Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
2.8
Journals
Processes
Special Issues
Modeling, Simulation and Control of Industrial Processes
share announcement

Modeling, Simulation and Control of 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: 25 June 2025 | Viewed by 991

Special Issue Editor

Dr. Guihua Hu

E-Mail Website
Guest Editor
Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
Interests: industrial process modeling and optimization; CFD and artificial intelligence; uncertainty quantization and robust optimization design

Special Issue Information

Dear Colleagues,

Establishing accurate mathematical models is crucial for controlling and optimizing industrial processes. However, many uncertain factors, such as machining error and operation parameters in the actual process, bring challenges to the optimal design of manufacturing equipment. Traditional numerical methods need much computation time, and surrogate models lack information about the physical field. The combination of surrogate models and computational fluid dynamics (CFDs) is becoming a new trend to improve the quality of training sets and prediction speed. The existence of uncertainty in complex systems has a significant impact on the optimal solution. The degree to which quantitative systems are affected by uncertainty has important application value for optimizing actual industrial processes. Multi-objective evolutionary algorithms have broad application prospects in solving practical optimization problems. However, for the optimization design of industrial processes, after introducing uncertain input variables, how to perform multi-objective robust optimization provides a feasible optimization framework for industrial practice.

This Special Issue aims to provide high-quality work focusing on the latest novel method to solve the optimization and control of industrial processes. The topics of interest for publication include but are not limited to

  • Integration of artificial intelligence and CFDs; 
  • Uncertainty quantification and robust optimization of industrial processes;
  • Modeling, simulation, and control of technological processes; 
  • Reduced order models for complex industrial processes.

Dr. Guihua Hu
Guest Editor

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 system modeling
  • robust optimization design
  • simulation
  • uncertainty quantification
  • reduced-order model
  • artificial intelligence
  • intelligent control

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 3674 KiB  
Article
A Dual-Loop Modified Active Disturbance Rejection Control Scheme for a High-Purity Distillation Column
by Xudong Song, Yuedong Zhao, Zihao Li, Jingchao Song, Zhenlong Wu, Jingzhong Guo and Jian Zhang
Processes 2025, 13(5), 1359; https://doi.org/10.3390/pr13051359 - 29 Apr 2025
Abstract
High-purity distillation columns typically give rise to multi-variable, strongly coupled nonlinear systems with substantial time delay and significant inertia. The control performance of high-purity distillation columns crucially influences the purity of the final product. Taking into account the process of a high-purity distillation [...] Read more.
High-purity distillation columns typically give rise to multi-variable, strongly coupled nonlinear systems with substantial time delay and significant inertia. The control performance of high-purity distillation columns crucially influences the purity of the final product. Taking into account the process of a high-purity distillation column, this article puts forward a dual-loop modified active disturbance rejection control (MADRC) scheme to improve the control of product purity. During the stable operation of the distillation process, the structures of two control loops are, respectively, approximated by two linear transfer function models via open-loop experiments. Subsequently, the compensation part of the MADRC scheme is designed, respectively, for each approximate model. Furthermore, this paper employs singular perturbation theory to prove the stability of MADRC. The performance of the dual-loop MADRC scheme (MADRC) is compared with that of a proportional–integral–derivative (PID) control scheme, a cascade PID control scheme (CPID), and a regular ADRC scheme (ADRC). The simulations demonstrate that the dual-loop MADRC scheme is capable of efficiently tracking the reference value and exhibits optimal disturbance rejection capabilities. Additionally, the superiority of the dual-loop MADRC scheme is validated through Monte Carlo trials. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Industrial Processes)
►▼ Show Figures

