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Processes
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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: Second Edition

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 2530

Special Issue Editors

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
Special Issues, Collections and Topics in MDPI journals
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
Special Issues, Collections and Topics in MDPI journals
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
Special Issues, Collections and Topics in MDPI journals
Dr. Zheren Zhu

E-Mail Website
Guest Editor
School of Mathematics, Hangzhou Normal University, Hangzhou, China
Interests: safety control; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

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 provide 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, and 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 and costs, and comply with strict environmental and quality standards.

Furthermore, the era of Big Data and the rise in 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 such as 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 address the challenges faced by modern industries.

This Special Issue “Innovative Approaches to Modeling, Optimization, Control and Monitoring in Industrial Processes: Second Edition” aims to highlight original research in this field, with a focus on practical applications. Topics of interest include the following:

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

In addition, this Special Issue has established a collaboration with “The 3rd International Conference on Advanced Robotics, Control, and Artificial Intelligence (ARCAI 2026) (https://www.icarcai.org/)” and will also feature papers contributed from the conference.

Dr. Xiaoyu Jiang
Prof. Dr. Yuanqiang Zhou
Dr. Le Yao
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 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 modeling
  • process optimization
  • process control
  • process monitoring
  • process system engineering
  • machine learning
  • big data

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Published Papers (4 papers)

