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Processes
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Dynamics Analysis and Intelligent Control in Industrial Engineering
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Dynamics Analysis and Intelligent Control in Industrial Engineering

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

Deadline for manuscript submissions: 30 July 2024 | Viewed by 3484

Special Issue Editors

Prof. Dr. Liming Dai

E-Mail Website
Guest Editor
Industrial Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada
Interests: nonlinear dynamics; numerical methods and simulations of nonlinear dynamic systems; diagnosis of nonlinear characteristics; response prediction of nonlinear systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jialin Tian

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southwest Petroleum University, Chengdu 610500, China
Interests: mechanical nonlinear dynamics and control; modern design theory and methods of oil and gas equipment; downhole tools and drill bit technology; downhole testing and intelligent control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I trust this email finds you well. We are currently preparing a Special Issue, "Dynamics Analysis and Intelligent Control in Industrial Engineering", in the open access journal Processes. We cordially invite you to contribute one feature paper to this thematic issue based on your expertise, new research, and previous publications.

The development of industrial products and equipment aims at higher speed, controllable vibration, intelligent control, and high accuracy and reliability. This leads to demands for smarter equipment and for products with more complex functions, higher performance, and more elaborated structures. Dynamic analysis and intelligent control therefore become necessary for meeting the demands and play a critical role in developing the equipment and products. Additionally, dynamic analysis and intelligent control are core technologies in the fields of medical treatment, aeronautic and aerospace engineering, transportation, petroleum engineering, operation safety, performance stability, etc. Dynamic analysis and intelligent control are also critical in theoretical and numerical modeling, decision making, accuracy control, and stability analyses. Today, dynamic analysis and intelligent control are widely applied in research and industrial applications of machine vision, deep learning, adaptive control, performance optimization, fuzzy control, and expert systems. Research on dynamic analysis and intelligent control is therefore not only academically sound, but it also has a bright future in industrial applications.

This Special Issue on "Dynamics Analysis and Intelligent Control in Industrial Engineering" reflects and addresses the above challenges and needs, aiming to provide a platform for researchers, scientists, engineers, and others who are active or interested in this area to showcase and exchange their research progress, new discoveries, innovative developments, and applications in the field. Topics of interest in this Special Issue include but are not limited to:

  • Intelligent control of process equipment;
  • Intelligent vibration suppression of manufacturing equipment;
  • Power system simulation, analysis, and control;
  • Dynamic analysis of the operation process of airplanes, high-speed rail, automobiles, and ships;
  • Vibration monitoring and safety control for petrochemical equipment;
  • Fault diagnosis in medical devices;
  • Robot dynamic analysis and intelligent control;
  • Data analysis of other engineering equipment health-monitoring systems utilizing artificial intelligence technology;
  • Modeling and dynamic analysis of intelligent vehicle systems.

We look forward to hearing from you and receiving your contributions.

Prof. Dr. Liming Dai
Prof. Dr. Jialin Tian
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

  • dynamics analysis
  • intelligent control
  • modeling
  • artificial intelligence
  • machine learning
  • fault diagnosis
  • health monitoring
  • industrial applications

Published Papers (4 papers)

