditions. To accurately predict the fatigue life of roller bearings under actual operating conditions, it is necessary to account for the influence of the impact loading, especially for high speeds and light load conditions. Full article
(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control, and Monitoring in Industrial Processes)
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26 pages, 4865 KB  
Article
Field and Numerical Analysis of Downhole Mechanical Inflow Control Devices (ICD and AICD) for Mature Heavy Oil Fields
by Miguel Asuaje, Camilo Díaz, Nicolás Ratkovich, Andrés Pinilla and Ricardo Nieto
Processes 2025, 13(8), 2538; https://doi.org/10.3390/pr13082538 - 12 Aug 2025
Viewed by 461
Abstract
The challenge of excess water production in mature heavy oil reservoirs presents significant environmental and economic concerns. This study evaluates the effectiveness of inflow control devices (ICDs) and autonomous inflow control devices (AICDs) for managing water production in heavy oil reservoirs with strong [...] Read more.
The challenge of excess water production in mature heavy oil reservoirs presents significant environmental and economic concerns. This study evaluates the effectiveness of inflow control devices (ICDs) and autonomous inflow control devices (AICDs) for managing water production in heavy oil reservoirs with strong aquifer drives. Our investigation comprises two field implementations and a computational fluid dynamics (CFD) study. In the first field implementation, both ICDs and AICDs achieved substantial water reduction (25% and 32%, respectively) compared to conventional slotted liner completions, with ICDs demonstrating superior oil production performance, extending well life by approximately 30% and doubling accumulated oil. The second field implementation featured rate-controlled production (RCP) devices, showing that two AICD wells together produced 60% more accumulated oil and 40% less water than a single conventional well, effectively relieving surface facility bottlenecks. Full 3D Navier–Stokes simulations for a third field implementation revealed that passive ICDs outperformed AICDs under specific draw-down and spacing conditions, challenging the industry preference for newer technologies. The study’s findings, which include quantifiable reductions in the carbon footprint associated with decreased power consumption, provide valuable insights for operators seeking to optimize water management while minimizing environmental impact, advancing the sustainable oil production practices aligned with UN Sustainable Development Goals 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure), and 13 (Climate Action). Full article
(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control, and Monitoring in Industrial Processes)
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25 pages, 5652 KB  
Article
Modeling and Optimization of the Vacuum Degassing Process in Electric Steelmaking Route
by Bikram Konar, Noah Quintana and Mukesh Sharma
Processes 2025, 13(8), 2368; https://doi.org/10.3390/pr13082368 - 25 Jul 2025
Viewed by 634
Abstract
Vacuum degassing (VD) is a critical refining step in electric arc furnace (EAF) steelmaking for producing clean steel with reduced nitrogen and hydrogen content. This study develops an Effective Equilibrium Reaction Zone (EERZ) model focused on denitrogenation (de-N) by simulating interfacial reactions at [...] Read more.
Vacuum degassing (VD) is a critical refining step in electric arc furnace (EAF) steelmaking for producing clean steel with reduced nitrogen and hydrogen content. This study develops an Effective Equilibrium Reaction Zone (EERZ) model focused on denitrogenation (de-N) by simulating interfacial reactions at the bubble–steel interface (Z1). The model incorporates key process parameters such as argon flow rate, vacuum pressure, and initial nitrogen and sulfur concentrations. A robust empirical correlation was established between de-N efficiency and the mass of Z1, reducing prediction time from a day to under a minute. Additionally, the model was further improved by incorporating a dynamic surface exposure zone (Z_eye) to account for transient ladle eye effects on nitrogen removal under deep vacuum (<10 torr), validated using synchronized plant trials and Python-based video analysis. The integrated approach—combining thermodynamic-kinetic modeling, plant validation, and image-based diagnostics—provides a robust framework for optimizing VD control and enhancing nitrogen removal control in EAF-based steelmaking. Full article
(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control, and Monitoring in Industrial Processes)
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20 pages, 1507 KB  
Article
Extended State Observer Based Robust Nonlinear PID Attitude Tracking Control of Quadrotor with Lumped Disturbance
by Gang Xu, Shengping Luo, Yiqing Huang and Xiongfeng Deng
Processes 2025, 13(5), 1470; https://doi.org/10.3390/pr13051470 - 12 May 2025
Cited by 1 | Viewed by 613
Abstract
The paper presents a robust nonlinear PID controller for the attitude tracking problem of quadrotors subject to disturbance. First, to suppress the influence caused by external disturbance torque, considering the fact that the angular velocity can be obtained by the inertial measurement unit [...] Read more.
The paper presents a robust nonlinear PID controller for the attitude tracking problem of quadrotors subject to disturbance. First, to suppress the influence caused by external disturbance torque, considering the fact that the angular velocity can be obtained by the inertial measurement unit (IMU), a reduced-order extended state observer (ESO) is applied as a feedforward compensation to improve the robustness of the tracking system. Then, an ESO-based nonlinear PID controller is constructed to track the desired attitude command, and the rigorous proof of the convergence of the closed-loop system is derived by utilizing the Lyapunov method. Finally, the effectiveness of the proposed method is illustrated by numerical simulations and platform experiments. Full article
(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control, and Monitoring in Industrial Processes)
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