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Research

18 pages, 1003 KB  
Article
Comprehensive Evaluation of Optimization Algorithms and Performance Criteria for ANN-Based PEMFC Voltage Prediction
by Hafsa Abbade, Abdessamad Intidam, Hassan El Fadil, Abdellah Lassioui, Ahmed Hamed, Anwar Hasni, Marouane El Ancary and Mohamed Mouyane
Processes 2026, 14(5), 844; https://doi.org/10.3390/pr14050844 - 5 Mar 2026
Viewed by 494
Abstract
Proton exchange membrane fuel cells (PEMFCs) are considered to be a promising solution for clean energy conversion in hydrogen electric vehicles. Accurate voltage prediction is crucial for designing efficient energy management and control strategies. While deep neural networks have shown good potential in [...] Read more.
Proton exchange membrane fuel cells (PEMFCs) are considered to be a promising solution for clean energy conversion in hydrogen electric vehicles. Accurate voltage prediction is crucial for designing efficient energy management and control strategies. While deep neural networks have shown good potential in modeling PEMFCs, the role of optimization algorithms and training performance criteria in achieving accurate voltage predictions remains unclear. This research aims to carry out a comprehensive comparative study using three popular optimization algorithms and different performance criteria including prediction accuracy, convergence speed, and training stability. A real experimental dataset for a Nexa PEMFC system has been used to train and evaluate different models of artificial neural networks (ANNs) to find out which optimization algorithm and performance criteria are best for efficient modeling of PEMFCs under varying operating conditions. The results of this study are analyzed through a comparative evaluation of different metaheuristic optimization algorithms applied within a unified ANN training framework for PEMFC voltage prediction. Particle swarm optimization (PSO) provides the highest voltage prediction accuracy and robust convergence behavior, whereas Grey Wolf Optimization (GWO) achieves the fastest convergence with reduced computational time. Full article
(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control and Monitoring in Industrial Processes: Second Edition)
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17 pages, 2664 KB  
Article
Accurate Hourly Forecasting of Wind Energy in Romania Using Deep Learning Models
by Grigore Cican, Adrian-Nicolae Buturache and Florin Popescu
Processes 2026, 14(3), 574; https://doi.org/10.3390/pr14030574 - 6 Feb 2026
Viewed by 543
Abstract
Wind energy plays a critical role in the European Union’s decarbonization strategy, including Romania’s growing renewable energy capacity. This study proposes a deep learning-based method for forecasting hourly wind energy production in Romania using feedforward neural networks (FFNNs) and recurrent neural networks (RNNs), [...] Read more.
Wind energy plays a critical role in the European Union’s decarbonization strategy, including Romania’s growing renewable energy capacity. This study proposes a deep learning-based method for forecasting hourly wind energy production in Romania using feedforward neural networks (FFNNs) and recurrent neural networks (RNNs), trained on a dataset spanning from 1 January to 31 December 2023. The dataset includes hourly wind energy output data (mean = 850.6 MW, std = 694.0 MW) and 13 meteorological variables (e.g., average wind speed = 4.7 km/h, temperature = 14.4 °C). A total of 1296 models were trained and evaluated, with the best-performing RNN model achieving a coefficient of determination of R2 = 0.9680 and a mean absolute error (MAE) of 81.03 MW. The top three models all exceeded R2 = 0.966, demonstrating strong generalization on unseen data. The models were also validated using two external time intervals outside the training/testing sets, confirming robustness. These results show that deep learning models can provide highly accurate, data-driven predictions of wind energy output, supporting grid stability and informed decision-making amid renewable energy variability. Full article
(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control and Monitoring in Industrial Processes: Second Edition)
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16 pages, 2800 KB  
Article
Study on Wellhead Pressure Control in the Cementing and Setting Stages Based on Pressure Transfer Efficiency
by Xiaoshan Wang, Qiang Cui, Zehao Zheng and Bin Yuan
Processes 2026, 14(3), 538; https://doi.org/10.3390/pr14030538 - 4 Feb 2026
Viewed by 368
Abstract
This study addresses the challenge of annular gas migration control during the waiting-on-cement (WOC) period in managed pressure cementing for formations with narrow safe pressure windows. A dynamic pressure compensation optimization strategy is proposed by integrating a composite mechanistic model with experimental validation. [...] Read more.
This study addresses the challenge of annular gas migration control during the waiting-on-cement (WOC) period in managed pressure cementing for formations with narrow safe pressure windows. A dynamic pressure compensation optimization strategy is proposed by integrating a composite mechanistic model with experimental validation. Based on the hydration degree (T) model, a predictive model for static gel strength development was established. By coupling the gelation-induced suspension effect with cement slurry volumetric shrinkage, a static hydrostatic pressure decline model was developed. Experimental results indicate that the prediction errors of the proposed models are all within 7%, demonstrating improved accuracy compared with traditional empirical approaches and classical shear stress models. In addition, a testing methodology was developed to characterize pressure transmission efficiency during the WOC process, revealing its dynamic attenuation behavior. Experimental results show that when the static gel strength of anti-gas-migration cement slurry reaches 240 Pa, the pressure transmission efficiency ranges from 45% to 49%. Based on these findings, a wellhead backpressure calculation model incorporating the evolution of pressure transmission efficiency was established, providing a quantitative basis for annular pressure management during cement setting. Full article
(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control and Monitoring in Industrial Processes: Second Edition)
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23 pages, 701 KB  
Article
Improving Energy Efficiency and Reliability of Parallel Pump Systems Using Hybrid PSO–ADMM–LQR
by Samir Nassiri, Ahmed Abbou and Mohamed Cherkaoui
Processes 2026, 14(2), 186; https://doi.org/10.3390/pr14020186 - 6 Jan 2026
Viewed by 716
Abstract
This paper proposes a hybrid optimization–control framework that combines the Particle Swarm Optimization (PSO) algorithm, the Alternating Direction Method of Multipliers (ADMM), and a Linear–Quadratic Regulator (LQR) for energy-efficient and reliable operation of parallel pump systems. The PSO layer performs global exploration over [...] Read more.
This paper proposes a hybrid optimization–control framework that combines the Particle Swarm Optimization (PSO) algorithm, the Alternating Direction Method of Multipliers (ADMM), and a Linear–Quadratic Regulator (LQR) for energy-efficient and reliable operation of parallel pump systems. The PSO layer performs global exploration over mixed discrete–continuous design variables, while the ADMM layer coordinates distributed flows under head and reliability constraints, yielding hydraulically feasible operating points. The inner LQR controller achieves optimal speed tracking with guaranteed asymptotic stability and improved robustness against nonlinear load disturbances. The overall PSO–ADMM–LQR co-design minimizes a composite objective that accounts for steady-state efficiency, transient performance, and control effort. Simulation results on benchmark multi-pump systems demonstrate that the proposed framework outperforms conventional PSO- and PID-based methods in terms of energy savings, dynamic response, and robustness. The method exhibits low computational complexity, scalability to large systems, and practical suitability for real-time implementation in smart water distribution and industrial pumping applications. Full article
(This article belongs to the Special Issue Innovative Approaches to Modeling, Optimization, Control and Monitoring in Industrial Processes: Second Edition)
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