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Research

17 pages, 3608 KiB  
Article
Numerical Study of Three-Dimensional Models of Single- and Two-Phase Nanofluid Flow through Corrugated Channels
by Elhadi Kh Abugnah, Wan Saiful-Islam Wan Salim, Abdulhafid M. A. Elfaghi, Sami Al-Alimi, Yazid Saif and Wenbin Zhou
Processes 2024, 12(5), 870; https://doi.org/10.3390/pr12050870 - 26 Apr 2024
Viewed by 251
Abstract
This study delves into computational fluid dynamics (CFDs) predictions for SiO2–water nanofluids, meticulously examining both single-phase and two-phase models. Employing the finite volume approach, we tackled the three-dimensional partial differential equations governing the turbulent mixed convection flow in a horizontally corrugated [...] Read more.
This study delves into computational fluid dynamics (CFDs) predictions for SiO2–water nanofluids, meticulously examining both single-phase and two-phase models. Employing the finite volume approach, we tackled the three-dimensional partial differential equations governing the turbulent mixed convection flow in a horizontally corrugated channel with uniform heat flux. The study encompasses two nanoparticle volume concentrations and five Reynolds numbers (10,000, 15,000, 20,000, 25,000, and 30,000) to unravel these intricate dynamics. Despite previous research on the mixed convection of nanofluids using both single-phase and two-phase models, our work stands out as the inaugural systematic comparison of their predictions for turbulent mixed convection flow through this corrugated channel, considering the influences of temperature-dependent properties and hydrodynamic characteristics. The results reveal distinct variations in thermal fields between the two-phase and single-phase models, with negligible differences in hydrodynamic fields. Notably, the forecasts generated by three two-phase models—Volume of Fluid (VOF), Eulerian Mixture Model (EMM), and Eulerian Eulerian Model (EEM)—demonstrate remarkable similarity in the average Nusselt number, which are 24% higher than the single-phase model (SPM). For low nanoparticle volume fractions, the average Nusselt number predicted by the two-phase models closely aligns with that of the single-phase model. However, as the volume fraction increases, differences emerge, especially at higher Reynolds numbers. In other words, as the volume fraction of the nanoparticles increases, the nanofluid flow becomes a multi-phase problem, as depicted by the findings of this study. Full article
(This article belongs to the Special Issue Dynamics Analysis and Intelligent Control in Industrial Engineering)
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23 pages, 12590 KiB  
Article
Pre-trained 1DCNN-BiLSTM Hybrid Network for Temperature Prediction of Wind Turbine Gearboxes
by Kejia Zhuang, Cong Ma, Heung-Fai Lam, Li Zou and Jun Hu
Processes 2023, 11(12), 3324; https://doi.org/10.3390/pr11123324 - 29 Nov 2023
Cited by 1 | Viewed by 994
Abstract
The safety and stability of a wind turbine is determined by the health condition of its gearbox. The temperature variation, compared with other characteristics of the gearbox, can directly and sensitively reflect its health conditions. However, the existing deep learning models (including the [...] Read more.
The safety and stability of a wind turbine is determined by the health condition of its gearbox. The temperature variation, compared with other characteristics of the gearbox, can directly and sensitively reflect its health conditions. However, the existing deep learning models (including the single model and the hybrid model) have their limitations in dealing with nonlinear and complex temperature data, making it challenging to achieve high-precision prediction results. In order to tackle this issue, this paper introduces a novel two-phase deep learning network for predicting the temperature of wind turbine gearboxes. In the first phase, a one-dimensional convolutional neural network (1DCNN) and a bidirectional long short-term memory (BiLSTM) network are separately trained using the same dataset. The two pre-trained networks are combined and fine-tuned to form the 1DCNN-BiLSTM model for the accurate prediction of gearbox temperatures in the second phase. The proposed model was trained and validated by measured datasets from gearboxes from an existing wind farm. The effectiveness of the model presented was showcased through a comparative analysis with five traditional models, and the result has clearly shown that the proposed model has a great improvement in its prediction accuracy. Full article
(This article belongs to the Special Issue Dynamics Analysis and Intelligent Control in Industrial Engineering)
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24 pages, 15749 KiB  
Article
Optimal Degradation-Aware Control Using Process-Controlled Sparse Bayesian Learning
by Amirhossein Hosseinzadeh Dadash and Niclas Björsell
Processes 2023, 11(11), 3229; https://doi.org/10.3390/pr11113229 - 15 Nov 2023
Cited by 1 | Viewed by 816
Abstract
Efficient production planning hinges on reducing costs and maintaining output quality, with machine degradation management as a key factor. The traditional approaches to control this degradation face two main challenges: high costs associated with physical modeling and a lack of physical interpretability in [...] Read more.
Efficient production planning hinges on reducing costs and maintaining output quality, with machine degradation management as a key factor. The traditional approaches to control this degradation face two main challenges: high costs associated with physical modeling and a lack of physical interpretability in machine learning methods. Addressing these issues, our study presents an innovative solution focused on controlling the degradation, a common cause of machine failure. We propose a method that integrates machine degradation as a virtual state within the system model, utilizing relevance vector machine-based identification designed in a way that offers physical interpretability. This integration maximizes the machine’s operational lifespan. Our approach merges a physical machine model with a physically interpretable data-driven degradation model, effectively tackling the challenges in physical degradation modeling and accessibility to the system disturbance model. By embedding degradation into the system’s state-space model, we simplify implementation and address stability issues. The results demonstrate that our method effectively controls degradation and significantly increases the machine’s mean time to failure. This represents a significant advancement in production planning, offering a cost-effective and interpretable method for managing machine degradation. Full article
(This article belongs to the Special Issue Dynamics Analysis and Intelligent Control in Industrial Engineering)
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33 pages, 24644 KiB  
Article
Remote Monitoring the Parameters of Interest in the 18O Isotope Separation Technological Process
by Adrian Codoban, Helga Silaghi, Sanda Dale and Vlad Muresan
Processes 2023, 11(6), 1594; https://doi.org/10.3390/pr11061594 - 23 May 2023
Viewed by 1029
Abstract
This manuscript presents the remote monitoring of the main parameters in the 18O isotope separation technological process. It proposes to monitor the operation of the five cracking reactors in the isotope production system, respectively, the temperature in the preheating furnaces, the converter [...] Read more.
This manuscript presents the remote monitoring of the main parameters in the 18O isotope separation technological process. It proposes to monitor the operation of the five cracking reactors in the isotope production system, respectively, the temperature in the preheating furnaces, the converter reactors and the cracking reactors. In addition, it performs the monitoring of the two separation columns from the separation cascade structure, respectively, the concentrations of the produced 18O isotope and the input nitric oxides flows. Even if the production process is continuously monitored by teams of operators, the professionals who designed the technical process and those who can monitor it remotely have the possibility to intervene with the view of making the necessary adjustments. Based on the processing of experimental data, which was gathered from the actual plant, the proposed original model of the separation cascade functioning was developed. The process computer from the monitoring system structure runs the proposed mathematical model in parallel with the real plant and estimates several signal values, which are essential to be known by the operators in order to make the appropriate decisions regarding the plant operation. The separation process associated with the final separation column from the separation cascade structure is modeled as a fractional-order process with variable and adjustable differentiation order, which represents another original aspect. Neural networks have been employed in order to implement the proposed mathematical model. The accuracy, validity and efficiency in the operation of the proposed mathematical model is demonstrated through the simulation results presented in the final part of the manuscript. Full article
(This article belongs to the Special Issue Dynamics Analysis and Intelligent Control in Industrial Engineering)
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