Figure 1

25 pages, 6699 KiB  
Article
Optimization of ORC-Based Micro-CHP Systems: An Experimental and Control-Oriented Study
by Márcio Santos, Jorge André, Ricardo Mendes and José B. Ribeiro
Processes 2025, 13(4), 1104; https://doi.org/10.3390/pr13041104 - 7 Apr 2025
Viewed by 539
Abstract
This study presents an experimental and numerical investigation into the performance and control optimization of an Organic Rankine Cycle (ORC)-based micro-combined heat and power (micro-CHP) system. A steady-state, off-design, charge-sensitive model is developed to design a control strategy for an ORC micro-CHP combi-boiler, [...] Read more.
This study presents an experimental and numerical investigation into the performance and control optimization of an Organic Rankine Cycle (ORC)-based micro-combined heat and power (micro-CHP) system. A steady-state, off-design, charge-sensitive model is developed to design a control strategy for an ORC micro-CHP combi-boiler, aiming to efficiently meet real-time domestic hot water demands (up to 40 °C and 35 kW) while generating up to 2 kW of electricity. The system utilizes a natural gas burner to evaporate the working fluid (R245fa), with combustion heat power, volumetric pump speed, and expander speed as control variables. Experimental and numerical evaluations generate steady-state control maps to identify optimal operating regions. A PID-based dynamic control strategy is then developed to stabilize operation during start-ups and user demand variations. The results confirm that the strategy delivers hot water within 1.5 min in simple boiler mode and 3 min in cogeneration mode while improving electricity generation stability and outperforming manual control. The findings demonstrate that integrating steady-state modeling with optimized control enhances the performance, responsiveness, and efficiency of ORC-based micro-CHP systems, making them a viable alternative for residential energy solutions. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Industrial Processes)
►▼ Show Figures

Figure 1

21 pages, 7166 KiB  
Article
Surrogate Modeling of Hydrogen-Enriched Combustion Using Autoencoder-Based Dimensionality Reduction
by Lanfei Zhang, Xu Chu, Siyu Ding, Mingshuo Zhou, Chenxu Ni and Xingjian Wang
Processes 2025, 13(4), 1093; https://doi.org/10.3390/pr13041093 - 5 Apr 2025
Viewed by 241
Abstract
Deep learning-based surrogate models have received wide attention for efficient and cost-effective predictions of fluid flows and combustion, while their hyperparameter settings often lack generalizable guidelines. This study examines two different types of surrogate models, convolutional autoencoder (CAE)-based reduced order models (ROMs) and [...] Read more.
Deep learning-based surrogate models have received wide attention for efficient and cost-effective predictions of fluid flows and combustion, while their hyperparameter settings often lack generalizable guidelines. This study examines two different types of surrogate models, convolutional autoencoder (CAE)-based reduced order models (ROMs) and fully connected autoencoder (FCAE)-based ROMs, for emulating hydrogen-enriched combustion from a triple-coaxial nozzle jet. The performances of these ROMs are discussed in detail, with an emphasis on key hyperparameters, including the number of network layers in the encoder (l), latent vector dimensionality (dim), and convolutional stride (s). The results indicate that a larger l is essential for capturing features in strongly nonlinear flowfields, whereas a smaller l is more effective for less nonlinear distributions, as additional layers may cause overfitting. Specifically, when employing CAE-based ROMs to predict the spatial distribution for H2 (XH2) with weak nonlinearity, the reconstruction absolute average relative deviation (AARD) from the two-layer model was marginally higher than that of three- and four-layer models, whereas the prediction AARD was approximately 5% lower. A smaller dim yields better performance in weakly nonlinear flowfields but may increase local errors in some cases due to excessive feature compression. A CAE-based ROM with a dim = 10 achieved a notably lower AARD of 4.01% for XH2 prediction. A smaller s may enhance the spatial resolution yet raise computational costs. Under identical hyperparameters, the CAE-based ROM outperformed the FCAE-based ROM in both cost-effectiveness and accuracy, achieving a 35 times faster training speed and lower absolute average relative deviation in prediction. These findings provide important guidelines for hyperparameter selection in training autoencoder (AE)-based ROMs for hydrogen-enriched combustion and other similar engineering design problems. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Industrial Processes)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop